From 6af5b759f8c8cda3761c0e6b39fe01d3882ce00a Mon Sep 17 00:00:00 2001 From: arnavsinghvi11 Date: Wed, 3 Sep 2025 23:03:43 -0700 Subject: [PATCH 01/12] support for native reasoning in CoT for reasoning models --- dspy/adapters/base.py | 5 ++ dspy/clients/base_lm.py | 29 ++++++++ dspy/clients/lm.py | 13 ++++ dspy/predict/chain_of_thought.py | 17 +++-- tests/clients/test_lm.py | 113 +++++++++++++++++++++++++++++++ 5 files changed, 171 insertions(+), 6 deletions(-) diff --git a/dspy/adapters/base.py b/dspy/adapters/base.py index eaea79563c..4c65d917a4 100644 --- a/dspy/adapters/base.py +++ b/dspy/adapters/base.py @@ -78,12 +78,14 @@ def _call_postprocess( for output in outputs: output_logprobs = None tool_calls = None + reasoning = None text = output if isinstance(output, dict): text = output["text"] output_logprobs = output.get("logprobs") tool_calls = output.get("tool_calls") + reasoning = output.get("reasoning") if text: value = self.parse(processed_signature, text) @@ -109,6 +111,9 @@ def _call_postprocess( if output_logprobs: value["logprobs"] = output_logprobs + if reasoning: + value["reasoning"] = reasoning + values.append(value) return values diff --git a/dspy/clients/base_lm.py b/dspy/clients/base_lm.py index 6f86da5632..fb0a338a2a 100644 --- a/dspy/clients/base_lm.py +++ b/dspy/clients/base_lm.py @@ -179,6 +179,10 @@ def _process_completion(self, response, merged_kwargs): for c in response.choices: output = {} output["text"] = c.message.content if hasattr(c, "message") else c["text"] + + if hasattr(c, "message") and hasattr(c.message, "reasoning_content") and c.message.reasoning_content: + output["reasoning"] = c.message.reasoning_content + if merged_kwargs.get("logprobs"): output["logprobs"] = c.logprobs if hasattr(c, "logprobs") else c["logprobs"] if hasattr(c, "message") and getattr(c.message, "tool_calls", None): @@ -203,12 +207,37 @@ def _process_response(self, response): """ outputs = [] tool_calls = [] + reasoning_content = None + for output_item in response.output: if output_item.type == "message": for content_item in output_item.content: outputs.append(content_item.text) elif output_item.type == "function_call": tool_calls.append(output_item.model_dump()) + elif output_item.type == "reasoning": + if hasattr(output_item, 'content') and output_item.content: + reasoning_content = output_item.content + elif hasattr(output_item, 'summary') and output_item.summary: + if isinstance(output_item.summary, list): + summary_texts = [] + for summary_item in output_item.summary: + if hasattr(summary_item, 'text'): + summary_texts.append(summary_item.text) + reasoning_content = "\n\n".join(summary_texts) if summary_texts else output_item.summary + else: + reasoning_content = output_item.summary + + if len(outputs) == 1 and isinstance(outputs[0], str): + result = {"text": outputs[0]} + if reasoning_content: + result["reasoning"] = reasoning_content + outputs = [result] + elif reasoning_content: + if outputs and isinstance(outputs[0], str): + outputs[0] = {"text": outputs[0], "reasoning": reasoning_content} + elif outputs and isinstance(outputs[0], dict): + outputs[0]["reasoning"] = reasoning_content if tool_calls: outputs.append({"tool_calls": tool_calls}) diff --git a/dspy/clients/lm.py b/dspy/clients/lm.py index 3c133a7c03..420652bf83 100644 --- a/dspy/clients/lm.py +++ b/dspy/clients/lm.py @@ -103,6 +103,19 @@ def __init__( self._warn_zero_temp_rollout(self.kwargs.get("temperature"), self.kwargs.get("rollout_id")) + # Set flag if model supports native reasoning AND user specified any reasoning parameter + reasoning_params = ['reasoning_effort', 'reasoning', 'thinking'] # Common reasoning parameter names + has_reasoning_param = any(param in self.kwargs for param in reasoning_params) + if litellm.supports_reasoning(self.model) and has_reasoning_param: + settings.use_native_reasoning = True + + # Normalize reasoning_effort to get reasoning summaries (for OpenAI reasoning models which don't expose reasoning content) + if ('reasoning_effort' in self.kwargs and + (self.model_type == "responses" or + ('openai/' in self.model.lower() and litellm.supports_reasoning(self.model)))): + effort = self.kwargs.pop('reasoning_effort') + self.kwargs['reasoning'] = {'effort': effort, 'summary': 'auto'} + def _warn_zero_temp_rollout(self, temperature: float | None, rollout_id): if ( not self._warned_zero_temp_rollout diff --git a/dspy/predict/chain_of_thought.py b/dspy/predict/chain_of_thought.py index 96afef8588..cddf034c9a 100644 --- a/dspy/predict/chain_of_thought.py +++ b/dspy/predict/chain_of_thought.py @@ -5,6 +5,7 @@ import dspy from dspy.primitives.module import Module from dspy.signatures.signature import Signature, ensure_signature +from dspy.dsp.utils.settings import settings class ChainOfThought(Module): @@ -26,12 +27,16 @@ def __init__( """ super().__init__() signature = ensure_signature(signature) - prefix = "Reasoning: Let's think step by step in order to" - desc = "${reasoning}" - rationale_field_type = rationale_field.annotation if rationale_field else rationale_field_type - rationale_field = rationale_field if rationale_field else dspy.OutputField(prefix=prefix, desc=desc) - extended_signature = signature.prepend(name="reasoning", field=rationale_field, type_=rationale_field_type) - self.predict = dspy.Predict(extended_signature, **config) + + if getattr(settings, 'use_native_reasoning', False): + self.predict = dspy.Predict(signature, **config) + else: + prefix = "Reasoning: Let's think step by step in order to" + desc = "${reasoning}" + rationale_field_type = rationale_field.annotation if rationale_field else rationale_field_type + rationale_field = rationale_field if rationale_field else dspy.OutputField(prefix=prefix, desc=desc) + extended_signature = signature.prepend(name="reasoning", field=rationale_field, type_=rationale_field_type) + self.predict = dspy.Predict(extended_signature, **config) def forward(self, **kwargs): return self.predict(**kwargs) diff --git a/tests/clients/test_lm.py b/tests/clients/test_lm.py index 54068e512d..92f9decdf5 100644 --- a/tests/clients/test_lm.py +++ b/tests/clients/test_lm.py @@ -13,6 +13,7 @@ import dspy from dspy.utils.usage_tracker import track_usage +from dspy.dsp.utils.settings import settings def make_response(output_blocks): @@ -551,3 +552,115 @@ def test_responses_api_tool_calls(litellm_test_server): dspy_responses.assert_called_once() assert dspy_responses.call_args.kwargs["model"] == "openai/dspy-test-model" + + +def test_reasoning_effort_normalization(): + """Test that reasoning_effort gets normalized to reasoning format for OpenAI models.""" + with mock.patch("litellm.supports_reasoning", return_value=True): + # OpenAI model with Responses API - should normalize + lm1 = dspy.LM( + model="openai/gpt-5", + model_type="responses", + reasoning_effort="low", + max_tokens=16000, + temperature=1.0 + ) + assert "reasoning_effort" not in lm1.kwargs + assert lm1.kwargs["reasoning"] == {"effort": "low", "summary": "auto"} + + # OpenAI model with Chat API - should normalize + lm2 = dspy.LM( + model="openai/gpt-5", + reasoning_effort="medium", + max_tokens=16000, + temperature=1.0 + ) + assert "reasoning_effort" not in lm2.kwargs + assert lm2.kwargs["reasoning"] == {"effort": "medium", "summary": "auto"} + + # Non-OpenAI model - should NOT normalize + lm3 = dspy.LM( + model="deepseek-ai/DeepSeek-R1", + reasoning_effort="low", + max_tokens=4000, + temperature=0.7 + ) + assert "reasoning_effort" in lm3.kwargs + assert "reasoning" not in lm3.kwargs + + +@mock.patch("litellm.supports_reasoning") +@mock.patch("dspy.dsp.utils.settings") +def test_native_reasoning_flag_setting(mock_settings, mock_supports): + """Test that use_native_reasoning flag is set correctly.""" + mock_supports.return_value = True + + # Should set flag when model supports reasoning and has reasoning param + dspy.LM(model="openai/gpt-5", reasoning_effort="low", max_tokens=16000, temperature=1.0) + mock_settings.use_native_reasoning = True + + mock_supports.return_value = False + + # Should NOT set flag when model doesn't support reasoning + dspy.LM(model="openai/gpt-4", reasoning_effort="low", max_tokens=1000, temperature=0.7) + + +def test_reasoning_content_extraction(): + """Test that reasoning models can be created with proper configuration.""" + # Test that reasoning models are properly configured + lm = dspy.LM( + model="openai/gpt-5", + model_type="responses", + max_tokens=16000, + temperature=1.0, + reasoning_effort="low" + ) + + # Verify reasoning parameters are normalized + assert "reasoning" in lm.kwargs + assert lm.kwargs["reasoning"]["effort"] == "low" + assert "max_completion_tokens" in lm.kwargs + assert lm.kwargs["max_completion_tokens"] == 16000 + + +def test_chain_of_thought_with_native_reasoning(): + """Test ChainOfThought with native reasoning vs manual reasoning.""" + + class SimpleSignature(dspy.Signature): + """Answer the question.""" + question: str = dspy.InputField() + answer: str = dspy.OutputField() + + # Test with native reasoning enabled + settings.use_native_reasoning = True + with mock.patch("dspy.Predict") as mock_predict: + mock_predict_instance = mock.MagicMock() + mock_predict_instance.return_value = dspy.Prediction(answer="42", reasoning="native reasoning") + mock_predict.return_value = mock_predict_instance + + cot = dspy.ChainOfThought(SimpleSignature) + result = cot(question="What is the answer?") + + # Should use Predict with original signature (no reasoning field added) + mock_predict.assert_called_once() + call_args = mock_predict.call_args[0] + assert call_args[0] == SimpleSignature + assert hasattr(result, 'reasoning') + + # Reset and test with native reasoning disabled (traditional ChainOfThought) + settings.use_native_reasoning = False + with mock.patch("dspy.Predict") as mock_predict: + mock_predict_instance = mock.MagicMock() + mock_predict_instance.return_value = dspy.Prediction(reasoning="step by step...", answer="42") + mock_predict.return_value = mock_predict_instance + + cot = dspy.ChainOfThought(SimpleSignature) + result = cot(question="What is the answer?") + + # Should use Predict with extended signature (reasoning field added) + mock_predict.assert_called_once() + call_args = mock_predict.call_args[0] + # Check that signature was extended with reasoning field + extended_signature = call_args[0] + assert 'reasoning' in extended_signature.fields + assert hasattr(result, 'reasoning') From c699a1fc874434aa4c88a671b13c3fd94063538d Mon Sep 17 00:00:00 2001 From: arnavsinghvi11 Date: Wed, 3 Sep 2025 23:17:36 -0700 Subject: [PATCH 02/12] ruff and test --- dspy/clients/base_lm.py | 23 ++++++++--------- dspy/clients/lm.py | 12 ++++----- dspy/predict/chain_of_thought.py | 6 ++--- tests/clients/test_lm.py | 44 ++++++++++++++++---------------- 4 files changed, 41 insertions(+), 44 deletions(-) diff --git a/dspy/clients/base_lm.py b/dspy/clients/base_lm.py index fb0a338a2a..97a2f71e2c 100644 --- a/dspy/clients/base_lm.py +++ b/dspy/clients/base_lm.py @@ -179,10 +179,10 @@ def _process_completion(self, response, merged_kwargs): for c in response.choices: output = {} output["text"] = c.message.content if hasattr(c, "message") else c["text"] - + if hasattr(c, "message") and hasattr(c.message, "reasoning_content") and c.message.reasoning_content: output["reasoning"] = c.message.reasoning_content - + if merged_kwargs.get("logprobs"): output["logprobs"] = c.logprobs if hasattr(c, "logprobs") else c["logprobs"] if hasattr(c, "message") and getattr(c.message, "tool_calls", None): @@ -208,7 +208,7 @@ def _process_response(self, response): outputs = [] tool_calls = [] reasoning_content = None - + for output_item in response.output: if output_item.type == "message": for content_item in output_item.content: @@ -216,25 +216,22 @@ def _process_response(self, response): elif output_item.type == "function_call": tool_calls.append(output_item.model_dump()) elif output_item.type == "reasoning": - if hasattr(output_item, 'content') and output_item.content: + if hasattr(output_item, "content") and output_item.content: reasoning_content = output_item.content - elif hasattr(output_item, 'summary') and output_item.summary: + elif hasattr(output_item, "summary") and output_item.summary: if isinstance(output_item.summary, list): summary_texts = [] for summary_item in output_item.summary: - if hasattr(summary_item, 'text'): + if hasattr(summary_item, "text"): summary_texts.append(summary_item.text) reasoning_content = "\n\n".join(summary_texts) if summary_texts else output_item.summary else: reasoning_content = output_item.summary - if len(outputs) == 1 and isinstance(outputs[0], str): - result = {"text": outputs[0]} - if reasoning_content: - result["reasoning"] = reasoning_content - outputs = [result] - elif reasoning_content: - if outputs and isinstance(outputs[0], str): + if reasoning_content: + if len(outputs) == 1 and isinstance(outputs[0], str): + outputs = [{"text": outputs[0], "reasoning": reasoning_content}] + elif outputs and isinstance(outputs[0], str): outputs[0] = {"text": outputs[0], "reasoning": reasoning_content} elif outputs and isinstance(outputs[0], dict): outputs[0]["reasoning"] = reasoning_content diff --git a/dspy/clients/lm.py b/dspy/clients/lm.py index 420652bf83..3ab39eb6a0 100644 --- a/dspy/clients/lm.py +++ b/dspy/clients/lm.py @@ -104,17 +104,17 @@ def __init__( self._warn_zero_temp_rollout(self.kwargs.get("temperature"), self.kwargs.get("rollout_id")) # Set flag if model supports native reasoning AND user specified any reasoning parameter - reasoning_params = ['reasoning_effort', 'reasoning', 'thinking'] # Common reasoning parameter names + reasoning_params = ["reasoning_effort", "reasoning", "thinking"] # Common reasoning parameter names has_reasoning_param = any(param in self.kwargs for param in reasoning_params) if litellm.supports_reasoning(self.model) and has_reasoning_param: settings.use_native_reasoning = True # Normalize reasoning_effort to get reasoning summaries (for OpenAI reasoning models which don't expose reasoning content) - if ('reasoning_effort' in self.kwargs and - (self.model_type == "responses" or - ('openai/' in self.model.lower() and litellm.supports_reasoning(self.model)))): - effort = self.kwargs.pop('reasoning_effort') - self.kwargs['reasoning'] = {'effort': effort, 'summary': 'auto'} + if ("reasoning_effort" in self.kwargs and + (self.model_type == "responses" or + ("openai/" in self.model.lower() and litellm.supports_reasoning(self.model)))): + effort = self.kwargs.pop("reasoning_effort") + self.kwargs["reasoning"] = {"effort": effort, "summary": "auto"} def _warn_zero_temp_rollout(self, temperature: float | None, rollout_id): if ( diff --git a/dspy/predict/chain_of_thought.py b/dspy/predict/chain_of_thought.py index cddf034c9a..2d9681407f 100644 --- a/dspy/predict/chain_of_thought.py +++ b/dspy/predict/chain_of_thought.py @@ -3,9 +3,9 @@ from pydantic.fields import FieldInfo import dspy +from dspy.dsp.utils.settings import settings from dspy.primitives.module import Module from dspy.signatures.signature import Signature, ensure_signature -from dspy.dsp.utils.settings import settings class ChainOfThought(Module): @@ -27,8 +27,8 @@ def __init__( """ super().__init__() signature = ensure_signature(signature) - - if getattr(settings, 'use_native_reasoning', False): + + if getattr(settings, "use_native_reasoning", False): self.predict = dspy.Predict(signature, **config) else: prefix = "Reasoning: Let's think step by step in order to" diff --git a/tests/clients/test_lm.py b/tests/clients/test_lm.py index 92f9decdf5..8756c922f1 100644 --- a/tests/clients/test_lm.py +++ b/tests/clients/test_lm.py @@ -12,8 +12,8 @@ from openai import RateLimitError import dspy -from dspy.utils.usage_tracker import track_usage from dspy.dsp.utils.settings import settings +from dspy.utils.usage_tracker import track_usage def make_response(output_blocks): @@ -559,7 +559,7 @@ def test_reasoning_effort_normalization(): with mock.patch("litellm.supports_reasoning", return_value=True): # OpenAI model with Responses API - should normalize lm1 = dspy.LM( - model="openai/gpt-5", + model="openai/gpt-5", model_type="responses", reasoning_effort="low", max_tokens=16000, @@ -568,10 +568,10 @@ def test_reasoning_effort_normalization(): assert "reasoning_effort" not in lm1.kwargs assert lm1.kwargs["reasoning"] == {"effort": "low", "summary": "auto"} - # OpenAI model with Chat API - should normalize + # OpenAI model with Chat API - should normalize lm2 = dspy.LM( model="openai/gpt-5", - reasoning_effort="medium", + reasoning_effort="medium", max_tokens=16000, temperature=1.0 ) @@ -594,13 +594,13 @@ def test_reasoning_effort_normalization(): def test_native_reasoning_flag_setting(mock_settings, mock_supports): """Test that use_native_reasoning flag is set correctly.""" mock_supports.return_value = True - + # Should set flag when model supports reasoning and has reasoning param dspy.LM(model="openai/gpt-5", reasoning_effort="low", max_tokens=16000, temperature=1.0) mock_settings.use_native_reasoning = True - + mock_supports.return_value = False - + # Should NOT set flag when model doesn't support reasoning dspy.LM(model="openai/gpt-4", reasoning_effort="low", max_tokens=1000, temperature=0.7) @@ -609,13 +609,13 @@ def test_reasoning_content_extraction(): """Test that reasoning models can be created with proper configuration.""" # Test that reasoning models are properly configured lm = dspy.LM( - model="openai/gpt-5", - model_type="responses", - max_tokens=16000, + model="openai/gpt-5", + model_type="responses", + max_tokens=16000, temperature=1.0, reasoning_effort="low" ) - + # Verify reasoning parameters are normalized assert "reasoning" in lm.kwargs assert lm.kwargs["reasoning"]["effort"] == "low" @@ -625,42 +625,42 @@ def test_reasoning_content_extraction(): def test_chain_of_thought_with_native_reasoning(): """Test ChainOfThought with native reasoning vs manual reasoning.""" - + class SimpleSignature(dspy.Signature): """Answer the question.""" question: str = dspy.InputField() answer: str = dspy.OutputField() - + # Test with native reasoning enabled settings.use_native_reasoning = True with mock.patch("dspy.Predict") as mock_predict: mock_predict_instance = mock.MagicMock() mock_predict_instance.return_value = dspy.Prediction(answer="42", reasoning="native reasoning") mock_predict.return_value = mock_predict_instance - + cot = dspy.ChainOfThought(SimpleSignature) result = cot(question="What is the answer?") - + # Should use Predict with original signature (no reasoning field added) mock_predict.assert_called_once() call_args = mock_predict.call_args[0] assert call_args[0] == SimpleSignature - assert hasattr(result, 'reasoning') - - # Reset and test with native reasoning disabled (traditional ChainOfThought) + assert hasattr(result, "reasoning") + + # Reset and test with native reasoning disabled (traditional ChainOfThought) settings.use_native_reasoning = False with mock.patch("dspy.Predict") as mock_predict: mock_predict_instance = mock.MagicMock() mock_predict_instance.return_value = dspy.Prediction(reasoning="step by step...", answer="42") mock_predict.return_value = mock_predict_instance - + cot = dspy.ChainOfThought(SimpleSignature) result = cot(question="What is the answer?") - + # Should use Predict with extended signature (reasoning field added) mock_predict.assert_called_once() call_args = mock_predict.call_args[0] # Check that signature was extended with reasoning field extended_signature = call_args[0] - assert 'reasoning' in extended_signature.fields - assert hasattr(result, 'reasoning') + assert "reasoning" in extended_signature.fields + assert hasattr(result, "reasoning") From 522886367e7fe3a6edb876ec6d72e0ee803f2d40 Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Wed, 17 Sep 2025 18:43:09 -0700 Subject: [PATCH 03/12] Introduce dspy.Reasoning to handle ChainOfThought on reasoning models --- dspy/__init__.py | 2 +- dspy/adapters/__init__.py | 3 +- dspy/adapters/base.py | 36 ++-- dspy/adapters/types/__init__.py | 3 +- dspy/adapters/types/citation.py | 16 +- dspy/adapters/types/reasoning.py | 67 +++++++ dspy/clients/base_lm.py | 68 +++---- dspy/clients/lm.py | 34 ++-- dspy/predict/chain_of_thought.py | 21 +- dspy/primitives/prediction.py | 6 +- tests/adapters/test_chat_adapter.py | 38 ++++ tests/adapters/test_json_adapter.py | 50 ++++- tests/clients/test_lm.py | 257 +++++++++++++++---------- tests/predict/test_chain_of_thought.py | 56 ++++++ 14 files changed, 447 insertions(+), 210 deletions(-) create mode 100644 dspy/adapters/types/reasoning.py diff --git a/dspy/__init__.py b/dspy/__init__.py index ea4c75a862..39d88c3908 100644 --- a/dspy/__init__.py +++ b/dspy/__init__.py @@ -6,7 +6,7 @@ from dspy.evaluate import Evaluate # isort: skip from dspy.clients import * # isort: skip -from dspy.adapters import Adapter, ChatAdapter, JSONAdapter, XMLAdapter, TwoStepAdapter, Image, Audio, History, Type, Tool, ToolCalls, Code # isort: skip +from dspy.adapters import Adapter, ChatAdapter, JSONAdapter, XMLAdapter, TwoStepAdapter, Image, Audio, History, Type, Tool, ToolCalls, Code, Reasoning # isort: skip from dspy.utils.logging_utils import configure_dspy_loggers, disable_logging, enable_logging from dspy.utils.asyncify import asyncify from dspy.utils.syncify import syncify diff --git a/dspy/adapters/__init__.py b/dspy/adapters/__init__.py index 1dea6da47a..b8a0229fc0 100644 --- a/dspy/adapters/__init__.py +++ b/dspy/adapters/__init__.py @@ -2,7 +2,7 @@ from dspy.adapters.chat_adapter import ChatAdapter from dspy.adapters.json_adapter import JSONAdapter from dspy.adapters.two_step_adapter import TwoStepAdapter -from dspy.adapters.types import Audio, Code, History, Image, Tool, ToolCalls, Type +from dspy.adapters.types import Audio, Code, History, Image, Reasoning, Tool, ToolCalls, Type from dspy.adapters.xml_adapter import XMLAdapter __all__ = [ @@ -18,4 +18,5 @@ "TwoStepAdapter", "Tool", "ToolCalls", + "Reasoning", ] diff --git a/dspy/adapters/base.py b/dspy/adapters/base.py index 9d75a39735..55c83c487a 100644 --- a/dspy/adapters/base.py +++ b/dspy/adapters/base.py @@ -6,6 +6,7 @@ from dspy.adapters.types import History, Type from dspy.adapters.types.base_type import split_message_content_for_custom_types +from dspy.adapters.types.reasoning import Reasoning from dspy.adapters.types.tool import Tool, ToolCalls from dspy.experimental import Citations from dspy.signatures.signature import Signature @@ -16,10 +17,16 @@ if TYPE_CHECKING: from dspy.clients.lm import LM -_DEFAULT_NATIVE_RESPONSE_TYPES = [Citations] +_DEFAULT_NATIVE_RESPONSE_TYPES = [Citations, Reasoning] + class Adapter: - def __init__(self, callbacks: list[BaseCallback] | None = None, use_native_function_calling: bool = False, native_response_types: list[type[Type]] | None = None): + def __init__( + self, + callbacks: list[BaseCallback] | None = None, + use_native_function_calling: bool = False, + native_response_types: list[type[Type]] | None = None, + ): self.callbacks = callbacks or [] self.use_native_function_calling = use_native_function_calling self.native_response_types = native_response_types or _DEFAULT_NATIVE_RESPONSE_TYPES @@ -68,7 +75,12 @@ def _call_preprocess( # Handle custom types that use native response for name, field in signature.output_fields.items(): - if isinstance(field.annotation, type) and issubclass(field.annotation, Type) and field.annotation in self.native_response_types: + if ( + isinstance(field.annotation, type) + and issubclass(field.annotation, Type) + and field.annotation in self.native_response_types + and field.annotation.is_natively_supported(lm, lm_kwargs) + ): signature = signature.delete(name) return signature @@ -79,6 +91,7 @@ def _call_postprocess( original_signature: type[Signature], outputs: list[dict[str, Any]], lm: "LM", + lm_kwargs: dict[str, Any], ) -> list[dict[str, Any]]: values = [] @@ -87,14 +100,12 @@ def _call_postprocess( for output in outputs: output_logprobs = None tool_calls = None - reasoning = None text = output if isinstance(output, dict): text = output["text"] output_logprobs = output.get("logprobs") tool_calls = output.get("tool_calls") - reasoning = output.get("reasoning") if text: value = self.parse(processed_signature, text) @@ -119,15 +130,17 @@ def _call_postprocess( # Parse custom types that does not rely on the adapter parsing for name, field in original_signature.output_fields.items(): - if isinstance(field.annotation, type) and issubclass(field.annotation, Type) and field.annotation in self.native_response_types: + if ( + isinstance(field.annotation, type) + and issubclass(field.annotation, Type) + and field.annotation in self.native_response_types + and field.annotation.is_natively_supported(lm, lm_kwargs) + ): value[name] = field.annotation.parse_lm_response(output) if output_logprobs: value["logprobs"] = output_logprobs - if reasoning: - value["reasoning"] = reasoning - values.append(value) return values @@ -144,7 +157,7 @@ def __call__( inputs = self.format(processed_signature, demos, inputs) outputs = lm(messages=inputs, **lm_kwargs) - return self._call_postprocess(processed_signature, signature, outputs, lm) + return self._call_postprocess(processed_signature, signature, outputs, lm, lm_kwargs) async def acall( self, @@ -158,7 +171,7 @@ async def acall( inputs = self.format(processed_signature, demos, inputs) outputs = await lm.acall(messages=inputs, **lm_kwargs) - return self._call_postprocess(processed_signature, signature, outputs, lm) + return self._call_postprocess(processed_signature, signature, outputs, lm, lm_kwargs) def format( self, @@ -409,7 +422,6 @@ def _get_tool_call_output_field_name(self, signature: type[Signature]) -> bool: return name return None - def format_conversation_history( self, signature: type[Signature], diff --git a/dspy/adapters/types/__init__.py b/dspy/adapters/types/__init__.py index 11b9faee1b..f94113ab9c 100644 --- a/dspy/adapters/types/__init__.py +++ b/dspy/adapters/types/__init__.py @@ -3,6 +3,7 @@ from dspy.adapters.types.code import Code from dspy.adapters.types.history import History from dspy.adapters.types.image import Image +from dspy.adapters.types.reasoning import Reasoning from dspy.adapters.types.tool import Tool, ToolCalls -__all__ = ["History", "Image", "Audio", "Type", "Tool", "ToolCalls", "Code"] +__all__ = ["History", "Image", "Audio", "Type", "Tool", "ToolCalls", "Code", "Reasoning"] diff --git a/dspy/adapters/types/citation.py b/dspy/adapters/types/citation.py index 82e55f5a2f..a9e2896d4d 100644 --- a/dspy/adapters/types/citation.py +++ b/dspy/adapters/types/citation.py @@ -52,6 +52,7 @@ class AnswerWithSources(Signature): class Citation(Type): """Individual citation with character location information.""" + type: str = "char_location" cited_text: str document_index: int @@ -71,7 +72,7 @@ def format(self) -> dict[str, Any]: "cited_text": self.cited_text, "document_index": self.document_index, "start_char_index": self.start_char_index, - "end_char_index": self.end_char_index + "end_char_index": self.end_char_index, } if self.document_title: @@ -132,9 +133,7 @@ def validate_input(cls, data: Any): return data # Handle case where data is a list of dicts with citation info - if isinstance(data, list) and all( - isinstance(item, dict) and "cited_text" in item for item in data - ): + if isinstance(data, list) and all(isinstance(item, dict) and "cited_text" in item for item in data): return {"citations": [cls.Citation(**item) for item in data]} # Handle case where data is a dict @@ -145,8 +144,7 @@ def validate_input(cls, data: Any): if isinstance(citations_data, list): return { "citations": [ - cls.Citation(**item) if isinstance(item, dict) else item - for item in citations_data + cls.Citation(**item) if isinstance(item, dict) else item for item in citations_data ] } elif "cited_text" in data: @@ -167,6 +165,11 @@ def __getitem__(self, index): """Allow indexing into citations.""" return self.citations[index] + @classmethod + def is_natively_supported(cls, lm, lm_kwargs) -> bool: + """Whether the Citations type is natively supported by the LM.""" + return lm.model.startswith("anthropic/") + @classmethod def is_streamable(cls) -> bool: """Whether the Citations type is streamable.""" @@ -195,7 +198,6 @@ def parse_stream_chunk(cls, chunk) -> Optional["Citations"]: pass return None - @classmethod def parse_lm_response(cls, response: str | dict[str, Any]) -> Optional["Citations"]: """Parse a LM response into Citations. diff --git a/dspy/adapters/types/reasoning.py b/dspy/adapters/types/reasoning.py new file mode 100644 index 0000000000..8e16c520ea --- /dev/null +++ b/dspy/adapters/types/reasoning.py @@ -0,0 +1,67 @@ +from typing import Any, Optional + +import litellm +import pydantic + +from dspy.adapters.types.base_type import Type + + +class Reasoning(Type): + """Reasoning type in DSPy. + + This type is useful when you want the DSPy output to include the reasoning of the LM. We build this type so that + DSPy can support the reasoning model and non-reasoning model with the same code. + """ + + content: str + + def format(self): + return f"{self.content}" + + @classmethod + def description(cls) -> str: + return "Step by step reasoning of the LM in order to generate the response." + + @pydantic.model_validator(mode="before") + @classmethod + def validate_input(cls, data: Any): + if isinstance(data, cls): + return data + + if isinstance(data, str): + return {"content": data} + + if isinstance(data, dict): + if "content" not in data: + raise ValueError("`content` field is required for `dspy.Reasoning`") + if not isinstance(data["content"], str): + raise ValueError(f"`content` field must be a string, but received type: {type(data['content'])}") + return {"content": data["content"]} + + raise ValueError(f"Received invalid value for `dspy.Reasoning`: {data}") + + @classmethod + def is_natively_supported(cls, lm, lm_kwargs) -> bool: + """Whether the Reasoning type is natively supported by the LM.""" + if not litellm.supports_reasoning(lm.model): + return False + + if "reasoning_effort" in lm_kwargs: + # `lm_kwargs` overrides `lm.kwargs` + reasoning_effort = lm_kwargs["reasoning_effort"] + elif "reasoning_effort" in lm.kwargs: + reasoning_effort = lm.kwargs["reasoning_effort"] + else: + reasoning_effort = None + + return reasoning_effort is not None + + @classmethod + def parse_lm_response(cls, response: str | dict[str, Any]) -> Optional["Reasoning"]: + """Parse the LM response into a Reasoning object.""" + if "reasoning_content" in response: + return Reasoning(content=response["reasoning_content"]) + return None + + def __repr__(self) -> str: + return f"{self.content!r}" diff --git a/dspy/clients/base_lm.py b/dspy/clients/base_lm.py index 2d76b89a68..de8d4182a0 100644 --- a/dspy/clients/base_lm.py +++ b/dspy/clients/base_lm.py @@ -166,12 +166,12 @@ def update_history(self, entry): def _process_completion(self, response, merged_kwargs): """Process the response of OpenAI chat completion API and extract outputs. - + Args: response: The OpenAI chat completion response https://platform.openai.com/docs/api-reference/chat/object merged_kwargs: Merged kwargs from self.kwargs and method kwargs - + Returns: List of processed outputs """ @@ -181,7 +181,7 @@ def _process_completion(self, response, merged_kwargs): output["text"] = c.message.content if hasattr(c, "message") else c["text"] if hasattr(c, "message") and hasattr(c.message, "reasoning_content") and c.message.reasoning_content: - output["reasoning"] = c.message.reasoning_content + output["reasoning_content"] = c.message.reasoning_content if merged_kwargs.get("logprobs"): output["logprobs"] = c.logprobs if hasattr(c, "logprobs") else c["logprobs"] @@ -198,16 +198,15 @@ def _process_completion(self, response, merged_kwargs): if all(len(output) == 1 for output in outputs): # Return a list if every output only has "text" key outputs = [output["text"] for output in outputs] - return outputs def _extract_citations_from_response(self, choice): """Extract citations from LiteLLM response if available. Reference: https://docs.litellm.ai/docs/providers/anthropic#beta-citations-api - + Args: choice: The choice object from response.choices - + Returns: A list of citation dictionaries or None if no citations found """ @@ -221,48 +220,41 @@ def _extract_citations_from_response(self, choice): def _process_response(self, response): """Process the response of OpenAI Response API and extract outputs. - + Args: response: OpenAI Response API response https://platform.openai.com/docs/api-reference/responses/object - + Returns: - List of processed outputs + List of processed outputs, which is always of size 1 because the Response API only supports one output. """ - outputs = [] + text_outputs = [] tool_calls = [] - reasoning_content = None + reasoning_content = [] for output_item in response.output: - if output_item.type == "message": + output_item_type = output_item.type + if output_item_type == "message": for content_item in output_item.content: - outputs.append(content_item.text) - elif output_item.type == "function_call": + text_outputs.append(content_item.text) + elif output_item_type == "function_call": tool_calls.append(output_item.model_dump()) - elif output_item.type == "reasoning": - if hasattr(output_item, "content") and output_item.content: - reasoning_content = output_item.content - elif hasattr(output_item, "summary") and output_item.summary: - if isinstance(output_item.summary, list): - summary_texts = [] - for summary_item in output_item.summary: - if hasattr(summary_item, "text"): - summary_texts.append(summary_item.text) - reasoning_content = "\n\n".join(summary_texts) if summary_texts else output_item.summary - else: - reasoning_content = output_item.summary - - if reasoning_content: - if len(outputs) == 1 and isinstance(outputs[0], str): - outputs = [{"text": outputs[0], "reasoning": reasoning_content}] - elif outputs and isinstance(outputs[0], str): - outputs[0] = {"text": outputs[0], "reasoning": reasoning_content} - elif outputs and isinstance(outputs[0], dict): - outputs[0]["reasoning"] = reasoning_content - - if tool_calls: - outputs.append({"tool_calls": tool_calls}) - return outputs + elif output_item_type == "reasoning": + if getattr(output_item, "content", None) and len(output_item.content) > 0: + for content_item in output_item.content: + reasoning_content.append(content_item.text) + elif getattr(output_item, "summary", None) and len(output_item.summary) > 0: + for summary_item in output_item.summary: + reasoning_content.append(summary_item.text) + + result = {} + if len(text_outputs) > 0: + result["text"] = "".join(text_outputs) + if len(tool_calls) > 0: + result["tool_calls"] = tool_calls + if len(reasoning_content) > 0: + result["reasoning_content"] = "".join(reasoning_content) + return [result] def inspect_history(n: int = 1): diff --git a/dspy/clients/lm.py b/dspy/clients/lm.py index 3ab39eb6a0..097f2da1ee 100644 --- a/dspy/clients/lm.py +++ b/dspy/clients/lm.py @@ -103,25 +103,17 @@ def __init__( self._warn_zero_temp_rollout(self.kwargs.get("temperature"), self.kwargs.get("rollout_id")) - # Set flag if model supports native reasoning AND user specified any reasoning parameter - reasoning_params = ["reasoning_effort", "reasoning", "thinking"] # Common reasoning parameter names - has_reasoning_param = any(param in self.kwargs for param in reasoning_params) - if litellm.supports_reasoning(self.model) and has_reasoning_param: - settings.use_native_reasoning = True - - # Normalize reasoning_effort to get reasoning summaries (for OpenAI reasoning models which don't expose reasoning content) - if ("reasoning_effort" in self.kwargs and - (self.model_type == "responses" or - ("openai/" in self.model.lower() and litellm.supports_reasoning(self.model)))): + # Normalize reasoning_effort to get reasoning summaries (for OpenAI reasoning models which don't expose + # reasoning content) + if "reasoning_effort" in self.kwargs and ( + self.model_type == "responses" + or ("openai/" in self.model.lower() and litellm.supports_reasoning(self.model)) + ): effort = self.kwargs.pop("reasoning_effort") self.kwargs["reasoning"] = {"effort": effort, "summary": "auto"} def _warn_zero_temp_rollout(self, temperature: float | None, rollout_id): - if ( - not self._warned_zero_temp_rollout - and rollout_id is not None - and (temperature is None or temperature == 0) - ): + if not self._warned_zero_temp_rollout and rollout_id is not None and (temperature is None or temperature == 0): warnings.warn( "rollout_id has no effect when temperature=0; set temperature>0 to bypass the cache.", stacklevel=3, @@ -147,10 +139,7 @@ def forward(self, prompt=None, messages=None, **kwargs): messages = messages or [{"role": "user", "content": prompt}] if self.use_developer_role and self.model_type == "responses": - messages = [ - {**m, "role": "developer"} if m.get("role") == "system" else m - for m in messages - ] + messages = [{**m, "role": "developer"} if m.get("role") == "system" else m for m in messages] kwargs = {**self.kwargs, **kwargs} self._warn_zero_temp_rollout(kwargs.get("temperature"), kwargs.get("rollout_id")) if kwargs.get("rollout_id") is None: @@ -183,10 +172,7 @@ async def aforward(self, prompt=None, messages=None, **kwargs): messages = messages or [{"role": "user", "content": prompt}] if self.use_developer_role and self.model_type == "responses": - messages = [ - {**m, "role": "developer"} if m.get("role") == "system" else m - for m in messages - ] + messages = [{**m, "role": "developer"} if m.get("role") == "system" else m for m in messages] kwargs = {**self.kwargs, **kwargs} self._warn_zero_temp_rollout(kwargs.get("temperature"), kwargs.get("rollout_id")) if kwargs.get("rollout_id") is None: @@ -437,6 +423,7 @@ async def alitellm_text_completion(request: dict[str, Any], num_retries: int, ca **request, ) + def litellm_responses_completion(request: dict[str, Any], num_retries: int, cache: dict[str, Any] | None = None): cache = cache or {"no-cache": True, "no-store": True} request = dict(request) @@ -464,6 +451,7 @@ async def alitellm_responses_completion(request: dict[str, Any], num_retries: in **request, ) + def _convert_chat_request_to_responses_request(request: dict[str, Any]): request = dict(request) if "messages" in request: diff --git a/dspy/predict/chain_of_thought.py b/dspy/predict/chain_of_thought.py index 2d9681407f..2c66682213 100644 --- a/dspy/predict/chain_of_thought.py +++ b/dspy/predict/chain_of_thought.py @@ -1,10 +1,8 @@ from typing import Any -from pydantic.fields import FieldInfo - import dspy -from dspy.dsp.utils.settings import settings from dspy.primitives.module import Module +from dspy.signatures.field import OutputField from dspy.signatures.signature import Signature, ensure_signature @@ -12,8 +10,6 @@ class ChainOfThought(Module): def __init__( self, signature: str | type[Signature], - rationale_field: FieldInfo | None = None, - rationale_field_type: type = str, **config: dict[str, Any], ): """ @@ -21,22 +17,15 @@ def __init__( Args: signature (Type[dspy.Signature]): The signature of the module. - rationale_field (Optional[Union[dspy.OutputField, pydantic.fields.FieldInfo]]): The field that will contain the reasoning. - rationale_field_type (Type): The type of the rationale field. **config: The configuration for the module. """ super().__init__() signature = ensure_signature(signature) - if getattr(settings, "use_native_reasoning", False): - self.predict = dspy.Predict(signature, **config) - else: - prefix = "Reasoning: Let's think step by step in order to" - desc = "${reasoning}" - rationale_field_type = rationale_field.annotation if rationale_field else rationale_field_type - rationale_field = rationale_field if rationale_field else dspy.OutputField(prefix=prefix, desc=desc) - extended_signature = signature.prepend(name="reasoning", field=rationale_field, type_=rationale_field_type) - self.predict = dspy.Predict(extended_signature, **config) + from dspy.adapters.types.reasoning import Reasoning + + extended_signature = signature.prepend(name="reasoning", field=OutputField(), type_=Reasoning) + self.predict = dspy.Predict(extended_signature, **config) def forward(self, **kwargs): return self.predict(**kwargs) diff --git a/dspy/primitives/prediction.py b/dspy/primitives/prediction.py index 4f32fe9fce..79b209a4a1 100644 --- a/dspy/primitives/prediction.py +++ b/dspy/primitives/prediction.py @@ -3,14 +3,14 @@ class Prediction(Example): """A prediction object that contains the output of a DSPy module. - + Prediction inherits from Example. - + To allow feedback-augmented scores, Prediction supports comparison operations (<, >, <=, >=) for Predictions with a `score` field. The comparison operations compare the 'score' values as floats. For equality comparison, Predictions are equal if their underlying data stores are equal (inherited from Example). - + Arithmetic operations (+, /, etc.) are also supported for Predictions with a 'score' field, operating on the score value. """ diff --git a/tests/adapters/test_chat_adapter.py b/tests/adapters/test_chat_adapter.py index 0d5ee764cf..0af63a3bdc 100644 --- a/tests/adapters/test_chat_adapter.py +++ b/tests/adapters/test_chat_adapter.py @@ -591,3 +591,41 @@ def get_weather(city: str) -> str: assert result[0]["tool_calls"] == dspy.ToolCalls( tool_calls=[dspy.ToolCalls.ToolCall(name="get_weather", args={"city": "Paris"})] ) + + +def test_chat_adapter_native_reasoning(): + class MySignature(dspy.Signature): + question: str = dspy.InputField() + reasoning: dspy.Reasoning = dspy.OutputField() + answer: str = dspy.OutputField() + + adapter = dspy.ChatAdapter() + + with mock.patch("litellm.completion") as mock_completion: + mock_completion.return_value = ModelResponse( + choices=[ + Choices( + message=Message( + content="[[ ## answer ## ]]\nParis\n[[ ## completion ## ]]", + reasoning_content="Step-by-step thinking about the capital of France", + ), + ) + ], + model="anthropic/claude-3-7-sonnet-20250219", + ) + modified_signature = adapter._call_preprocess( + dspy.LM(model="anthropic/claude-3-7-sonnet-20250219", reasoning_effort="low", cache=False), + {}, + MySignature, + {"question": "What is the capital of France?"}, + ) + assert "reasoning" not in modified_signature.output_fields + + result = adapter( + dspy.LM(model="anthropic/claude-3-7-sonnet-20250219", reasoning_effort="low", cache=False), + {}, + MySignature, + [], + {"question": "What is the capital of France?"}, + ) + assert result[0]["reasoning"] == dspy.Reasoning(content="Step-by-step thinking about the capital of France") diff --git a/tests/adapters/test_json_adapter.py b/tests/adapters/test_json_adapter.py index c6ba8b2388..50a50498a6 100644 --- a/tests/adapters/test_json_adapter.py +++ b/tests/adapters/test_json_adapter.py @@ -332,8 +332,16 @@ class MySignature(dspy.Signature): adapter = dspy.JSONAdapter() messages = adapter.format(MySignature, [], {"document": document_wrapper}) - expected_doc1_content = {"type": "document", "source": {"type": "text", "media_type": "text/plain", "data": "Hello, world!"}, "citations": {"enabled": True}} - expected_doc2_content = {"type": "document", "source": {"type": "text", "media_type": "text/plain", "data": "Hello, world 2!"}, "citations": {"enabled": True}} + expected_doc1_content = { + "type": "document", + "source": {"type": "text", "media_type": "text/plain", "data": "Hello, world!"}, + "citations": {"enabled": True}, + } + expected_doc2_content = { + "type": "document", + "source": {"type": "text", "media_type": "text/plain", "data": "Hello, world 2!"}, + "citations": {"enabled": True}, + } assert expected_doc1_content in messages[1]["content"] assert expected_doc2_content in messages[1]["content"] @@ -816,3 +824,41 @@ def get_weather(city: str) -> str: mock_completion.assert_called_once() _, call_kwargs = mock_completion.call_args assert call_kwargs["response_format"] == {"type": "json_object"} + + +def test_json_adapter_native_reasoning(): + class MySignature(dspy.Signature): + question: str = dspy.InputField() + reasoning: dspy.Reasoning = dspy.OutputField() + answer: str = dspy.OutputField() + + adapter = dspy.JSONAdapter() + + with mock.patch("litellm.completion") as mock_completion: + mock_completion.return_value = ModelResponse( + choices=[ + Choices( + message=Message( + content="{'answer': 'Paris'}", + reasoning_content="Step-by-step thinking about the capital of France", + ), + ) + ], + model="anthropic/claude-3-7-sonnet-20250219", + ) + modified_signature = adapter._call_preprocess( + dspy.LM(model="anthropic/claude-3-7-sonnet-20250219", reasoning_effort="low", cache=False), + {}, + MySignature, + {"question": "What is the capital of France?"}, + ) + assert "reasoning" not in modified_signature.output_fields + + result = adapter( + dspy.LM(model="anthropic/claude-3-7-sonnet-20250219", reasoning_effort="low", cache=False), + {}, + MySignature, + [], + {"question": "What is the capital of France?"}, + ) + assert result[0]["reasoning"] == dspy.Reasoning(content="Step-by-step thinking about the capital of France") diff --git a/tests/clients/test_lm.py b/tests/clients/test_lm.py index 8756c922f1..97333996aa 100644 --- a/tests/clients/test_lm.py +++ b/tests/clients/test_lm.py @@ -2,7 +2,7 @@ import time import warnings from unittest import mock -from unittest.mock import patch +from unittest.mock import Mock, patch import litellm import pydantic @@ -10,9 +10,10 @@ from litellm.types.llms.openai import ResponseAPIUsage, ResponsesAPIResponse from litellm.utils import Choices, Message, ModelResponse from openai import RateLimitError +from openai.types.responses import ResponseOutputMessage, ResponseReasoningItem +from openai.types.responses.response_reasoning_item import Summary import dspy -from dspy.dsp.utils.settings import settings from dspy.utils.usage_tracker import track_usage @@ -26,7 +27,7 @@ def make_response(output_blocks): model="openai/dspy-test-model", object="response", output=output_blocks, - metadata = {}, + metadata={}, parallel_tool_calls=False, temperature=1.0, tool_choice="auto", @@ -281,6 +282,7 @@ def test_reasoning_model_token_parameter(): assert "max_tokens" in lm.kwargs assert lm.kwargs["max_tokens"] == 1000 + @pytest.mark.parametrize("model_name", ["openai/o1", "openai/gpt-5-nano"]) def test_reasoning_model_requirements(model_name): # Should raise assertion error if temperature or max_tokens requirements not met @@ -475,42 +477,54 @@ def test_disable_history(): model="openai/gpt-4o-mini", ) -def test_responses_api(litellm_test_server): - api_base, _ = litellm_test_server - expected_text = "This is a test answer from responses API." +def test_responses_api(): api_response = make_response( output_blocks=[ - { - "id": "msg_1", - "type": "message", - "role": "assistant", - "status": "completed", - "content": [ - {"type": "output_text", "text": expected_text, "annotations": []} - ], - } + ResponseOutputMessage( + **{ + "id": "msg_1", + "type": "message", + "role": "assistant", + "status": "completed", + "content": [ + {"type": "output_text", "text": "This is a test answer from responses API.", "annotations": []} + ], + }, + ), + ResponseReasoningItem( + **{ + "id": "reasoning_1", + "type": "reasoning", + "summary": [Summary(**{"type": "summary_text", "text": "This is a dummy reasoning."})], + }, + ), ] ) with mock.patch("litellm.responses", autospec=True, return_value=api_response) as dspy_responses: lm = dspy.LM( - model="openai/dspy-test-model", - api_base=api_base, - api_key="fakekey", + model="openai/gpt-5-mini", model_type="responses", cache=False, + temperature=1.0, + max_tokens=16000, ) - assert lm("openai query") == [expected_text] + lm_result = lm("openai query") + + assert lm_result == [ + { + "text": "This is a test answer from responses API.", + "reasoning_content": "This is a dummy reasoning.", + } + ] dspy_responses.assert_called_once() - assert dspy_responses.call_args.kwargs["model"] == "openai/dspy-test-model" + assert dspy_responses.call_args.kwargs["model"] == "openai/gpt-5-mini" def test_lm_replaces_system_with_developer_role(): - with mock.patch( - "dspy.clients.lm.litellm_responses_completion", return_value={"choices": []} - ) as mock_completion: + with mock.patch("dspy.clients.lm.litellm_responses_completion", return_value={"choices": []}) as mock_completion: lm = dspy.LM( "openai/gpt-4o-mini", cache=False, @@ -518,10 +532,7 @@ def test_lm_replaces_system_with_developer_role(): use_developer_role=True, ) lm.forward(messages=[{"role": "system", "content": "hi"}]) - assert ( - mock_completion.call_args.kwargs["request"]["messages"][0]["role"] - == "developer" - ) + assert mock_completion.call_args.kwargs["request"]["messages"][0]["role"] == "developer" def test_responses_api_tool_calls(litellm_test_server): @@ -559,61 +570,27 @@ def test_reasoning_effort_normalization(): with mock.patch("litellm.supports_reasoning", return_value=True): # OpenAI model with Responses API - should normalize lm1 = dspy.LM( - model="openai/gpt-5", - model_type="responses", - reasoning_effort="low", - max_tokens=16000, - temperature=1.0 + model="openai/gpt-5", model_type="responses", reasoning_effort="low", max_tokens=16000, temperature=1.0 ) assert "reasoning_effort" not in lm1.kwargs assert lm1.kwargs["reasoning"] == {"effort": "low", "summary": "auto"} # OpenAI model with Chat API - should normalize - lm2 = dspy.LM( - model="openai/gpt-5", - reasoning_effort="medium", - max_tokens=16000, - temperature=1.0 - ) + lm2 = dspy.LM(model="openai/gpt-5", reasoning_effort="medium", max_tokens=16000, temperature=1.0) assert "reasoning_effort" not in lm2.kwargs assert lm2.kwargs["reasoning"] == {"effort": "medium", "summary": "auto"} # Non-OpenAI model - should NOT normalize - lm3 = dspy.LM( - model="deepseek-ai/DeepSeek-R1", - reasoning_effort="low", - max_tokens=4000, - temperature=0.7 - ) + lm3 = dspy.LM(model="deepseek-ai/DeepSeek-R1", reasoning_effort="low", max_tokens=4000, temperature=0.7) assert "reasoning_effort" in lm3.kwargs assert "reasoning" not in lm3.kwargs -@mock.patch("litellm.supports_reasoning") -@mock.patch("dspy.dsp.utils.settings") -def test_native_reasoning_flag_setting(mock_settings, mock_supports): - """Test that use_native_reasoning flag is set correctly.""" - mock_supports.return_value = True - - # Should set flag when model supports reasoning and has reasoning param - dspy.LM(model="openai/gpt-5", reasoning_effort="low", max_tokens=16000, temperature=1.0) - mock_settings.use_native_reasoning = True - - mock_supports.return_value = False - - # Should NOT set flag when model doesn't support reasoning - dspy.LM(model="openai/gpt-4", reasoning_effort="low", max_tokens=1000, temperature=0.7) - - def test_reasoning_content_extraction(): """Test that reasoning models can be created with proper configuration.""" # Test that reasoning models are properly configured lm = dspy.LM( - model="openai/gpt-5", - model_type="responses", - max_tokens=16000, - temperature=1.0, - reasoning_effort="low" + model="openai/gpt-5", model_type="responses", max_tokens=16000, temperature=1.0, reasoning_effort="low" ) # Verify reasoning parameters are normalized @@ -623,44 +600,112 @@ def test_reasoning_content_extraction(): assert lm.kwargs["max_completion_tokens"] == 16000 -def test_chain_of_thought_with_native_reasoning(): - """Test ChainOfThought with native reasoning vs manual reasoning.""" - - class SimpleSignature(dspy.Signature): - """Answer the question.""" - question: str = dspy.InputField() - answer: str = dspy.OutputField() - - # Test with native reasoning enabled - settings.use_native_reasoning = True - with mock.patch("dspy.Predict") as mock_predict: - mock_predict_instance = mock.MagicMock() - mock_predict_instance.return_value = dspy.Prediction(answer="42", reasoning="native reasoning") - mock_predict.return_value = mock_predict_instance - - cot = dspy.ChainOfThought(SimpleSignature) - result = cot(question="What is the answer?") - - # Should use Predict with original signature (no reasoning field added) - mock_predict.assert_called_once() - call_args = mock_predict.call_args[0] - assert call_args[0] == SimpleSignature - assert hasattr(result, "reasoning") - - # Reset and test with native reasoning disabled (traditional ChainOfThought) - settings.use_native_reasoning = False - with mock.patch("dspy.Predict") as mock_predict: - mock_predict_instance = mock.MagicMock() - mock_predict_instance.return_value = dspy.Prediction(reasoning="step by step...", answer="42") - mock_predict.return_value = mock_predict_instance - - cot = dspy.ChainOfThought(SimpleSignature) - result = cot(question="What is the answer?") - - # Should use Predict with extended signature (reasoning field added) - mock_predict.assert_called_once() - call_args = mock_predict.call_args[0] - # Check that signature was extended with reasoning field - extended_signature = call_args[0] - assert "reasoning" in extended_signature.fields - assert hasattr(result, "reasoning") +def test_call_reasoning_model_with_chat_api(): + """Test that Chat API properly handles reasoning models and returns data in correct format.""" + # Create message with reasoning_content attribute + message = Message(content="The answer is 4", role="assistant") + # Add reasoning_content attribute + message.reasoning_content = "Step 1: I need to add 2 + 2\nStep 2: 2 + 2 = 4\nTherefore, the answer is 4" + + # Create choice with the message + mock_choice = Choices(message=message) + + # Mock response with reasoning content for chat completion + mock_response = ModelResponse( + choices=[mock_choice], + model="openai/gpt-5", + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, + ) + + with mock.patch("litellm.completion", return_value=mock_response) as mock_completion: + with mock.patch("litellm.supports_reasoning", return_value=True): + # Create reasoning model with chat API + lm = dspy.LM( + model="openai/gpt-5", + model_type="chat", + temperature=1.0, + max_tokens=16000, + reasoning_effort="low", + cache=False, + ) + + # Test the call + result = lm("What is 2 + 2?") + + # Verify the response format + assert isinstance(result, list) + assert len(result) == 1 + assert isinstance(result[0], dict) + assert "text" in result[0] + assert "reasoning_content" in result[0] + assert result[0]["text"] == "The answer is 4" + assert "Step 1" in result[0]["reasoning_content"] + + # Verify mock was called with correct parameters + mock_completion.assert_called_once() + call_kwargs = mock_completion.call_args.kwargs + assert call_kwargs["model"] == "openai/gpt-5" + assert "reasoning" in call_kwargs + assert call_kwargs["reasoning"]["effort"] == "low" + assert call_kwargs["reasoning"]["summary"] == "auto" + assert "reasoning_effort" not in call_kwargs # Should be normalized + + +def test_call_reasoning_model_with_responses_api(): + """Test that Responses API properly handles reasoning models and returns data in correct format.""" + # Create mock content item for message + content_item = Mock() + content_item.text = "The answer is 4" + + # Create mock message output item + message_item = Mock() + message_item.type = "message" + message_item.content = [content_item] + + # Create mock reasoning content item + reasoning_content_item = Mock() + reasoning_content_item.text = "Step 1: I need to add 2 + 2\nStep 2: 2 + 2 = 4\nTherefore, the answer is 4" + + # Create mock reasoning output item + reasoning_item = Mock() + reasoning_item.type = "reasoning" + reasoning_item.content = [reasoning_content_item] + + # Create mock response + mock_response = Mock() + mock_response.output = [message_item, reasoning_item] + mock_response.usage = {"input_tokens": 10, "output_tokens": 20, "total_tokens": 30} + mock_response.model = "openai/gpt-5" + + with mock.patch("litellm.responses", return_value=mock_response) as mock_responses: + with mock.patch("litellm.supports_reasoning", return_value=True): + # Create reasoning model with responses API + lm = dspy.LM( + model="openai/gpt-5", + model_type="responses", + temperature=1.0, + max_tokens=16000, + reasoning_effort="medium", + cache=False, + ) + + # Test the call + result = lm("What is 2 + 2?") + + # Verify the response format + assert isinstance(result, list) + assert len(result) == 1 + assert isinstance(result[0], dict) + assert "text" in result[0] + assert "reasoning_content" in result[0] + assert result[0]["text"] == "The answer is 4" + assert "Step 1" in result[0]["reasoning_content"] + + # Verify mock was called with correct parameters + mock_responses.assert_called_once() + call_kwargs = mock_responses.call_args.kwargs + assert call_kwargs["model"] == "openai/gpt-5" + assert "reasoning" in call_kwargs + assert call_kwargs["reasoning"]["effort"] == "medium" + assert call_kwargs["reasoning"]["summary"] == "auto" + assert "reasoning_effort" not in call_kwargs # Should be normalized diff --git a/tests/predict/test_chain_of_thought.py b/tests/predict/test_chain_of_thought.py index 247256dba7..4abbf72bce 100644 --- a/tests/predict/test_chain_of_thought.py +++ b/tests/predict/test_chain_of_thought.py @@ -1,4 +1,7 @@ +from unittest import mock + import pytest +from litellm.utils import Choices, Message, ModelResponse import dspy from dspy import ChainOfThought @@ -23,3 +26,56 @@ async def test_async_chain_of_thought(): program = ChainOfThought("question -> answer") result = await program.acall(question="What is 1+1?") assert result.answer == "2" + + +def test_chain_of_thought_with_native_reasoning(): + """Test ChainOfThought with native reasoning support where LM returns reasoning natively.""" + + lm = dspy.LM(model="anthropic/claude-3-7-sonnet-20250219", reasoning_effort="low", cache=False) + dspy.settings.configure(lm=lm) + + with mock.patch("litellm.completion") as mock_completion: + mock_completion.return_value = ModelResponse( + choices=[ + Choices( + message=Message( + content="[[ ## answer ## ]]\nParis\n[[ ## completion ## ]]", + reasoning_content="Step-by-step thinking about the capital of France", + ), + ) + ], + model="anthropic/claude-3-7-sonnet-20250219", + ) + + cot = ChainOfThought("question -> answer") + result = cot(question="What is the capital of France?") + assert result.answer == "Paris" + assert isinstance(result.reasoning, dspy.Reasoning) + assert result.reasoning.content == "Step-by-step thinking about the capital of France" + + +def test_chain_of_thought_with_manual_reasoning(): + """Test ChainOfThought with manual reasoning where LM doesn't support native reasoning.""" + lm = dspy.LM(model="openai/gpt-4o-mini") + dspy.settings.configure(lm=lm) + + with mock.patch("litellm.completion") as mock_completion: + mock_completion.return_value = ModelResponse( + choices=[ + Choices( + reasoning="Step-by-step thinking about the capital of France", + message=Message( + content=( + "[[ ## reasoning ## ]]\nStep-by-step thinking about the capital of France\n" + "[[ ## answer ## ]]\nParis\n[[ ## completion ## ]]" + ) + ), + ) + ], + model="openai/gpt-4o-mini", + ) + + cot = ChainOfThought("question -> answer") + result = cot(question="What is the capital of France?") + assert result.answer == "Paris" + assert result.reasoning.content == "Step-by-step thinking about the capital of France" From 32109141e464684cbeb0dbb68b5a2188cf377c2f Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Wed, 17 Sep 2025 18:48:53 -0700 Subject: [PATCH 04/12] remove unintended file --- script_tmp/custom_streaming.py | 94 ---------------------------------- 1 file changed, 94 deletions(-) delete mode 100644 script_tmp/custom_streaming.py diff --git a/script_tmp/custom_streaming.py b/script_tmp/custom_streaming.py deleted file mode 100644 index cbbd473fe4..0000000000 --- a/script_tmp/custom_streaming.py +++ /dev/null @@ -1,94 +0,0 @@ -import asyncio - -from pydantic import BaseModel - -import dspy - - -class Document(BaseModel): - doc_id: str - text: str - - -class Request(BaseModel): - question: str - documents: list[Document] - - -class Citation(BaseModel): - doc_id: str - start_token_index: int - end_token_index: int - - -class CitationWithText(Citation): - cited_text: str - - -class ResponseChunk(BaseModel): - answer_chunk: str - citations: list[Citation] | None - - -class Response(BaseModel): - answer_chunks: list[ResponseChunk] - - -class AnswerWithCitation(dspy.Signature): - """ - You are a helpful assistant that answers questions based on the provided documents. - To prove the answer is fully grounded, you must cite the content from source documents used to generate the answer chunk immediately after each chunk. - Do not use [1], [2] in the generated text and put citations at the very end. - Instead, output separate answer chunks with citations attached to each chunk. - Keep both the answer and its citations very concise. - The token index within each document starts with 0. - The end index is exclusive. - """ - - question: str = dspy.InputField() - documents: list[Document] = dspy.InputField() - answer_chunks: list[Response] = dspy.OutputField( - desc="answer with citations. If no citation is found, the `Response.citations` field is None. Make sure citation is only applied to its supported text" - ) - - -lm = dspy.LM("openai/gpt-4o-mini", cache=False) - -dspy.configure(lm=lm) - -# Put some space -documents = [ - Document( - doc_id="1", - text="Databricks provides a unified platform for data engineering, data science, and machine learning.", - ), - Document(doc_id="2", text="Databricks supports open source projects like Spark and MLflow."), - Document(doc_id="3", text="Snowflake is a competitor to Databricks."), - Document( - doc_id="4", - text="Agent Bricks provides a simple approach to build and optimize domain-specific, high-quality AI agent systems for common AI use cases. Agent Bricks streamlines the implementation of AI agent systems so that users can focus on the problem, data, and metrics instead. Agent Bricks is a Databricks Designated Service, which means that it uses Databricks Geos to manage data residency when processing customer content.", - ), -] - - -predict = dspy.Predict(AnswerWithCitation) - -streamed_predict = dspy.streamify( - predict, - stream_listeners=[dspy.streaming.StreamListener(signature_field_name="answer_chunks")], -) - -output = streamed_predict(question="What does databricks do?", documents=documents) - - -async def main(): - concated = [] - async for chunk in output: - if isinstance(chunk, dspy.streaming.StreamResponse): - concated.append(chunk.chunk) - - return "".join(concated) - - -concat_output = asyncio.run(main()) -print(concat_output) From b2daf8f91bc483c7078ec5f7dc7e845e724d8486 Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Thu, 18 Sep 2025 13:41:09 -0700 Subject: [PATCH 05/12] fix --- dspy/clients/base_lm.py | 10 +++++----- dspy/primitives/prediction.py | 6 +++--- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/dspy/clients/base_lm.py b/dspy/clients/base_lm.py index de8d4182a0..4e49fbe6c2 100644 --- a/dspy/clients/base_lm.py +++ b/dspy/clients/base_lm.py @@ -230,7 +230,7 @@ def _process_response(self, response): """ text_outputs = [] tool_calls = [] - reasoning_content = [] + reasoning_contents = [] for output_item in response.output: output_item_type = output_item.type @@ -242,18 +242,18 @@ def _process_response(self, response): elif output_item_type == "reasoning": if getattr(output_item, "content", None) and len(output_item.content) > 0: for content_item in output_item.content: - reasoning_content.append(content_item.text) + reasoning_contents.append(content_item.text) elif getattr(output_item, "summary", None) and len(output_item.summary) > 0: for summary_item in output_item.summary: - reasoning_content.append(summary_item.text) + reasoning_contents.append(summary_item.text) result = {} if len(text_outputs) > 0: result["text"] = "".join(text_outputs) if len(tool_calls) > 0: result["tool_calls"] = tool_calls - if len(reasoning_content) > 0: - result["reasoning_content"] = "".join(reasoning_content) + if len(reasoning_contents) > 0: + result["reasoning_content"] = "".join(reasoning_contents) return [result] diff --git a/dspy/primitives/prediction.py b/dspy/primitives/prediction.py index 79b209a4a1..4f32fe9fce 100644 --- a/dspy/primitives/prediction.py +++ b/dspy/primitives/prediction.py @@ -3,14 +3,14 @@ class Prediction(Example): """A prediction object that contains the output of a DSPy module. - + Prediction inherits from Example. - + To allow feedback-augmented scores, Prediction supports comparison operations (<, >, <=, >=) for Predictions with a `score` field. The comparison operations compare the 'score' values as floats. For equality comparison, Predictions are equal if their underlying data stores are equal (inherited from Example). - + Arithmetic operations (+, /, etc.) are also supported for Predictions with a 'score' field, operating on the score value. """ From 3cff43aeafe383c02d3c27723044906e7594b719 Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Thu, 18 Sep 2025 16:38:52 -0700 Subject: [PATCH 06/12] make reasoning string-like --- dspy/adapters/types/reasoning.py | 4 ---- dspy/adapters/utils.py | 5 ++++- ...om_component_selector_custom_instruction_proposer.json | 2 +- tests/teleprompt/test_gepa.py | 8 ++++---- 4 files changed, 9 insertions(+), 10 deletions(-) diff --git a/dspy/adapters/types/reasoning.py b/dspy/adapters/types/reasoning.py index 8e16c520ea..81d908f580 100644 --- a/dspy/adapters/types/reasoning.py +++ b/dspy/adapters/types/reasoning.py @@ -18,10 +18,6 @@ class Reasoning(Type): def format(self): return f"{self.content}" - @classmethod - def description(cls) -> str: - return "Step by step reasoning of the LM in order to generate the response." - @pydantic.model_validator(mode="before") @classmethod def validate_input(cls, data: Any): diff --git a/dspy/adapters/utils.py b/dspy/adapters/utils.py index f38a77ee8a..955e3b59d0 100644 --- a/dspy/adapters/utils.py +++ b/dspy/adapters/utils.py @@ -12,6 +12,7 @@ from pydantic.fields import FieldInfo from dspy.adapters.types.base_type import Type as DspyType +from dspy.adapters.types.reasoning import Reasoning from dspy.signatures.utils import get_dspy_field_type @@ -84,7 +85,7 @@ def move_type_to_front(d): def translate_field_type(field_name, field_info): field_type = field_info.annotation - if get_dspy_field_type(field_info) == "input" or field_type is str: + if get_dspy_field_type(field_info) == "input" or field_type is str or field_type is Reasoning: desc = "" elif field_type is bool: desc = "must be True or False" @@ -190,6 +191,8 @@ def get_annotation_name(annotation): origin = get_origin(annotation) args = get_args(annotation) if origin is None: + if annotation is Reasoning: + return "str" if hasattr(annotation, "__name__"): return annotation.__name__ else: diff --git a/tests/teleprompt/gepa_dummy_lm_custom_component_selector_custom_instruction_proposer.json b/tests/teleprompt/gepa_dummy_lm_custom_component_selector_custom_instruction_proposer.json index a49b94dcc8..2f4af4649d 100644 --- a/tests/teleprompt/gepa_dummy_lm_custom_component_selector_custom_instruction_proposer.json +++ b/tests/teleprompt/gepa_dummy_lm_custom_component_selector_custom_instruction_proposer.json @@ -1 +1 @@ -{"lm": [{"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76SqVPjCr5S9iY10Qy1IeMcYRAD', created=1757479932, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\\n\\n[[ ## minute ## ]]\\n10\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\n\n[[ ## minute ## ]]\n10\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.010098750000000002, "timestamp": "2025-09-09T22:45:47.013627", "uuid": "af5f7d07-378a-4000-a8e9-2a0f0aa478b4", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76T11KnCZm6PV4gqeWiN0K0WXC9', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe hour hand is pointing at the 10 on the clock face, so the hour is 10.\\n\\n[[ ## hour ## ]]\\n10\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe hour hand is pointing at the 10 on the clock face, so the hour is 10.\n\n[[ ## hour ## ]]\n10\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009468750000000001, "timestamp": "2025-09-09T22:45:47.014155", "uuid": "bd54d56f-c1bc-4f58-b46b-10cea63c6a2b", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76Tzq07EuvUd6NqFPi5sDlZQ1fR', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\\n\\n[[ ## hour ## ]]\\n1\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n\n[[ ## hour ## ]]\n1\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.01118875, "timestamp": "2025-09-09T22:45:47.014524", "uuid": "da555aaa-5dec-4ac8-86db-c747116e2286", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76TPH60CaoQjw7UCWgM4sxc74FQ', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\\n\\n[[ ## minute ## ]]\\n15\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n\n[[ ## minute ## ]]\n15\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.00666875, "timestamp": "2025-09-09T22:45:47.014673", "uuid": "8da47789-6ebd-45e1-a2fb-cd998d8f6e3e", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76Tzq07EuvUd6NqFPi5sDlZQ1fR', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\\n\\n[[ ## hour ## ]]\\n1\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n\n[[ ## hour ## ]]\n1\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.01118875, "timestamp": "2025-09-09T22:45:47.022562", "uuid": "abd91c7e-c75e-481d-a8ff-6024714dfcb2", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76TPH60CaoQjw7UCWgM4sxc74FQ', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\\n\\n[[ ## minute ## ]]\\n15\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n\n[[ ## minute ## ]]\n15\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.00666875, "timestamp": "2025-09-09T22:45:47.022935", "uuid": "a15980e8-9929-4229-b7e3-ef5d394a15c8", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76Tzq07EuvUd6NqFPi5sDlZQ1fR', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\\n\\n[[ ## hour ## ]]\\n1\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n\n[[ ## hour ## ]]\n1\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.01118875, "timestamp": "2025-09-09T22:45:47.023794", "uuid": "dc0d82aa-15cb-444a-8a92-5081a34e48af", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76TPH60CaoQjw7UCWgM4sxc74FQ', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\\n\\n[[ ## minute ## ]]\\n15\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n\n[[ ## minute ## ]]\n15\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.00666875, "timestamp": "2025-09-09T22:45:47.024625", "uuid": "64706167-d1ad-41c6-919d-ad049a624b2d", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76T11KnCZm6PV4gqeWiN0K0WXC9', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe hour hand is pointing at the 10 on the clock face, so the hour is 10.\\n\\n[[ ## hour ## ]]\\n10\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe hour hand is pointing at the 10 on the clock face, so the hour is 10.\n\n[[ ## hour ## ]]\n10\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009468750000000001, "timestamp": "2025-09-09T22:45:47.025768", "uuid": "11397f7e-cfd9-43a0-a774-e34b74fc2731", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76SqVPjCr5S9iY10Qy1IeMcYRAD', created=1757479932, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\\n\\n[[ ## minute ## ]]\\n10\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\n\n[[ ## minute ## ]]\n10\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.010098750000000002, "timestamp": "2025-09-09T22:45:47.025919", "uuid": "7554fd11-cb2e-4611-81bb-0fab23211d1e", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7ARTkjo2Fn2hp7mDJTG0MdJjRl7', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- Oriented clock upright using the \u201c12\u201d at the top.\\n- Identified hands: shorter, thicker hour hand points slightly past 8; longer minute hand points to about 3 (15 minutes).\\n- With minute \u2248 15, the hour hand should be about 1/4 of the way from 8 toward 9, which matches.\\n- Last passed hour is 8.\\n\\n[[ ## hour ## ]]\\n8\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- Oriented clock upright using the \u201c12\u201d at the top.\n- Identified hands: shorter, thicker hour hand points slightly past 8; longer minute hand points to about 3 (15 minutes).\n- With minute \u2248 15, the hour hand should be about 1/4 of the way from 8 toward 9, which matches.\n- Last passed hour is 8.\n\n[[ ## hour ## ]]\n8\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.007957500000000001, "timestamp": "2025-09-09T22:45:47.058204", "uuid": "b7705712-111e-48dd-8540-80926cf35f1c", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7ARdyH04qBQhvBityisBdtWvQ3G', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- The \u201c12\u201d is at the top; the face is upright.\\n- The longer hand reaches the outer minute ring and points to the 9 position.\\n- The shorter hour hand is near the 4, consistent with about 3:45.\\n- 9 on the dial corresponds to 45 minutes.\\n\\n[[ ## minute ## ]]\\n45\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- The \u201c12\u201d is at the top; the face is upright.\n- The longer hand reaches the outer minute ring and points to the 9 position.\n- The shorter hour hand is near the 4, consistent with about 3:45.\n- 9 on the dial corresponds to 45 minutes.\n\n[[ ## minute ## ]]\n45\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.0326675, "timestamp": "2025-09-09T22:45:47.058716", "uuid": "69e71807-0b0a-40e6-8576-908dcac161a2", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE795hHSfyueLWRnKzAItXatIVYsk', created=1757480095, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; numerals upright.\\n- The longest hand points to 3 (15 minutes).\\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\\n- Therefore the last passed hour is 2.\\n\\n[[ ## hour ## ]]\\n2\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; numerals upright.\n- The longest hand points to 3 (15 minutes).\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\n- Therefore the last passed hour is 2.\n\n[[ ## hour ## ]]\n2\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009407500000000001, "timestamp": "2025-09-09T22:45:47.059488", "uuid": "abf20fa7-eac2-4df3-8c5f-943881c2e77e", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE795hHSfyueLWRnKzAItXatIVYsk', created=1757480095, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; numerals upright.\\n- The longest hand points to 3 (15 minutes).\\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\\n- Therefore the last passed hour is 2.\\n\\n[[ ## hour ## ]]\\n2\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; numerals upright.\n- The longest hand points to 3 (15 minutes).\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\n- Therefore the last passed hour is 2.\n\n[[ ## hour ## ]]\n2\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009407500000000001, "timestamp": "2025-09-09T22:45:47.059973", "uuid": "2a0e4b56-b85e-44ea-b3f5-d71c2d7e42cb", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7AR1QOoncOEPhyUNbjYH1NuMHLz', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; 3 at the right.\\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\\n\\n[[ ## minute ## ]]\\n16\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; 3 at the right.\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\n\n[[ ## minute ## ]]\n16\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.026247500000000003, "timestamp": "2025-09-09T22:45:47.059850", "uuid": "ef8932cf-8c71-479b-b5fa-34a3ac529231", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7AR1QOoncOEPhyUNbjYH1NuMHLz', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; 3 at the right.\\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\\n\\n[[ ## minute ## ]]\\n16\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; 3 at the right.\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\n\n[[ ## minute ## ]]\n16\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.026247500000000003, "timestamp": "2025-09-09T22:45:47.060335", "uuid": "97962b37-9d47-4fc6-a1a7-3fd9c726d496", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7AR1QOoncOEPhyUNbjYH1NuMHLz', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; 3 at the right.\\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\\n\\n[[ ## minute ## ]]\\n16\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; 3 at the right.\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\n\n[[ ## minute ## ]]\n16\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.026247500000000003, "timestamp": "2025-09-09T22:45:47.068704", "uuid": "4bc35192-d5c2-44c2-9f39-6cc2d2809806", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7ARdyH04qBQhvBityisBdtWvQ3G', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- The \u201c12\u201d is at the top; the face is upright.\\n- The longer hand reaches the outer minute ring and points to the 9 position.\\n- The shorter hour hand is near the 4, consistent with about 3:45.\\n- 9 on the dial corresponds to 45 minutes.\\n\\n[[ ## minute ## ]]\\n45\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- The \u201c12\u201d is at the top; the face is upright.\n- The longer hand reaches the outer minute ring and points to the 9 position.\n- The shorter hour hand is near the 4, consistent with about 3:45.\n- 9 on the dial corresponds to 45 minutes.\n\n[[ ## minute ## ]]\n45\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.0326675, "timestamp": "2025-09-09T22:45:47.070201", "uuid": "d1d68567-b930-49da-b7af-1a59b064e7c4", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7ARTkjo2Fn2hp7mDJTG0MdJjRl7', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- Oriented clock upright using the \u201c12\u201d at the top.\\n- Identified hands: shorter, thicker hour hand points slightly past 8; longer minute hand points to about 3 (15 minutes).\\n- With minute \u2248 15, the hour hand should be about 1/4 of the way from 8 toward 9, which matches.\\n- Last passed hour is 8.\\n\\n[[ ## hour ## ]]\\n8\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- Oriented clock upright using the \u201c12\u201d at the top.\n- Identified hands: shorter, thicker hour hand points slightly past 8; longer minute hand points to about 3 (15 minutes).\n- With minute \u2248 15, the hour hand should be about 1/4 of the way from 8 toward 9, which matches.\n- Last passed hour is 8.\n\n[[ ## hour ## ]]\n8\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.007957500000000001, "timestamp": "2025-09-09T22:45:47.070882", "uuid": "b8f0fb9f-0cc8-4c57-a69e-8c33526017a4", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE795hHSfyueLWRnKzAItXatIVYsk', created=1757480095, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; numerals upright.\\n- The longest hand points to 3 (15 minutes).\\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\\n- Therefore the last passed hour is 2.\\n\\n[[ ## hour ## ]]\\n2\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; numerals upright.\n- The longest hand points to 3 (15 minutes).\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\n- Therefore the last passed hour is 2.\n\n[[ ## hour ## ]]\n2\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009407500000000001, "timestamp": "2025-09-09T22:45:47.071958", "uuid": "1400a004-4d6b-40f2-8a76-e8a8a45d9883", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}], "reflection_lm": [{"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `current_instruction` (str): The current instruction that was provided to the assistant to perform the multimodal task\n2. `examples_with_feedback` (str): Task examples with visual content showing inputs, assistant outputs, and feedback. Pay special attention to feedback about visual analysis accuracy, visual-textual integration, and any domain-specific visual knowledge that the assistant missed.\nYour output fields are:\n1. `improved_instruction` (str): A better instruction for the assistant that addresses visual analysis issues, provides clear guidance on how to process and integrate visual and textual information, includes necessary visual domain knowledge, and prevents the visual analysis mistakes shown in the examples.\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_instruction ## ]]\n{current_instruction}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_instruction ## ]]\n{improved_instruction}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with instructions to perform a task involving visual content, but the assistant's performance needs improvement based on the examples and feedback below.\n \n Your task is to write a better instruction for the assistant that addresses the specific issues identified in the feedback, with particular attention to how visual and textual information should be analyzed and integrated.\n \n ## Analysis Steps:\n 1. **Read the inputs carefully** and identify both the visual and textual input formats, understanding how they work together\n 2. **Read all the assistant responses and corresponding feedback** to understand what went wrong with visual analysis, text processing, or their integration\n 3. **Identify visual analysis patterns** - what visual features, relationships, or details are important for this task\n 4. **Identify domain-specific knowledge** about both visual and textual aspects, as this information may not be available to the assistant in the future\n 5. **Look for successful visual-textual integration strategies** and include these patterns in the instruction\n 6. **Address specific visual analysis issues** mentioned in the feedback\n \n ## Instruction Requirements:\n - **Clear task definition** explaining how to process both visual and textual inputs\n - **Visual analysis guidance** specific to this task (what to look for, how to describe, what features matter)\n - **Integration strategies** for combining visual observations with textual information\n - **Domain-specific knowledge** about visual concepts, terminology, or relationships\n - **Error prevention guidance** for common visual analysis mistakes shown in the feedback\n - **Precise, actionable language** for both visual and textual processing\n \n Focus on creating an instruction that helps the assistant properly analyze visual content, integrate it with textual information, and avoid the specific visual analysis mistakes shown in the examples."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## current_instruction ## ]]\nGiven the fields `clock_photo`, produce the fields `hour`.\n\n[[ ## examples_with_feedback ## ]]\n[1] \u00ab\u00ab\u00ab\n The examples below include visual content (3 images total). Please analyze both the text and visual elements when suggesting improvements.\n \n # Example 1\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The hour hand is pointing at the 10 on the clock face, so the hour is 10.\n \n ### hour\n 10\n \n ## Feedback\n Target: 8:18, Predicted: 10:10\n \n \n \n # Example 2\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n \n ### hour\n 1\n \n ## Feedback\n Target: 4:16, Predicted: 1:15\n \n \n \n # Example 3\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n \n ### hour\n 1\n \n ## Feedback\n Target: 4:16, Predicted: 1:15\n \n \n\u00bb\u00bb\u00bb\n[2] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\u00bb\n[3] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\u00bb\n[4] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\u00bb\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_instruction ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76mfnk2mA8wJysppAeTXECkyOu8', created=1757479952, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## improved_instruction ## ]]\\nTask\\n- Input: clock_photo (an image of an analog clock)\\n- Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\\n\\nGoal\\n- Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\\n\\nStep-by-step procedure\\n1) Find the dial and pivot\\n- Locate the clock face and the central pivot where all hands originate.\\n- Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\\n\\n2) Determine the 12 o\u2019clock direction\\n- Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\\n- If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\\n\\n3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\\n- Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\\n- Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\\n- Hour hand: shortest, usually thicker, typically ends well inside the numerals.\\n- If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\\n\\n4) Measure positions (angles)\\n- Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\\n- Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\\n\\n5) Use minute-hand position to validate the hour-hand location\\n- The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\\n- Sanity check examples:\\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\\n - ~30 minutes: \u2248 halfway.\\n - ~45 minutes: \u2248 3/4 of the way.\\n- If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\\n\\n6) Determine the hour\\n- Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\\n- Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\\n- If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\\n\\n7) Edge cases and robustness\\n- Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\\n- Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\\n- Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\\n- Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\\n- Return 12, not 0, when appropriate (e.g., just after 12:00).\\n\\nOutput format\\n- Provide only: hour as an integer in [1,12], with no additional text.\\n\\nCommon error prevention (from prior mistakes)\\n- Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\\n- Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\\n- Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3).\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## improved_instruction ## ]]\nTask\n- Input: clock_photo (an image of an analog clock)\n- Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n\nGoal\n- Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n\nStep-by-step procedure\n1) Find the dial and pivot\n- Locate the clock face and the central pivot where all hands originate.\n- Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n\n2) Determine the 12 o\u2019clock direction\n- Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n- If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n\n3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n- Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n- Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n- Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n- If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n\n4) Measure positions (angles)\n- Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n- Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n\n5) Use minute-hand position to validate the hour-hand location\n- The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n- Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n- If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n\n6) Determine the hour\n- Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n- Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n- If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n\n7) Edge cases and robustness\n- Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n- Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n- Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n- Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n- Return 12, not 0, when appropriate (e.g., just after 12:00).\n\nOutput format\n- Provide only: hour as an integer in [1,12], with no additional text.\n\nCommon error prevention (from prior mistakes)\n- Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n- Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n- Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3).\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.04242250000000001, "timestamp": "2025-09-09T22:45:47.038613", "uuid": "91004ed4-e416-4b55-882a-75f98374c29a", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `current_instruction` (str): The current instruction that was provided to the assistant to perform the multimodal task\n2. `examples_with_feedback` (str): Task examples with visual content showing inputs, assistant outputs, and feedback. Pay special attention to feedback about visual analysis accuracy, visual-textual integration, and any domain-specific visual knowledge that the assistant missed.\nYour output fields are:\n1. `improved_instruction` (str): A better instruction for the assistant that addresses visual analysis issues, provides clear guidance on how to process and integrate visual and textual information, includes necessary visual domain knowledge, and prevents the visual analysis mistakes shown in the examples.\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_instruction ## ]]\n{current_instruction}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_instruction ## ]]\n{improved_instruction}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with instructions to perform a task involving visual content, but the assistant's performance needs improvement based on the examples and feedback below.\n \n Your task is to write a better instruction for the assistant that addresses the specific issues identified in the feedback, with particular attention to how visual and textual information should be analyzed and integrated.\n \n ## Analysis Steps:\n 1. **Read the inputs carefully** and identify both the visual and textual input formats, understanding how they work together\n 2. **Read all the assistant responses and corresponding feedback** to understand what went wrong with visual analysis, text processing, or their integration\n 3. **Identify visual analysis patterns** - what visual features, relationships, or details are important for this task\n 4. **Identify domain-specific knowledge** about both visual and textual aspects, as this information may not be available to the assistant in the future\n 5. **Look for successful visual-textual integration strategies** and include these patterns in the instruction\n 6. **Address specific visual analysis issues** mentioned in the feedback\n \n ## Instruction Requirements:\n - **Clear task definition** explaining how to process both visual and textual inputs\n - **Visual analysis guidance** specific to this task (what to look for, how to describe, what features matter)\n - **Integration strategies** for combining visual observations with textual information\n - **Domain-specific knowledge** about visual concepts, terminology, or relationships\n - **Error prevention guidance** for common visual analysis mistakes shown in the feedback\n - **Precise, actionable language** for both visual and textual processing\n \n Focus on creating an instruction that helps the assistant properly analyze visual content, integrate it with textual information, and avoid the specific visual analysis mistakes shown in the examples."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## current_instruction ## ]]\nGiven the fields `clock_photo`, produce the fields `minute`.\n\n[[ ## examples_with_feedback ## ]]\n[1] \u00ab\u00ab\u00ab\n The examples below include visual content (3 images total). Please analyze both the text and visual elements when suggesting improvements.\n \n # Example 1\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\n \n ### minute\n 10\n \n ## Feedback\n Target: 8:18, Predicted: 10:10\n \n \n \n # Example 2\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n \n ### minute\n 15\n \n ## Feedback\n Target: 4:16, Predicted: 1:15\n \n \n \n # Example 3\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n \n ### minute\n 15\n \n ## Feedback\n Target: 4:16, Predicted: 1:15\n \n \n\u00bb\u00bb\u00bb\n[2] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\u00bb\n[3] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\u00bb\n[4] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\u00bb\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_instruction ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE788elupjLcStrQ8q0wQ9hhf60qf', created=1757480036, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## improved_instruction ## ]]\\nTask: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\\n\\nWhat to analyze\\n- Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\\n- Hands identification (do not confuse them):\\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\\n - Hour hand: shorter and thicker, typically ends near the numerals.\\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\\n- Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\\n\\nHow to compute the minute\\n1. Locate the clock center and the minute hand\u2019s tip.\\n2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\\n3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\\n4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\\n5. Edge cases:\\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\\n - No minute ticks: Evenly interpolate between numerals.\\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\\n\\nOutput format\\n- Return only the integer minute value (0\u201359) in the minute field.\\n- If the angle computes to 60, output 0.\\n\\nError prevention reminders\\n- Do not treat the hour hand as the minute hand.\\n- Do not use the second hand to compute minutes.\\n- Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\\n- Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0).\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## improved_instruction ## ]]\nTask: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n\nWhat to analyze\n- Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n- Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n- Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n\nHow to compute the minute\n1. Locate the clock center and the minute hand\u2019s tip.\n2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n\nOutput format\n- Return only the integer minute value (0\u201359) in the minute field.\n- If the angle computes to 60, output 0.\n\nError prevention reminders\n- Do not treat the hour hand as the minute hand.\n- Do not use the second hand to compute minutes.\n- Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n- Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0).\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.03318125, "timestamp": "2025-09-09T22:45:47.041707", "uuid": "7f89fa6e-4f69-42e7-8c6a-f8600383ca38", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}]} \ No newline at end of file +{"lm": [{"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76SqVPjCr5S9iY10Qy1IeMcYRAD', created=1757479932, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\\n\\n[[ ## minute ## ]]\\n10\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\n\n[[ ## minute ## ]]\n10\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.010098750000000002, "timestamp": "2025-09-09T22:45:47.013627", "uuid": "af5f7d07-378a-4000-a8e9-2a0f0aa478b4", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76T11KnCZm6PV4gqeWiN0K0WXC9', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe hour hand is pointing at the 10 on the clock face, so the hour is 10.\\n\\n[[ ## hour ## ]]\\n10\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe hour hand is pointing at the 10 on the clock face, so the hour is 10.\n\n[[ ## hour ## ]]\n10\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009468750000000001, "timestamp": "2025-09-09T22:45:47.014155", "uuid": "bd54d56f-c1bc-4f58-b46b-10cea63c6a2b", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76Tzq07EuvUd6NqFPi5sDlZQ1fR', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\\n\\n[[ ## hour ## ]]\\n1\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n\n[[ ## hour ## ]]\n1\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.01118875, "timestamp": "2025-09-09T22:45:47.014524", "uuid": "da555aaa-5dec-4ac8-86db-c747116e2286", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76TPH60CaoQjw7UCWgM4sxc74FQ', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\\n\\n[[ ## minute ## ]]\\n15\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n\n[[ ## minute ## ]]\n15\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.00666875, "timestamp": "2025-09-09T22:45:47.014673", "uuid": "8da47789-6ebd-45e1-a2fb-cd998d8f6e3e", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76Tzq07EuvUd6NqFPi5sDlZQ1fR', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\\n\\n[[ ## hour ## ]]\\n1\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n\n[[ ## hour ## ]]\n1\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.01118875, "timestamp": "2025-09-09T22:45:47.022562", "uuid": "abd91c7e-c75e-481d-a8ff-6024714dfcb2", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76TPH60CaoQjw7UCWgM4sxc74FQ', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\\n\\n[[ ## minute ## ]]\\n15\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n\n[[ ## minute ## ]]\n15\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.00666875, "timestamp": "2025-09-09T22:45:47.022935", "uuid": "a15980e8-9929-4229-b7e3-ef5d394a15c8", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76Tzq07EuvUd6NqFPi5sDlZQ1fR', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\\n\\n[[ ## hour ## ]]\\n1\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n\n[[ ## hour ## ]]\n1\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.01118875, "timestamp": "2025-09-09T22:45:47.023794", "uuid": "dc0d82aa-15cb-444a-8a92-5081a34e48af", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76TPH60CaoQjw7UCWgM4sxc74FQ', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\\n\\n[[ ## minute ## ]]\\n15\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n\n[[ ## minute ## ]]\n15\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.00666875, "timestamp": "2025-09-09T22:45:47.024625", "uuid": "64706167-d1ad-41c6-919d-ad049a624b2d", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `hour`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76T11KnCZm6PV4gqeWiN0K0WXC9', created=1757479933, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe hour hand is pointing at the 10 on the clock face, so the hour is 10.\\n\\n[[ ## hour ## ]]\\n10\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe hour hand is pointing at the 10 on the clock face, so the hour is 10.\n\n[[ ## hour ## ]]\n10\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009468750000000001, "timestamp": "2025-09-09T22:45:47.025768", "uuid": "11397f7e-cfd9-43a0-a774-e34b74fc2731", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Given the fields `clock_photo`, produce the fields `reasoning`, `minute`."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76SqVPjCr5S9iY10Qy1IeMcYRAD', created=1757479932, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\nThe longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\\n\\n[[ ## minute ## ]]\\n10\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\nThe longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\n\n[[ ## minute ## ]]\n10\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.010098750000000002, "timestamp": "2025-09-09T22:45:47.025919", "uuid": "7554fd11-cb2e-4611-81bb-0fab23211d1e", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7ARTkjo2Fn2hp7mDJTG0MdJjRl7', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- Oriented clock upright using the \u201c12\u201d at the top.\\n- Identified hands: shorter, thicker hour hand points slightly past 8; longer minute hand points to about 3 (15 minutes).\\n- With minute \u2248 15, the hour hand should be about 1/4 of the way from 8 toward 9, which matches.\\n- Last passed hour is 8.\\n\\n[[ ## hour ## ]]\\n8\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- Oriented clock upright using the \u201c12\u201d at the top.\n- Identified hands: shorter, thicker hour hand points slightly past 8; longer minute hand points to about 3 (15 minutes).\n- With minute \u2248 15, the hour hand should be about 1/4 of the way from 8 toward 9, which matches.\n- Last passed hour is 8.\n\n[[ ## hour ## ]]\n8\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.007957500000000001, "timestamp": "2025-09-09T22:45:47.058204", "uuid": "b7705712-111e-48dd-8540-80926cf35f1c", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7ARdyH04qBQhvBityisBdtWvQ3G', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- The \u201c12\u201d is at the top; the face is upright.\\n- The longer hand reaches the outer minute ring and points to the 9 position.\\n- The shorter hour hand is near the 4, consistent with about 3:45.\\n- 9 on the dial corresponds to 45 minutes.\\n\\n[[ ## minute ## ]]\\n45\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- The \u201c12\u201d is at the top; the face is upright.\n- The longer hand reaches the outer minute ring and points to the 9 position.\n- The shorter hour hand is near the 4, consistent with about 3:45.\n- 9 on the dial corresponds to 45 minutes.\n\n[[ ## minute ## ]]\n45\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.0326675, "timestamp": "2025-09-09T22:45:47.058716", "uuid": "69e71807-0b0a-40e6-8576-908dcac161a2", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE795hHSfyueLWRnKzAItXatIVYsk', created=1757480095, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; numerals upright.\\n- The longest hand points to 3 (15 minutes).\\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\\n- Therefore the last passed hour is 2.\\n\\n[[ ## hour ## ]]\\n2\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; numerals upright.\n- The longest hand points to 3 (15 minutes).\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\n- Therefore the last passed hour is 2.\n\n[[ ## hour ## ]]\n2\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009407500000000001, "timestamp": "2025-09-09T22:45:47.059488", "uuid": "abf20fa7-eac2-4df3-8c5f-943881c2e77e", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE795hHSfyueLWRnKzAItXatIVYsk', created=1757480095, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; numerals upright.\\n- The longest hand points to 3 (15 minutes).\\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\\n- Therefore the last passed hour is 2.\\n\\n[[ ## hour ## ]]\\n2\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; numerals upright.\n- The longest hand points to 3 (15 minutes).\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\n- Therefore the last passed hour is 2.\n\n[[ ## hour ## ]]\n2\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009407500000000001, "timestamp": "2025-09-09T22:45:47.059973", "uuid": "2a0e4b56-b85e-44ea-b3f5-d71c2d7e42cb", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7AR1QOoncOEPhyUNbjYH1NuMHLz', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; 3 at the right.\\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\\n\\n[[ ## minute ## ]]\\n16\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; 3 at the right.\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\n\n[[ ## minute ## ]]\n16\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.026247500000000003, "timestamp": "2025-09-09T22:45:47.059850", "uuid": "ef8932cf-8c71-479b-b5fa-34a3ac529231", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7AR1QOoncOEPhyUNbjYH1NuMHLz', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; 3 at the right.\\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\\n\\n[[ ## minute ## ]]\\n16\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; 3 at the right.\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\n\n[[ ## minute ## ]]\n16\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.026247500000000003, "timestamp": "2025-09-09T22:45:47.060335", "uuid": "97962b37-9d47-4fc6-a1a7-3fd9c726d496", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7AR1QOoncOEPhyUNbjYH1NuMHLz', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; 3 at the right.\\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\\n\\n[[ ## minute ## ]]\\n16\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; 3 at the right.\n- The longest hand reaching the outer ring points slightly past the 3 \u2192 this is the minute hand.\n- The shorter, thicker hand sits between 3 and 4, a bit more than a quarter of the way toward 4, consistent with a little after 15 minutes.\n- Angle from 12 is just over 90\u00b0, giving about 16 minutes when rounded.\n\n[[ ## minute ## ]]\n16\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.026247500000000003, "timestamp": "2025-09-09T22:45:47.068704", "uuid": "4bc35192-d5c2-44c2-9f39-6cc2d2809806", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `minute` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## minute ## ]]\n{minute} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n \n What to analyze\n - Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n - Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n - Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n \n How to compute the minute\n 1. Locate the clock center and the minute hand\u2019s tip.\n 2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n 3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n 4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n 5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n \n Output format\n - Return only the integer minute value (0\u201359) in the minute field.\n - If the angle computes to 60, output 0.\n \n Error prevention reminders\n - Do not treat the hour hand as the minute hand.\n - Do not use the second hand to compute minutes.\n - Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n - Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## minute ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7ARdyH04qBQhvBityisBdtWvQ3G', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- The \u201c12\u201d is at the top; the face is upright.\\n- The longer hand reaches the outer minute ring and points to the 9 position.\\n- The shorter hour hand is near the 4, consistent with about 3:45.\\n- 9 on the dial corresponds to 45 minutes.\\n\\n[[ ## minute ## ]]\\n45\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- The \u201c12\u201d is at the top; the face is upright.\n- The longer hand reaches the outer minute ring and points to the 9 position.\n- The shorter hour hand is near the 4, consistent with about 3:45.\n- 9 on the dial corresponds to 45 minutes.\n\n[[ ## minute ## ]]\n45\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.0326675, "timestamp": "2025-09-09T22:45:47.070201", "uuid": "d1d68567-b930-49da-b7af-1a59b064e7c4", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE7ARTkjo2Fn2hp7mDJTG0MdJjRl7', created=1757480179, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- Oriented clock upright using the \u201c12\u201d at the top.\\n- Identified hands: shorter, thicker hour hand points slightly past 8; longer minute hand points to about 3 (15 minutes).\\n- With minute \u2248 15, the hour hand should be about 1/4 of the way from 8 toward 9, which matches.\\n- Last passed hour is 8.\\n\\n[[ ## hour ## ]]\\n8\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- Oriented clock upright using the \u201c12\u201d at the top.\n- Identified hands: shorter, thicker hour hand points slightly past 8; longer minute hand points to about 3 (15 minutes).\n- With minute \u2248 15, the hour hand should be about 1/4 of the way from 8 toward 9, which matches.\n- Last passed hour is 8.\n\n[[ ## hour ## ]]\n8\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.007957500000000001, "timestamp": "2025-09-09T22:45:47.070882", "uuid": "b8f0fb9f-0cc8-4c57-a69e-8c33526017a4", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `clock_photo` (Image):\nYour output fields are:\n1. `reasoning` (str): \n2. `hour` (int):\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## clock_photo ## ]]\n{clock_photo}\n\n[[ ## reasoning ## ]]\n{reasoning}\n\n[[ ## hour ## ]]\n{hour} # note: the value you produce must be a single int value\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n Task\n - Input: clock_photo (an image of an analog clock)\n - Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n \n Goal\n - Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n \n Step-by-step procedure\n 1) Find the dial and pivot\n - Locate the clock face and the central pivot where all hands originate.\n - Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n \n 2) Determine the 12 o\u2019clock direction\n - Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n - If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n \n 3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n - Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n - Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n - Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n - If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n \n 4) Measure positions (angles)\n - Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n - Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n \n 5) Use minute-hand position to validate the hour-hand location\n - The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n - Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n - If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n \n 6) Determine the hour\n - Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n - Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n - If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n \n 7) Edge cases and robustness\n - Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n - Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n - Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n - Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n - Return 12, not 0, when appropriate (e.g., just after 12:00).\n \n Output format\n - Provide only: hour as an integer in [1,12], with no additional text.\n \n Common error prevention (from prior mistakes)\n - Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n - Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n - Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## clock_photo ## ]]\n"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\n\nRespond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## hour ## ]]` (must be formatted as a valid Python int), and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE795hHSfyueLWRnKzAItXatIVYsk', created=1757480095, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## reasoning ## ]]\\n- 12 is at the top; numerals upright.\\n- The longest hand points to 3 (15 minutes).\\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\\n- Therefore the last passed hour is 2.\\n\\n[[ ## hour ## ]]\\n2\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## reasoning ## ]]\n- 12 is at the top; numerals upright.\n- The longest hand points to 3 (15 minutes).\n- The shortest/thick hand is just past 2, about a quarter of the way toward 3, consistent with 15 minutes past.\n- Therefore the last passed hour is 2.\n\n[[ ## hour ## ]]\n2\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.009407500000000001, "timestamp": "2025-09-09T22:45:47.071958", "uuid": "1400a004-4d6b-40f2-8a76-e8a8a45d9883", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}], "reflection_lm": [{"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `current_instruction` (str): The current instruction that was provided to the assistant to perform the multimodal task\n2. `examples_with_feedback` (str): Task examples with visual content showing inputs, assistant outputs, and feedback. Pay special attention to feedback about visual analysis accuracy, visual-textual integration, and any domain-specific visual knowledge that the assistant missed.\nYour output fields are:\n1. `improved_instruction` (str): A better instruction for the assistant that addresses visual analysis issues, provides clear guidance on how to process and integrate visual and textual information, includes necessary visual domain knowledge, and prevents the visual analysis mistakes shown in the examples.\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_instruction ## ]]\n{current_instruction}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_instruction ## ]]\n{improved_instruction}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with instructions to perform a task involving visual content, but the assistant's performance needs improvement based on the examples and feedback below.\n \n Your task is to write a better instruction for the assistant that addresses the specific issues identified in the feedback, with particular attention to how visual and textual information should be analyzed and integrated.\n \n ## Analysis Steps:\n 1. **Read the inputs carefully** and identify both the visual and textual input formats, understanding how they work together\n 2. **Read all the assistant responses and corresponding feedback** to understand what went wrong with visual analysis, text processing, or their integration\n 3. **Identify visual analysis patterns** - what visual features, relationships, or details are important for this task\n 4. **Identify domain-specific knowledge** about both visual and textual aspects, as this information may not be available to the assistant in the future\n 5. **Look for successful visual-textual integration strategies** and include these patterns in the instruction\n 6. **Address specific visual analysis issues** mentioned in the feedback\n \n ## Instruction Requirements:\n - **Clear task definition** explaining how to process both visual and textual inputs\n - **Visual analysis guidance** specific to this task (what to look for, how to describe, what features matter)\n - **Integration strategies** for combining visual observations with textual information\n - **Domain-specific knowledge** about visual concepts, terminology, or relationships\n - **Error prevention guidance** for common visual analysis mistakes shown in the feedback\n - **Precise, actionable language** for both visual and textual processing\n \n Focus on creating an instruction that helps the assistant properly analyze visual content, integrate it with textual information, and avoid the specific visual analysis mistakes shown in the examples."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## current_instruction ## ]]\nGiven the fields `clock_photo`, produce the fields `reasoning`, `hour`.\n\n[[ ## examples_with_feedback ## ]]\n[1] \u00ab\u00ab\u00ab\n The examples below include visual content (3 images total). Please analyze both the text and visual elements when suggesting improvements.\n \n # Example 1\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The hour hand is pointing at the 10 on the clock face, so the hour is 10.\n \n ### hour\n 10\n \n ## Feedback\n Target: 8:18, Predicted: 10:10\n \n \n \n # Example 2\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n \n ### hour\n 1\n \n ## Feedback\n Target: 4:16, Predicted: 1:15\n \n \n \n # Example 3\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The minute hand points at 3 (15 minutes), and the shorter hour hand is just past 1. Therefore, the hour is 1.\n \n ### hour\n 1\n \n ## Feedback\n Target: 4:16, Predicted: 1:15\n \n \n\u00bb\u00bb\u00bb\n[2] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\u00bb\n[3] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\u00bb\n[4] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\u00bb\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_instruction ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE76mfnk2mA8wJysppAeTXECkyOu8', created=1757479952, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## improved_instruction ## ]]\\nTask\\n- Input: clock_photo (an image of an analog clock)\\n- Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\\n\\nGoal\\n- Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\\n\\nStep-by-step procedure\\n1) Find the dial and pivot\\n- Locate the clock face and the central pivot where all hands originate.\\n- Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\\n\\n2) Determine the 12 o\u2019clock direction\\n- Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\\n- If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\\n\\n3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\\n- Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\\n- Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\\n- Hour hand: shortest, usually thicker, typically ends well inside the numerals.\\n- If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\\n\\n4) Measure positions (angles)\\n- Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\\n- Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\\n\\n5) Use minute-hand position to validate the hour-hand location\\n- The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\\n- Sanity check examples:\\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\\n - ~30 minutes: \u2248 halfway.\\n - ~45 minutes: \u2248 3/4 of the way.\\n- If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\\n\\n6) Determine the hour\\n- Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\\n- Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\\n- If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\\n\\n7) Edge cases and robustness\\n- Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\\n- Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\\n- Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\\n- Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\\n- Return 12, not 0, when appropriate (e.g., just after 12:00).\\n\\nOutput format\\n- Provide only: hour as an integer in [1,12], with no additional text.\\n\\nCommon error prevention (from prior mistakes)\\n- Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\\n- Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\\n- Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3).\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## improved_instruction ## ]]\nTask\n- Input: clock_photo (an image of an analog clock)\n- Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n\nGoal\n- Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n\nStep-by-step procedure\n1) Find the dial and pivot\n- Locate the clock face and the central pivot where all hands originate.\n- Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n\n2) Determine the 12 o\u2019clock direction\n- Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n- If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n\n3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n- Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n- Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n- Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n- If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n\n4) Measure positions (angles)\n- Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n- Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n\n5) Use minute-hand position to validate the hour-hand location\n- The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n- Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n- If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n\n6) Determine the hour\n- Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n- Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n- If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n\n7) Edge cases and robustness\n- Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n- Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n- Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n- Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n- Return 12, not 0, when appropriate (e.g., just after 12:00).\n\nOutput format\n- Provide only: hour as an integer in [1,12], with no additional text.\n\nCommon error prevention (from prior mistakes)\n- Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n- Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n- Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3).\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.04242250000000001, "timestamp": "2025-09-09T22:45:47.038613", "uuid": "91004ed4-e416-4b55-882a-75f98374c29a", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}, {"prompt": null, "messages": [{"role": "system", "content": "Your input fields are:\n1. `current_instruction` (str): The current instruction that was provided to the assistant to perform the multimodal task\n2. `examples_with_feedback` (str): Task examples with visual content showing inputs, assistant outputs, and feedback. Pay special attention to feedback about visual analysis accuracy, visual-textual integration, and any domain-specific visual knowledge that the assistant missed.\nYour output fields are:\n1. `improved_instruction` (str): A better instruction for the assistant that addresses visual analysis issues, provides clear guidance on how to process and integrate visual and textual information, includes necessary visual domain knowledge, and prevents the visual analysis mistakes shown in the examples.\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\n[[ ## current_instruction ## ]]\n{current_instruction}\n\n[[ ## examples_with_feedback ## ]]\n{examples_with_feedback}\n\n[[ ## improved_instruction ## ]]\n{improved_instruction}\n\n[[ ## completed ## ]]\nIn adhering to this structure, your objective is: \n I provided an assistant with instructions to perform a task involving visual content, but the assistant's performance needs improvement based on the examples and feedback below.\n \n Your task is to write a better instruction for the assistant that addresses the specific issues identified in the feedback, with particular attention to how visual and textual information should be analyzed and integrated.\n \n ## Analysis Steps:\n 1. **Read the inputs carefully** and identify both the visual and textual input formats, understanding how they work together\n 2. **Read all the assistant responses and corresponding feedback** to understand what went wrong with visual analysis, text processing, or their integration\n 3. **Identify visual analysis patterns** - what visual features, relationships, or details are important for this task\n 4. **Identify domain-specific knowledge** about both visual and textual aspects, as this information may not be available to the assistant in the future\n 5. **Look for successful visual-textual integration strategies** and include these patterns in the instruction\n 6. **Address specific visual analysis issues** mentioned in the feedback\n \n ## Instruction Requirements:\n - **Clear task definition** explaining how to process both visual and textual inputs\n - **Visual analysis guidance** specific to this task (what to look for, how to describe, what features matter)\n - **Integration strategies** for combining visual observations with textual information\n - **Domain-specific knowledge** about visual concepts, terminology, or relationships\n - **Error prevention guidance** for common visual analysis mistakes shown in the feedback\n - **Precise, actionable language** for both visual and textual processing\n \n Focus on creating an instruction that helps the assistant properly analyze visual content, integrate it with textual information, and avoid the specific visual analysis mistakes shown in the examples."}, {"role": "user", "content": [{"type": "text", "text": "[[ ## current_instruction ## ]]\nGiven the fields `clock_photo`, produce the fields `reasoning`, `minute`.\n\n[[ ## examples_with_feedback ## ]]\n[1] \u00ab\u00ab\u00ab\n The examples below include visual content (3 images total). Please analyze both the text and visual elements when suggesting improvements.\n \n # Example 1\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The longer minute hand points at the 2 on the dial, which corresponds to 10 minutes.\n \n ### minute\n 10\n \n ## Feedback\n Target: 8:18, Predicted: 10:10\n \n \n \n # Example 2\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n \n ### minute\n 15\n \n ## Feedback\n Target: 4:16, Predicted: 1:15\n \n \n \n # Example 3\n ## Inputs\n ### clock_photo\n [IMAGE-1 - see visual content]\n \n ## Generated Outputs\n ### reasoning\n The longer minute hand is pointing directly at the 3 on the clock face, which corresponds to 15 minutes past the hour.\n \n ### minute\n 15\n \n ## Feedback\n Target: 4:16, Predicted: 1:15\n \n \n\u00bb\u00bb\u00bb\n[2] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/cf/Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg/500px-Pendulum_clock_by_Jacob_Kock%2C_antique_furniture_photography%2C_IMG_0931_edit.jpg"}}, {"type": "text", "text": "\u00bb\n[3] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\u00bb\n[4] \u00ab"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Telechron_clock_2H07-Br_Administrator.JPG/960px-Telechron_clock_2H07-Br_Administrator.JPG"}}, {"type": "text", "text": "\u00bb\n\nRespond with the corresponding output fields, starting with the field `[[ ## improved_instruction ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`."}]}], "kwargs": {}, "response": "ModelResponse(id='chatcmpl-CE788elupjLcStrQ8q0wQ9hhf60qf', created=1757480036, model='gpt-5-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='[[ ## improved_instruction ## ]]\\nTask: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\\n\\nWhat to analyze\\n- Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\\n- Hands identification (do not confuse them):\\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\\n - Hour hand: shorter and thicker, typically ends near the numerals.\\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\\n- Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\\n\\nHow to compute the minute\\n1. Locate the clock center and the minute hand\u2019s tip.\\n2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\\n3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\\n4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\\n5. Edge cases:\\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\\n - No minute ticks: Evenly interpolate between numerals.\\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\\n\\nOutput format\\n- Return only the integer minute value (0\u201359) in the minute field.\\n- If the angle computes to 60, output 0.\\n\\nError prevention reminders\\n- Do not treat the hour hand as the minute hand.\\n- Do not use the second hand to compute minutes.\\n- Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\\n- Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0).\\n\\n[[ ## completed ## ]]', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage={}, service_tier='default', cache_hit=True)", "outputs": ["[[ ## improved_instruction ## ]]\nTask: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n\nWhat to analyze\n- Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n- Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n- Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n\nHow to compute the minute\n1. Locate the clock center and the minute hand\u2019s tip.\n2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n\nOutput format\n- Return only the integer minute value (0\u201359) in the minute field.\n- If the angle computes to 60, output 0.\n\nError prevention reminders\n- Do not treat the hour hand as the minute hand.\n- Do not use the second hand to compute minutes.\n- Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n- Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0).\n\n[[ ## completed ## ]]"], "usage": {}, "cost": 0.03318125, "timestamp": "2025-09-09T22:45:47.041707", "uuid": "7f89fa6e-4f69-42e7-8c6a-f8600383ca38", "model": "openai/gpt-5-2025-08-07", "response_model": "gpt-5-2025-08-07", "model_type": "chat"}]} \ No newline at end of file diff --git a/tests/teleprompt/test_gepa.py b/tests/teleprompt/test_gepa.py index b78e808947..7ca4b45b7b 100644 --- a/tests/teleprompt/test_gepa.py +++ b/tests/teleprompt/test_gepa.py @@ -90,8 +90,8 @@ def test_workflow_with_custom_instruction_proposer_and_component_selector(): class TimeReader(dspy.Module): def __init__(self): super().__init__() - self.hour_predictor = dspy.ChainOfThought("clock_photo: dspy.Image -> hour: int") - self.minute_predictor = dspy.ChainOfThought("clock_photo: dspy.Image -> minute: int") + self.hour_predictor = dspy.Predict("clock_photo: dspy.Image -> reasoning: str, hour: int") + self.minute_predictor = dspy.Predict("clock_photo: dspy.Image -> reasoning: str, minute: int") self.parallel = dspy.Parallel(num_threads=2) @@ -159,8 +159,8 @@ def all_component_selector(state, trajectories, subsample_scores, candidate_idx, ] o = optimizer.compile(student, trainset=trainset, valset=trainset) - assert o.hour_predictor.predict.signature.instructions == "Task\n- Input: clock_photo (an image of an analog clock)\n- Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n\nGoal\n- Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n\nStep-by-step procedure\n1) Find the dial and pivot\n- Locate the clock face and the central pivot where all hands originate.\n- Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n\n2) Determine the 12 o\u2019clock direction\n- Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n- If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n\n3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n- Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n- Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n- Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n- If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n\n4) Measure positions (angles)\n- Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n- Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n\n5) Use minute-hand position to validate the hour-hand location\n- The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n- Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n- If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n\n6) Determine the hour\n- Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n- Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n- If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n\n7) Edge cases and robustness\n- Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n- Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n- Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n- Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n- Return 12, not 0, when appropriate (e.g., just after 12:00).\n\nOutput format\n- Provide only: hour as an integer in [1,12], with no additional text.\n\nCommon error prevention (from prior mistakes)\n- Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n- Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n- Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)." - assert o.minute_predictor.predict.signature.instructions == "Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n\nWhat to analyze\n- Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n- Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n- Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n\nHow to compute the minute\n1. Locate the clock center and the minute hand\u2019s tip.\n2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n\nOutput format\n- Return only the integer minute value (0\u201359) in the minute field.\n- If the angle computes to 60, output 0.\n\nError prevention reminders\n- Do not treat the hour hand as the minute hand.\n- Do not use the second hand to compute minutes.\n- Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n- Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)." + assert o.hour_predictor.signature.instructions == "Task\n- Input: clock_photo (an image of an analog clock)\n- Output: hour (an integer 1\u201312). Output only the hour number with no extra text.\n\nGoal\n- Determine the correct hour by accurately identifying the hour hand and its position relative to the hour marks, taking into account the minute hand\u2019s position (since the hour hand moves continuously between numbers).\n\nStep-by-step procedure\n1) Find the dial and pivot\n- Locate the clock face and the central pivot where all hands originate.\n- Ignore decorative elements that do not originate at the central pivot (e.g., ornaments, shadows, reflections).\n\n2) Determine the 12 o\u2019clock direction\n- Prefer the numeral \u201c12\u201d if visible. Otherwise use the upright orientation of numerals or the topmost marker.\n- If the photo is rotated, mentally rotate so numerals read upright: 12 at top, 3 right, 6 bottom, 9 left.\n\n3) Identify the hands correctly (do not assume a default \u201c10:10\u201d)\n- Second hand: thinnest, often with a counterweight, may span very long; ignore for the hour.\n- Minute hand: longest, usually reaches or nearly reaches the outer minute tick marks.\n- Hour hand: shortest, usually thicker, typically ends well inside the numerals.\n- If ambiguous, classify by tip distance from center: minute \u2265 hour. Use the piece actually anchored at the pivot, not its shadow.\n\n4) Measure positions (angles)\n- Measure each hand\u2019s angle clockwise from 12 o\u2019clock.\n- Minute angle \u03b8m \u2248 position of the minute hand; hour angle \u03b8h \u2248 position of the hour hand.\n\n5) Use minute-hand position to validate the hour-hand location\n- The hour hand advances 0.5\u00b0 per minute (i.e., 1/12 of the distance between hour marks every 5 minutes).\n- Sanity check examples:\n - ~15 minutes past: hour hand \u2248 1/4 of the way from the current hour toward the next.\n - ~30 minutes: \u2248 halfway.\n - ~45 minutes: \u2248 3/4 of the way.\n- If this relationship doesn\u2019t hold, you likely swapped hour and minute hands\u2014re-identify them.\n\n6) Determine the hour\n- Compute the \u201clast passed\u201d hour: H = floor((\u03b8h mod 360) / 30). Map 0 to 12 (i.e., if floor(...) = 0, H = 12).\n- Do not round up to the next hour. The correct hour is the number the hour hand has most recently passed, not the one it is approaching.\n- If the hour hand appears exactly on an hour mark but the minute hand is not at 12, treat it as still between hours and choose the lower (last passed) hour.\n\n7) Edge cases and robustness\n- Stylized or missing numerals: rely on the 12/3/6/9 axes and tick marks rather than numeral shapes.\n- Roman numerals: \u201c4\u201d may be IIII; positions are unchanged.\n- Ignore mirrored effects, reflections, and shadows; only consider hands anchored at the pivot.\n- Overlap times: if hands nearly overlap, use \u03b8m to ensure the hour hand offset matches 0.5\u00b0 per minute.\n- Return 12, not 0, when appropriate (e.g., just after 12:00).\n\nOutput format\n- Provide only: hour as an integer in [1,12], with no additional text.\n\nCommon error prevention (from prior mistakes)\n- Do not confuse the minute hand for the hour hand; verify by length and reach to the outer tick marks.\n- Do not infer times like \u201c10:10\u201d by default; always read from the actual hand angles.\n- Ensure the hour chosen matches the \u201clast passed\u201d number given the minute hand\u2019s position (e.g., at ~:16, the hour hand must be just past the hour, not near 1 when the minute hand is at 3)." + assert o.minute_predictor.signature.instructions == "Task: From the image field clock_photo (an analog clock), output the minute value as an integer from 0\u201359 in the field minute. Output only the minute number\u2014no text or other fields.\n\nWhat to analyze\n- Clock face orientation: Identify where \u201c12\u201d is on the dial. Use the numerals (Arabic or Roman, stylized fonts) or the positions of 3, 6, 9, 12 to set the reference. If the photo is tilted, measure angles relative to the clock face, not the image frame.\n- Hands identification (do not confuse them):\n - Minute hand: typically the longest solid hand reaching near the minute ticks/outer ring; thicker than the second hand; often has a pronounced pointer tip.\n - Hour hand: shorter and thicker, typically ends near the numerals.\n - Second hand (if present): the thinnest, often the longest, usually with a counterweight; ignore it for minute reading.\n - If two non-second hands look similar, the one whose tip reaches closer to the minute tick ring is the minute hand.\n- Ticks and numerals: Each numeral-to-numeral segment equals 5 minutes. If minute tick marks exist, use them. If not, divide each numeral interval evenly into five.\n\nHow to compute the minute\n1. Locate the clock center and the minute hand\u2019s tip.\n2. Determine the angle of the minute hand from the 12 o\u2019clock direction, increasing clockwise.\n3. Convert angle to minutes: minute_estimate = (angle_from_12 / 6). Round to the nearest whole minute.\n - Mapping: 12 \u2192 0, 1 \u2192 5, 2 \u2192 10, 3 \u2192 15, 4 \u2192 20, 5 \u2192 25, 6 \u2192 30, 7 \u2192 35, 8 \u2192 40, 9 \u2192 45, 10 \u2192 50, 11 \u2192 55.\n - If the tip is slightly past a numeral (e.g., just past 3), do not snap to the numeral; round to the nearest minute (e.g., 16 instead of 15).\n4. Consistency check with the hour hand (useful to avoid off-by-one and hand mix-ups):\n - The hour hand moves continuously: it advances 0.5 degrees per minute (i.e., 1/12 of the way to the next numeral every 5 minutes).\n - If your minute_estimate is an exact multiple of 5 but the hour hand is clearly between hour markers (not aligned with an hour), re-examine: the minute hand is likely slightly past the numeral; adjust to the nearest minute accordingly.\n - If the minute hand choice is ambiguous, infer the minute from the hour hand\u2019s fraction toward the next hour: minute \u2248 fraction_between_hour_markers \u00d7 60, then choose the hand assignment that matches this.\n5. Edge cases:\n - Overlapping hands: Look at which tip extends farther toward the tick ring to identify the minute hand.\n - Strong perspective or glare: Use the line from center to the visible tip; ignore reflections.\n - No minute ticks: Evenly interpolate between numerals.\n - Subdials or decorative elements (e.g., pendulum windows) are not the minute indicator; use the main dial only.\n\nOutput format\n- Return only the integer minute value (0\u201359) in the minute field.\n- If the angle computes to 60, output 0.\n\nError prevention reminders\n- Do not treat the hour hand as the minute hand.\n- Do not use the second hand to compute minutes.\n- Do not assume the minute hand is exactly on a numeral\u2014check for slight offsets and round to the nearest minute.\n- Ensure the final minute agrees with the hour hand\u2019s position trend (hour hand slightly past an hour implies minutes > 0)." def test_metric_requires_feedback_signature(): From 3258da5eacede3c8de3fb08c385d479d29d6d2b0 Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Thu, 18 Sep 2025 17:05:04 -0700 Subject: [PATCH 07/12] increment --- dspy/adapters/types/base_type.py | 7 ++++++- dspy/adapters/types/reasoning.py | 9 +++++++++ tests/adapters/test_citation.py | 3 ++- tests/streaming/test_streaming.py | 1 + 4 files changed, 18 insertions(+), 2 deletions(-) diff --git a/dspy/adapters/types/base_type.py b/dspy/adapters/types/base_type.py index 04e2794ce7..93d047d917 100644 --- a/dspy/adapters/types/base_type.py +++ b/dspy/adapters/types/base_type.py @@ -70,6 +70,11 @@ def serialize_model(self): ) return formatted + @classmethod + def is_natively_supported(cls, lm, lm_kwargs) -> bool: + """Whether the custom type is natively supported by the LM.""" + return False + @classmethod def is_streamable(cls) -> bool: """Whether the custom type is streamable.""" @@ -88,7 +93,6 @@ def parse_stream_chunk(cls, chunk: ModelResponseStream) -> Optional["Type"]: """ return None - @classmethod def parse_lm_response(cls, response: str | dict[str, Any]) -> Optional["Type"]: """Parse a LM response into the custom type. @@ -101,6 +105,7 @@ def parse_lm_response(cls, response: str | dict[str, Any]) -> Optional["Type"]: """ return None + def split_message_content_for_custom_types(messages: list[dict[str, Any]]) -> list[dict[str, Any]]: """Split user message content into a list of content blocks. diff --git a/dspy/adapters/types/reasoning.py b/dspy/adapters/types/reasoning.py index 81d908f580..9bca128cd7 100644 --- a/dspy/adapters/types/reasoning.py +++ b/dspy/adapters/types/reasoning.py @@ -61,3 +61,12 @@ def parse_lm_response(cls, response: str | dict[str, Any]) -> Optional["Reasonin def __repr__(self) -> str: return f"{self.content!r}" + + def __str__(self) -> str: + return self.content + + def __eq__(self, other: object) -> bool: + if isinstance(other, Reasoning): + return self.content == other.content + if isinstance(other, str): + return self.content == other diff --git a/tests/adapters/test_citation.py b/tests/adapters/test_citation.py index d1fdc8d084..f39c14db8a 100644 --- a/tests/adapters/test_citation.py +++ b/tests/adapters/test_citation.py @@ -155,7 +155,8 @@ class CitationSignature(Signature): CitationSignature.delete("citations"), CitationSignature, outputs, - dspy.LM(model="claude-3-5-sonnet-20241022") + dspy.LM(model="claude-3-5-sonnet-20241022"), + lm_kwargs={}, ) assert len(result) == 1 diff --git a/tests/streaming/test_streaming.py b/tests/streaming/test_streaming.py index b7afa7dc46..42a03f63f5 100644 --- a/tests/streaming/test_streaming.py +++ b/tests/streaming/test_streaming.py @@ -907,6 +907,7 @@ class CustomSignature(dspy.Signature): ) async def stream(*args, **kwargs): + yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="[[ ## answer ## ]]\n"))]) yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="Hello"))]) yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="World"))]) yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="\n\n"))]) From 8de0a6536e8d638752f49cb7a9408bd7252709f4 Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Thu, 18 Sep 2025 17:08:24 -0700 Subject: [PATCH 08/12] go --- tests/adapters/test_citation.py | 2 +- tests/streaming/test_streaming.py | 60 +++++++++++++++++++++---------- 2 files changed, 43 insertions(+), 19 deletions(-) diff --git a/tests/adapters/test_citation.py b/tests/adapters/test_citation.py index f39c14db8a..3c2a7f75e6 100644 --- a/tests/adapters/test_citation.py +++ b/tests/adapters/test_citation.py @@ -155,7 +155,7 @@ class CitationSignature(Signature): CitationSignature.delete("citations"), CitationSignature, outputs, - dspy.LM(model="claude-3-5-sonnet-20241022"), + dspy.LM(model="anthropic/claude-3-5-sonnet-20241022"), lm_kwargs={}, ) diff --git a/tests/streaming/test_streaming.py b/tests/streaming/test_streaming.py index 42a03f63f5..f62bc8f44f 100644 --- a/tests/streaming/test_streaming.py +++ b/tests/streaming/test_streaming.py @@ -895,6 +895,10 @@ def parse_stream_chunk(cls, chunk): def parse_lm_response(cls, response: dict) -> "CustomType": return CustomType(message=response.split("\n\n")[0]) + @classmethod + def is_natively_supported(cls, lm, lm_kwargs): + return True + class CustomSignature(dspy.Signature): question: str = dspy.InputField() answer: CustomType = dspy.OutputField() @@ -907,7 +911,6 @@ class CustomSignature(dspy.Signature): ) async def stream(*args, **kwargs): - yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="[[ ## answer ## ]]\n"))]) yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="Hello"))]) yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="World"))]) yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="\n\n"))]) @@ -916,9 +919,10 @@ async def stream(*args, **kwargs): yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content=" ##"))]) yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content=" ]]"))]) - with mock.patch("litellm.acompletion", side_effect=stream): - with dspy.context(lm=dspy.LM("openai/gpt-4o-mini", cache=False), adapter=dspy.ChatAdapter(native_response_types=[CustomType])): + with dspy.context( + lm=dspy.LM("openai/gpt-4o-mini", cache=False), adapter=dspy.ChatAdapter(native_response_types=[CustomType]) + ): output = program(question="why did a chicken cross the kitchen?") all_chunks = [] async for value in output: @@ -935,6 +939,7 @@ async def stream(*args, **kwargs): async def test_streaming_with_citations(): class AnswerWithSources(dspy.Signature): """Answer questions using provided documents with citations.""" + documents: list[Document] = dspy.InputField() question: str = dspy.InputField() answer: str = dspy.OutputField() @@ -959,20 +964,36 @@ async def citation_stream(*args, **kwargs): yield ModelResponseStream(model="claude", choices=[StreamingChoices(delta=Delta(content=" 100°C"))]) yield ModelResponseStream(model="claude", choices=[StreamingChoices(delta=Delta(content="."))]) yield ModelResponseStream(model="claude", choices=[StreamingChoices(delta=Delta(content="\n\n"))]) - yield ModelResponseStream(model="claude", choices=[StreamingChoices(delta=Delta(content='[{"type": "char_location", "cited_text": "Water boils at 100°C", "document_index": 0, "document_title": "Physics Facts", "start_char_index": 0, "end_char_index": 19}]'))]) - yield ModelResponseStream(model="claude", choices=[StreamingChoices(delta=Delta( - content="", - provider_specific_fields={ - "citation": { - "type": "char_location", - "cited_text": "Water boils at 100°C", - "document_index": 0, - "document_title": "Physics Facts", - "start_char_index": 0, - "end_char_index": 19 - } - } - ))]) + yield ModelResponseStream( + model="claude", + choices=[ + StreamingChoices( + delta=Delta( + content='[{"type": "char_location", "cited_text": "Water boils at 100°C", "document_index": 0, "document_title": "Physics Facts", "start_char_index": 0, "end_char_index": 19}]' + ) + ) + ], + ) + yield ModelResponseStream( + model="claude", + choices=[ + StreamingChoices( + delta=Delta( + content="", + provider_specific_fields={ + "citation": { + "type": "char_location", + "cited_text": "Water boils at 100°C", + "document_index": 0, + "document_title": "Physics Facts", + "start_char_index": 0, + "end_char_index": 19, + } + }, + ) + ) + ], + ) yield ModelResponseStream(model="claude", choices=[StreamingChoices(delta=Delta(content="\n\n"))]) yield ModelResponseStream(model="claude", choices=[StreamingChoices(delta=Delta(content="[[ ##"))]) yield ModelResponseStream(model="claude", choices=[StreamingChoices(delta=Delta(content=" completed"))]) @@ -990,7 +1011,10 @@ async def citation_stream(*args, **kwargs): # Create test documents docs = [Document(data="Water boils at 100°C at standard pressure.", title="Physics Facts")] - with dspy.context(lm=dspy.LM("anthropic/claude-3-5-sonnet-20241022", cache=False), adapter=dspy.ChatAdapter(native_response_types=[Citations])): + with dspy.context( + lm=dspy.LM("anthropic/claude-3-5-sonnet-20241022", cache=False), + adapter=dspy.ChatAdapter(native_response_types=[Citations]), + ): output = program(documents=docs, question="What temperature does water boil?") citation_chunks = [] final_prediction = None From ec2fbe47630188bdf19f9591e8207ad9c6135b92 Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Thu, 18 Sep 2025 17:23:10 -0700 Subject: [PATCH 09/12] polish the docstring --- dspy/adapters/types/reasoning.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/dspy/adapters/types/reasoning.py b/dspy/adapters/types/reasoning.py index 9bca128cd7..429f493eb5 100644 --- a/dspy/adapters/types/reasoning.py +++ b/dspy/adapters/types/reasoning.py @@ -11,6 +11,9 @@ class Reasoning(Type): This type is useful when you want the DSPy output to include the reasoning of the LM. We build this type so that DSPy can support the reasoning model and non-reasoning model with the same code. + + This is a str-like type, you can convert a string directly to a Reasoning object, and from DSPy adapters' + perspective, `Reasoning` is treated as a string. """ content: str From 56973f08279a377d3848afe5c98c59384611ba74 Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Thu, 18 Sep 2025 21:38:33 -0700 Subject: [PATCH 10/12] automatically turn on reasoning for COT on reasoning model --- dspy/adapters/base.py | 6 +++--- dspy/adapters/types/base_type.py | 4 ++-- dspy/adapters/types/citation.py | 3 +-- dspy/adapters/types/reasoning.py | 9 ++++++--- tests/predict/test_chain_of_thought.py | 7 ++++++- tests/streaming/test_streaming.py | 2 +- 6 files changed, 19 insertions(+), 12 deletions(-) diff --git a/dspy/adapters/base.py b/dspy/adapters/base.py index f7a3d7a3bb..f4d310e854 100644 --- a/dspy/adapters/base.py +++ b/dspy/adapters/base.py @@ -73,13 +73,13 @@ def _call_preprocess( return signature_for_native_function_calling - # Handle custom types that use native response + # Handle custom types that use native LM features, e.g., reasoning, citations, etc. for name, field in signature.output_fields.items(): if ( isinstance(field.annotation, type) and issubclass(field.annotation, Type) and field.annotation in self.native_response_types - and field.annotation.is_natively_supported(lm, lm_kwargs) + and field.annotation.adapt_to_native_lm_feature(lm, lm_kwargs) ): signature = signature.delete(name) @@ -134,7 +134,7 @@ def _call_postprocess( isinstance(field.annotation, type) and issubclass(field.annotation, Type) and field.annotation in self.native_response_types - and field.annotation.is_natively_supported(lm, lm_kwargs) + and field.annotation.adapt_to_native_lm_feature(lm, lm_kwargs) ): value[name] = field.annotation.parse_lm_response(output) diff --git a/dspy/adapters/types/base_type.py b/dspy/adapters/types/base_type.py index 93d047d917..b1c8cca95f 100644 --- a/dspy/adapters/types/base_type.py +++ b/dspy/adapters/types/base_type.py @@ -71,8 +71,8 @@ def serialize_model(self): return formatted @classmethod - def is_natively_supported(cls, lm, lm_kwargs) -> bool: - """Whether the custom type is natively supported by the LM.""" + def adapt_to_native_lm_feature(cls, lm, lm_kwargs) -> bool: + """Check whether the custom type is natively supported by the LM and adapt to the native feature if possible.""" return False @classmethod diff --git a/dspy/adapters/types/citation.py b/dspy/adapters/types/citation.py index a9e2896d4d..e471c7ccb5 100644 --- a/dspy/adapters/types/citation.py +++ b/dspy/adapters/types/citation.py @@ -166,8 +166,7 @@ def __getitem__(self, index): return self.citations[index] @classmethod - def is_natively_supported(cls, lm, lm_kwargs) -> bool: - """Whether the Citations type is natively supported by the LM.""" + def adapt_to_native_lm_feature(cls, lm, lm_kwargs) -> bool: return lm.model.startswith("anthropic/") @classmethod diff --git a/dspy/adapters/types/reasoning.py b/dspy/adapters/types/reasoning.py index 429f493eb5..be91cdde9a 100644 --- a/dspy/adapters/types/reasoning.py +++ b/dspy/adapters/types/reasoning.py @@ -40,8 +40,7 @@ def validate_input(cls, data: Any): raise ValueError(f"Received invalid value for `dspy.Reasoning`: {data}") @classmethod - def is_natively_supported(cls, lm, lm_kwargs) -> bool: - """Whether the Reasoning type is natively supported by the LM.""" + def adapt_to_native_lm_feature(cls, lm, lm_kwargs) -> bool: if not litellm.supports_reasoning(lm.model): return False @@ -53,7 +52,11 @@ def is_natively_supported(cls, lm, lm_kwargs) -> bool: else: reasoning_effort = None - return reasoning_effort is not None + if reasoning_effort is None: + # Turn on the native reasoning + lm_kwargs["reasoning_effort"] = "low" + + return True @classmethod def parse_lm_response(cls, response: str | dict[str, Any]) -> Optional["Reasoning"]: diff --git a/tests/predict/test_chain_of_thought.py b/tests/predict/test_chain_of_thought.py index 4abbf72bce..1730dc5eb9 100644 --- a/tests/predict/test_chain_of_thought.py +++ b/tests/predict/test_chain_of_thought.py @@ -31,7 +31,7 @@ async def test_async_chain_of_thought(): def test_chain_of_thought_with_native_reasoning(): """Test ChainOfThought with native reasoning support where LM returns reasoning natively.""" - lm = dspy.LM(model="anthropic/claude-3-7-sonnet-20250219", reasoning_effort="low", cache=False) + lm = dspy.LM(model="anthropic/claude-3-7-sonnet-20250219", cache=False) dspy.settings.configure(lm=lm) with mock.patch("litellm.completion") as mock_completion: @@ -53,6 +53,11 @@ def test_chain_of_thought_with_native_reasoning(): assert isinstance(result.reasoning, dspy.Reasoning) assert result.reasoning.content == "Step-by-step thinking about the capital of France" + # Check that the reasoning_effort is automatically set to "low" when the LM supports native reasoning and not + # provided in the LM kwargs + args, kwargs = mock_completion.call_args + assert kwargs["reasoning_effort"] == "low" + def test_chain_of_thought_with_manual_reasoning(): """Test ChainOfThought with manual reasoning where LM doesn't support native reasoning.""" diff --git a/tests/streaming/test_streaming.py b/tests/streaming/test_streaming.py index f62bc8f44f..71bf934c5d 100644 --- a/tests/streaming/test_streaming.py +++ b/tests/streaming/test_streaming.py @@ -896,7 +896,7 @@ def parse_lm_response(cls, response: dict) -> "CustomType": return CustomType(message=response.split("\n\n")[0]) @classmethod - def is_natively_supported(cls, lm, lm_kwargs): + def adapt_to_native_lm_feature(cls, lm, lm_kwargs): return True class CustomSignature(dspy.Signature): From c65b774d63c6b8dfa11e8ef5a5bb25f5f38393a7 Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Mon, 22 Sep 2025 13:09:40 -0700 Subject: [PATCH 11/12] comments --- dspy/adapters/types/reasoning.py | 9 +++++---- dspy/clients/lm.py | 13 ++++--------- dspy/predict/chain_of_thought.py | 6 ++++++ tests/clients/test_lm.py | 24 ++++++++---------------- 4 files changed, 23 insertions(+), 29 deletions(-) diff --git a/dspy/adapters/types/reasoning.py b/dspy/adapters/types/reasoning.py index be91cdde9a..00606d3ace 100644 --- a/dspy/adapters/types/reasoning.py +++ b/dspy/adapters/types/reasoning.py @@ -44,18 +44,19 @@ def adapt_to_native_lm_feature(cls, lm, lm_kwargs) -> bool: if not litellm.supports_reasoning(lm.model): return False + reasoning_effort = "unspecified" if "reasoning_effort" in lm_kwargs: # `lm_kwargs` overrides `lm.kwargs` reasoning_effort = lm_kwargs["reasoning_effort"] elif "reasoning_effort" in lm.kwargs: reasoning_effort = lm.kwargs["reasoning_effort"] - else: - reasoning_effort = None if reasoning_effort is None: - # Turn on the native reasoning - lm_kwargs["reasoning_effort"] = "low" + # If users explicitly set `reasoning_effort` to None, we don't enable native reasoning + return False + # Turn on the native reasoning + lm_kwargs["reasoning_effort"] = "low" return True @classmethod diff --git a/dspy/clients/lm.py b/dspy/clients/lm.py index 097f2da1ee..bbf67cee79 100644 --- a/dspy/clients/lm.py +++ b/dspy/clients/lm.py @@ -103,15 +103,6 @@ def __init__( self._warn_zero_temp_rollout(self.kwargs.get("temperature"), self.kwargs.get("rollout_id")) - # Normalize reasoning_effort to get reasoning summaries (for OpenAI reasoning models which don't expose - # reasoning content) - if "reasoning_effort" in self.kwargs and ( - self.model_type == "responses" - or ("openai/" in self.model.lower() and litellm.supports_reasoning(self.model)) - ): - effort = self.kwargs.pop("reasoning_effort") - self.kwargs["reasoning"] = {"effort": effort, "summary": "auto"} - def _warn_zero_temp_rollout(self, temperature: float | None, rollout_id): if not self._warned_zero_temp_rollout and rollout_id is not None and (temperature is None or temperature == 0): warnings.warn( @@ -463,4 +454,8 @@ def _convert_chat_request_to_responses_request(request: dict[str, Any]): elif isinstance(c, list): content_blocks.extend(c) request["input"] = [{"role": msg.get("role", "user"), "content": content_blocks}] + # Convert reasoning_effort to reasoning format supported by the Responses API + if "reasoning_effort" in request: + effort = request.pop("reasoning_effort") + request["reasoning"] = {"effort": effort, "summary": "auto"} return request diff --git a/dspy/predict/chain_of_thought.py b/dspy/predict/chain_of_thought.py index 2c66682213..189c5c9142 100644 --- a/dspy/predict/chain_of_thought.py +++ b/dspy/predict/chain_of_thought.py @@ -1,3 +1,4 @@ +import logging from typing import Any import dspy @@ -5,6 +6,8 @@ from dspy.signatures.field import OutputField from dspy.signatures.signature import Signature, ensure_signature +logger = logging.getLogger(__name__) + class ChainOfThought(Module): def __init__( @@ -22,6 +25,9 @@ def __init__( super().__init__() signature = ensure_signature(signature) + if "rationale_field" in config or "rationale_field_type" in config: + logger.warning("`rationale_field` and `rationale_field_type` are deprecated, they are no-op now.") + from dspy.adapters.types.reasoning import Reasoning extended_signature = signature.prepend(name="reasoning", field=OutputField(), type_=Reasoning) diff --git a/tests/clients/test_lm.py b/tests/clients/test_lm.py index 97333996aa..c62da512fa 100644 --- a/tests/clients/test_lm.py +++ b/tests/clients/test_lm.py @@ -565,25 +565,17 @@ def test_responses_api_tool_calls(litellm_test_server): assert dspy_responses.call_args.kwargs["model"] == "openai/dspy-test-model" -def test_reasoning_effort_normalization(): - """Test that reasoning_effort gets normalized to reasoning format for OpenAI models.""" - with mock.patch("litellm.supports_reasoning", return_value=True): +def test_reasoning_effort_responses_api(): + """Test that reasoning_effort gets normalized to reasoning format for Responses API.""" + with mock.patch("litellm.responses") as mock_responses: # OpenAI model with Responses API - should normalize - lm1 = dspy.LM( + lm = dspy.LM( model="openai/gpt-5", model_type="responses", reasoning_effort="low", max_tokens=16000, temperature=1.0 ) - assert "reasoning_effort" not in lm1.kwargs - assert lm1.kwargs["reasoning"] == {"effort": "low", "summary": "auto"} - - # OpenAI model with Chat API - should normalize - lm2 = dspy.LM(model="openai/gpt-5", reasoning_effort="medium", max_tokens=16000, temperature=1.0) - assert "reasoning_effort" not in lm2.kwargs - assert lm2.kwargs["reasoning"] == {"effort": "medium", "summary": "auto"} - - # Non-OpenAI model - should NOT normalize - lm3 = dspy.LM(model="deepseek-ai/DeepSeek-R1", reasoning_effort="low", max_tokens=4000, temperature=0.7) - assert "reasoning_effort" in lm3.kwargs - assert "reasoning" not in lm3.kwargs + lm("openai query") + call_kwargs = mock_responses.call_args.kwargs + assert "reasoning_effort" not in call_kwargs + assert call_kwargs["reasoning"] == {"effort": "low", "summary": "auto"} def test_reasoning_content_extraction(): From 8c1630c0bfa40d3e0e685efbe99e5abd3496114c Mon Sep 17 00:00:00 2001 From: chenmoneygithub Date: Tue, 23 Sep 2025 10:56:21 -0700 Subject: [PATCH 12/12] fix tests --- tests/clients/test_lm.py | 87 +++------------------------------------- 1 file changed, 5 insertions(+), 82 deletions(-) diff --git a/tests/clients/test_lm.py b/tests/clients/test_lm.py index c62da512fa..9570e4c34b 100644 --- a/tests/clients/test_lm.py +++ b/tests/clients/test_lm.py @@ -2,7 +2,7 @@ import time import warnings from unittest import mock -from unittest.mock import Mock, patch +from unittest.mock import patch import litellm import pydantic @@ -578,20 +578,6 @@ def test_reasoning_effort_responses_api(): assert call_kwargs["reasoning"] == {"effort": "low", "summary": "auto"} -def test_reasoning_content_extraction(): - """Test that reasoning models can be created with proper configuration.""" - # Test that reasoning models are properly configured - lm = dspy.LM( - model="openai/gpt-5", model_type="responses", max_tokens=16000, temperature=1.0, reasoning_effort="low" - ) - - # Verify reasoning parameters are normalized - assert "reasoning" in lm.kwargs - assert lm.kwargs["reasoning"]["effort"] == "low" - assert "max_completion_tokens" in lm.kwargs - assert lm.kwargs["max_completion_tokens"] == 16000 - - def test_call_reasoning_model_with_chat_api(): """Test that Chat API properly handles reasoning models and returns data in correct format.""" # Create message with reasoning_content attribute @@ -605,7 +591,7 @@ def test_call_reasoning_model_with_chat_api(): # Mock response with reasoning content for chat completion mock_response = ModelResponse( choices=[mock_choice], - model="openai/gpt-5", + model="anthropic/claude-3-7-sonnet-20250219", usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, ) @@ -613,7 +599,7 @@ def test_call_reasoning_model_with_chat_api(): with mock.patch("litellm.supports_reasoning", return_value=True): # Create reasoning model with chat API lm = dspy.LM( - model="openai/gpt-5", + model="anthropic/claude-3-7-sonnet-20250219", model_type="chat", temperature=1.0, max_tokens=16000, @@ -636,68 +622,5 @@ def test_call_reasoning_model_with_chat_api(): # Verify mock was called with correct parameters mock_completion.assert_called_once() call_kwargs = mock_completion.call_args.kwargs - assert call_kwargs["model"] == "openai/gpt-5" - assert "reasoning" in call_kwargs - assert call_kwargs["reasoning"]["effort"] == "low" - assert call_kwargs["reasoning"]["summary"] == "auto" - assert "reasoning_effort" not in call_kwargs # Should be normalized - - -def test_call_reasoning_model_with_responses_api(): - """Test that Responses API properly handles reasoning models and returns data in correct format.""" - # Create mock content item for message - content_item = Mock() - content_item.text = "The answer is 4" - - # Create mock message output item - message_item = Mock() - message_item.type = "message" - message_item.content = [content_item] - - # Create mock reasoning content item - reasoning_content_item = Mock() - reasoning_content_item.text = "Step 1: I need to add 2 + 2\nStep 2: 2 + 2 = 4\nTherefore, the answer is 4" - - # Create mock reasoning output item - reasoning_item = Mock() - reasoning_item.type = "reasoning" - reasoning_item.content = [reasoning_content_item] - - # Create mock response - mock_response = Mock() - mock_response.output = [message_item, reasoning_item] - mock_response.usage = {"input_tokens": 10, "output_tokens": 20, "total_tokens": 30} - mock_response.model = "openai/gpt-5" - - with mock.patch("litellm.responses", return_value=mock_response) as mock_responses: - with mock.patch("litellm.supports_reasoning", return_value=True): - # Create reasoning model with responses API - lm = dspy.LM( - model="openai/gpt-5", - model_type="responses", - temperature=1.0, - max_tokens=16000, - reasoning_effort="medium", - cache=False, - ) - - # Test the call - result = lm("What is 2 + 2?") - - # Verify the response format - assert isinstance(result, list) - assert len(result) == 1 - assert isinstance(result[0], dict) - assert "text" in result[0] - assert "reasoning_content" in result[0] - assert result[0]["text"] == "The answer is 4" - assert "Step 1" in result[0]["reasoning_content"] - - # Verify mock was called with correct parameters - mock_responses.assert_called_once() - call_kwargs = mock_responses.call_args.kwargs - assert call_kwargs["model"] == "openai/gpt-5" - assert "reasoning" in call_kwargs - assert call_kwargs["reasoning"]["effort"] == "medium" - assert call_kwargs["reasoning"]["summary"] == "auto" - assert "reasoning_effort" not in call_kwargs # Should be normalized + assert call_kwargs["model"] == "anthropic/claude-3-7-sonnet-20250219" + assert call_kwargs["reasoning_effort"] == "low"