Chronos

Chronos is a baseline framework for question answering under continuous knowledge drift. It provides a unified pipeline for evaluating how large language models (LLMs) adapt to evolving, time-dependent knowledge using structured retrieval and reasoning.

Project Structure

chronos.py          # Main Chronos pipeline
baselines/          # Baseline implementations
├── direct.py     # Direct generation baseline
├── rag.py        # Retrieval-Augmented Generation baseline
└── react.py      # ReAct-style reasoning baseline
data/               # Dataset directory (must be prepared before running)
llm_api.py          # Unified LLM / embedding API wrapper (OpenAI / Azure / Claude / DeepInfra)
utils.py            # Utility functions (EM evaluation, time parsing, quad serialization)

Requirements

Python 3.10+ is recommended.

Install dependencies with:

pip install openai anthropic langchain-core langchain-huggingface sentence-transformers tqdm

Before running the code, select the desired api_source in llm_api.py and place the corresponding API key file in the project root directory.

Running Experiments

Run Chronos

python chronos.py

Run Baselines

python baselines/direct.py
python baselines/rag.py
python baselines/react.py

Outputs and Evaluation

All experiment outputs are written to:

outputs/{method}/{model_id}/{task}/record_*.json

Each script automatically reports Exact Match (EM) accuracy for each task upon completion.