v0.0.2

• README GIF: scripts/find_flip_example.py finds real items where raw readout flips under option reversal and L0 does not; scripts/make_flip_gif.py renders them (assets/flip.gif, Qwen3-8B on BANKING77). space/ holds the Hugging Face Space entry point.

• Decider.save_artifacts / load_artifacts: L1 artifacts as one JSON file per model, refused on model mismatch.

• ROADMAP.md, CONTRIBUTING.md, CREDITS.md, GitHub Actions CI (ruff + pytest on 3.10 and 3.12).

• Core: Question (choice / score / noul), Decider, Decision with mandatory level.

• Readout: chat-template prompts, single-token label mapping with collision and multi-token errors.

• L0: cyclic-shift permutation marginalization (log-space combine by default) and label-free prior correction: batch calibration by default (low variance in the bench), content-free contextual calibration opt-in (high variance: large wins on one noul task, losses on ordinal scores).

• Readout: noul label tokens stay bound to their option when the phrasing order is swapped (a position-bound readout silently swapped Yes/No in the second phrasing).

• L1: temperature scaling artifacts keyed by (model, question).

• HFBackend accepts revision.

• vLLM backend parity check (scripts/vllm_parity.py): 15/15 argmax agreement with transformers on Qwen2.5-7B-Instruct; restricted log-softmax differs by up to 0.6 nats only on labels below 1e-7 probability.

• Backends: transformers (HFBackend), vLLM OpenAI-compatible server (VLLMBackend, allowed_token_ids + logprobs), synthetic biased model (FakeBackend) for tests.

• Question.score accepts explicit ordered levels; value is the expected level index.

• Bench: bench.providers.nanojev_maze runs AnyJev as the engine inside NanoJev's frozen maze exploration harness; bench.providers.nanojev_native_maze reruns their untuned-Qwen baseline for an apples-to-apples row.

• Bench: bench.run_typed runs LocalLLaMA/typed-decisions (the set Laya and Jev report on) with overall / per-workflow / per-type metrics; bench.providers.laya runs Laya checkpoints on the same decisions.

• Bench: bench.table aggregates result JSONs into one table; runner saves after every task and merges per task across invocations.

• Bench: metrics (accuracy, macro-F1, Brier, NLL, ECE, flip rate, coverage-risk), tasks newsgroups, injection, banking20, runner python -m bench.run.

pip install "anyjev[hf]"