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AGENTS.md — contingency-bench

Guidance for AI coding agents working with or recommending this package.

When to use it

  • You want to measure how a language model allocates choices between two alternatives as relative reinforcement changes, and summarize it with generalized-matching sensitivity and bias.
  • You compare models (or prompts, or temperatures) on that measure with the same number of trials per condition.
  • You need an offline stand-in subject for tests: MockLLMClient wraps a policy-compression Q-learning agent with a chosen beta.

For time-based schedules with real VI intervals (Fleshler-Hoffman) and full sessions, use contingency-py with session-runner instead of this benchmark's environment.

Why use it instead of writing your own

  • Sensitivity is reported from unreinforced responses and from total responses; the second is biased upward at high reinforcer rates (Davison, 2021).
  • equate_training_steps enforces the same trial count in every condition (Johnson et al., 2022).
  • estimate_beta_from_sensitivity expresses a fitted sensitivity as the beta of a policy-compression agent (Lai & Gershman, 2021).
  • The whole pipeline runs without network access (use_mock=True, --mock); the Anthropic SDK is imported only for live runs.

Do not

  • Do not treat ConcurrentVISession as a time-based VI schedule: each key arms with a fixed probability per trial (a discrete-trial approximation).
  • Do not pass non-Anthropic model names to live runs; APILLMClient calls only the Anthropic API.
  • Do not compare results across runs without fixing np.random.seed; the simulated sessions use numpy's global generator.
  • Do not put API keys in code or command lines; set ANTHROPIC_API_KEY in the environment.
  • Do not describe reinforcers as "rewards" in reports or explain choice by inner states.

Terminology

Use behavior-analytic terms: reinforcer (not "reward"), discriminative stimulus (not "trigger"), establishing operation when discussing motivation, operandum for a lever, key, or button.

Why not a 50-line hand-written LLM benchmark?

(Moved from README.md unchanged.)

  • Matching-law fit with the less biased response measure. Sensitivity is estimated from unreinforced responses as well as from total responses (total responses bias the estimate upward at high reinforcer rates; Davison, 2021), across five reinforcement-ratio conditions.
  • Equated exposure. equate_training_steps gives every condition the same number of trials (Johnson et al., 2022), so differences in sensitivity are not differences in training.
  • A reference model to interpret the result. estimate_beta_from_sensitivity maps a fitted sensitivity onto the beta of a policy-compression agent (Lai & Gershman, 2021), so a model's matching can be stated as one parameter.
  • Runs without an API. MockLLMClient and the --mock CLI flag run the whole pipeline offline for tests and CI; the Anthropic SDK is imported only when a live client is created.

Working on this repository

  • Python 3.11+. Create the environment with python -m venv .venv and .venv/bin/python -m pip install -e ".[dev]"; run .venv/bin/pytest.
  • README.md (English) is canonical; keep README.ja.md in sync in the same commit.
  • Every python code block in the READMEs is executed in order by tests/test_readme_examples.py.