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Benchmark assets, reproducibility tooling, and evidence checks for dormant behavior audit.

Project description

Dormant Behavior Audit

PyPI version Homepage Benchmark release Zenodo DOI

This repository contains the flagship benchmark assets, reference bundle, and reproducibility materials for auditing latent, condition-dependent model behavior.

The motivating historical case is the Jane Street dormant-model puzzle, but the repo is now organized as a public benchmark and research release rather than a contest-only submission package.

Slow Tour

A minimal starry-night terminal animation showing the slow benchmark flow from charter to reference bundle to reproduction to claim checks to release.

A quiet walk through the release path: open the charter, inspect the reference bundle, rerun the evidence, compare claim checks, and package the release.

Start Here

If you want the quickest tour, read these in order:

  1. benchmarks/BENCHMARK_CHARTER.md
  2. findings/RELEASE_PACKET_V2.md
  3. benchmarks/reference/dormant_puzzle_v1/benchmark_bundle_v0.json
  4. PUBLIC_RELEASE_CHECKLIST.md
  5. CONTRIBUTING.md

Install The CLI

The repository now builds as a Python package with a unified dba command.

The current live package release is 1.0.3 on PyPI. It is the sanitized maintenance package for the frozen benchmark/report release v1.0.0, which remains the canonical tagged research bundle.

pipx install dormant-behavior-audit
dba --help

For a local one-off run without a permanent install:

uvx --from dormant-behavior-audit dba --help

Optional extras:

  • pipx install 'dormant-behavior-audit[tui]' for the Orbit Textual UI
  • pipx install 'dormant-behavior-audit[notebooks]' for notebook-heavy local analysis

The default install is intentionally substantial because it includes the research stack needed for reproduction and analysis, not just a lightweight wrapper CLI.

What This Repo Ships

Public-facing research packet

Benchmark assets

Reproducibility artifacts

Benchmark Shape

The current benchmark release has three layers:

  • core local seeded and clean-control tasks,
  • a naturalistic historical reference bundle built from the dormant puzzle result,
  • and a supplementary hosted-comparator lane used for calibration and mechanism interpretation.

The benchmark is designed to reward:

  • family recovery instead of one lucky string guess,
  • candidate-versus-control specificity,
  • repeated-run stability,
  • interpretation-aware reporting,
  • and artifact-rich submission packets instead of one scalar score.

Reproducing The Reference Case

Install dependencies:

pip install -r requirements.txt

Run the reproducibility pipeline:

python3 scripts/reproduce_submission.py

This writes a fresh bundle under artifacts/reproduction/<timestamp>/.

Use these files to judge success:

  • artifacts/reproduction/<timestamp>/reproduction_report.md
  • artifacts/reproduction/<timestamp>/findings/claim_consistency_report.md

Important notes:

  • local warmup stages are expected to reproduce on MPS-capable hardware,
  • API-side artifacts are stochastic, so claim-level consistency matters more than exact JSON replay,
  • and scripts/reproduce_submission.py --warmup-start-stage ... can resume a late warmup failure without rerunning the entire local sweep.

Repo Map

  • benchmarks/: benchmark specs, tasks, schemas, public-release drafts, and the normalized reference bundle
  • findings/: public report packet, appendices, raw evidence snapshots, and release-facing validation records
  • artifacts/: checked-in submission packets, reproduction bundles, tightening bundles, and hosted-baseline outputs
  • scripts/: bundle builders, release checkers, reproducibility scripts, and analysis utilities
  • src/, orbit/, problems/: earlier investigation and local-analysis surfaces preserved for provenance and follow-on work

Release Status

The canonical release metadata lives in benchmarks/public/release_metadata.json.

Current public release URLs:

The working launch checklist is still preserved in PUBLIC_RELEASE_CHECKLIST.md as the release record.

Licensing

  • Code, scripts, and schemas: Apache-2.0 via LICENSE
  • Public-facing reports, benchmark docs, and release artifacts: CC BY 4.0 via LICENSE-docs.md

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