ai-research-reproduction

Purpose

ai-research-reproduction

../../references/agent-operating-principles.md

../../references/research-rigor-principles.md

../../references/deep-learning-experiment-principles.md

Fit

• The target is an AI code repository with a README, scripts, configs, or documented commands.
• The request spans multiple trusted phases such as intake, setup, execution, training verification, analysis, paper-gap resolution, and reporting.
• The desired result is a small reproducible target, not broad experimentation.

Trusted Target Selection

1. documented inference
2. documented evaluation
3. documented training startup or partial verification
4. full training only after explicit user confirmation

paper-context-resolver

Workflow

1. Read the README and nearby repo signals.
2. Use

   repo-intake-and-plan

   to extract documented commands and candidate targets.
3. Select and justify the minimum trustworthy target.
4. Use

   env-and-assets-bootstrap

   only for target-specific environment, checkpoint, dataset, and cache assumptions.
5. Use

   analyze-project

   only when structure, insertion points, or suspicious implementation patterns need read-only clarification.
6. Use

   minimal-run-and-audit

   for documented inference, evaluation, smoke, or sanity execution.
7. Use

   run-train

   instead when the selected trusted target is training startup, short-run verification, full kickoff, or resume.
8. Pause for human review before fuller training claims or any change that could alter dataset, split, checkpoint, preprocessing, metric, loss, model semantics, or result interpretation.
9. Write the standardized outputs and give a concise final note in the user's language when practical.

Patch Boundary

• Try command-line arguments, environment variables, path fixes, dependency version fixes, or dependency-file fixes before code changes.
• Reproduction fixes are allowed when needed, but they must not be hidden. State what changed, why it was necessary, whether it changes scientific meaning, and whether it affects comparability with the paper, README, or baseline.
• Avoid changing model architecture, core inference semantics, training logic, loss functions, or experiment meaning.
• If repository files must change, create a branch named

  repro/YYYY-MM-DD-short-task

  , keep verified patch commits sparse, and record README-fidelity impact in

  PATCHES.md

  .

references/patch-policy.md

Outputs

repro_outputs/

SUMMARY.md
COMMANDS.md
LOG.md
SCIENTIFIC_CHANGELOG.md
COMPARABILITY_REPORT.md
status.json
PATCHES.md   # only if patches were applied

assets/

references/output-spec.md

• Put the shortest high-value summary in

  SUMMARY.md

  .
• Put copyable commands in

  COMMANDS.md

  .
• Put process evidence, assumptions, failures, and decisions in

  LOG.md

  .
• Put scientific meaning and change effects in

  SCIENTIFIC_CHANGELOG.md

  .
• Put comparison anchors and protocol deviations in

  COMPARABILITY_REPORT.md

  .
• Put durable machine-readable state in

  status.json

  .
• Put branch, commit, validation, and README-fidelity impact in

  PATCHES.md

  when needed.
• Distinguish verified facts from inferred guesses.

Reference Loading

• Load

  references/language-policy.md

  when writing human-readable outputs.
• Load

  ../../references/research-rigor-principles.md

  before making comparability, contribution, or research-result claims.
• Load

  ../../references/deep-learning-experiment-principles.md

  when dataset, split, metric, checkpoint, training, or evaluation details matter.
• Load

  references/research-safety-principles.md

  before protocol-sensitive decisions.
• Load

  references/patch-policy.md

  before modifying repository files.
• Keep specialized logic in sub-skills, scripts, templates, or references rather than expanding this entrypoint.