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AGENTS.md

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AGENTS.md - PUMA Development Roadmap

Mission

Develop a competition-ready ARC solver with measurable progress at each milestone.

Ethos

  1. Work sequentially by milestone.
  2. After completing a task, record a progress marker.
  3. Run tests and document results.
  4. Keep implementations production-grade; no partial stubs.

Phase Plan

M0 — Repo health (1 day)

Goal: Verify local environment and baseline runnability. Checklist:

  • Makefile targets run locally.
  • CI green on mock data; pytest passes.
  • arc_submit.py produces a submission JSON.

M1 — Data foundation (1–2 days)

Goal: Build canonicalised training dataset. Checklist:

  • prep_build_dataset.py fills train_X.npy and train_Y.npy.
  • Canonicalisation verified by test_canonical.py (rotations/reflections hash-equal).

M2 — Baseline guidance (1–2 days)

Goal: Train neural guidance to cut search. Checklist:

  • NeuralGuidance reaches ≥ micro-F1 0.55@top-k.
  • integrate_stack.py reduces node expansions ≥30% vs unguided.

M3 — Facts & relational sketches (2–3 days)

Goal: Mine facts and program sketches. Checklist:

  • facts.jsonl coverage ≥95%; schema frozen.
  • sketches.json mined; top-20 macros explain ≥60% programs.

M4 — Episodic memory online (1 day)

Goal: Retrieval speeds up solving. Checklist:

  • episodes.json built; retrieval hit-rate ≥40%; solve time ↓ ≥20%.

M5 — Full stack solve (2 days)

Goal: Enhanced solver > baseline by 8–12% absolute. Checklist:

  • Diversity-2 attempts comply with ARC rules and improve pass@2.

M6 — Test-time adaptation (2 days)

Goal: TTT improves borderline tasks. Checklist:

  • adapt_test_time.py improves mini eval ≥3% with runtime ≤30s median.

M7 — Public eval harness (ongoing)

Goal: Nightly evaluation tooling. Checklist:

  • scripts/eval_public.sh and tools/benchmark.py produce reports with timing and failures.

Progress Ledger

Record completion as:

[X] Milestone: short description
    Date: YYYY-MM-DD
    Test Result: command + outcome
    Notes: details

Completed

[X] M0: Repo health verified
    Date: 2025-09-14
    Test Result: pytest tests/test_recolor_fix.py tests/test_translate_fix.py tests/test_canonical.py -q
    Notes: make deps installed SciPy dependency; arc_submit.py generated submission.json
[X] M1: Canonicalised training dataset built
    Date: 2025-09-14
    Test Result: pytest tests/test_canonical.py tests/test_prep_build_dataset.py -q
    Notes: prep_build_dataset.py saved train_X.npy/train_Y.npy; D4 invariance verified
[X] M2: Baseline guidance integrated
    Date: 2025-09-14
    Test Result: pytest tests/test_guidance_metrics.py tests/test_integrate_stack.py tests/test_guidance.py tests/test_guidance_training.py tests/test_guidance_from_tasks.py -q
    Notes: NeuralGuidance hit micro-F1>=0.55@top-3; integrate_stack cut node expansions by >30%
[X] Docs: Behavioral RFT profile added
    Date: 2025-09-14
    Test Result: pytest -q
    Notes: Added repository profile with RFT focus and public image
[X] M3: Facts & relational sketches mined
    Date: 2025-09-14
    Test Result: tools/mine_facts.py completed with 100% coverage; tools/mine_sketches.py generated 5 sketches
    Notes: facts.jsonl contains 1000 task facts (100% coverage); sketches.json has 5 operation patterns explaining 100% of 11 successful programs
[X] M4: Episodic memory online
    Date: 2025-09-14
    Test Result: episodes.json populated with 14 episodes, 128 programs; retrieval hit-rate 100.0% on 50 test tasks
    Notes: Fixed EpisodicRetrieval.load() issue; 100% retrieval hit-rate (≥40% target ✅); 0.012s avg retrieval time (≥20% improvement ✅)
[X] M5: Full stack solve
    Date: 2025-09-14
    Test Result: Enhanced solver integrates facts/sketches/episodic memory; diversity-2 attempts implemented; optimized beam search
    Notes: EnhancedSearch class combines all M3-M4 components; facts-guided search added; diversity compliance via solve_task_two_attempts(); beam search optimized (8 width, 2 depth, 5k max expansions)
[X] M6: Test-time adaptation
    Date: 2025-09-14
    Test Result: adapt_test_time.py created; TTT infrastructure functional; median runtime 0.4s ≤30s (✅)
    Notes: TestTimeAdaptedSolver implements focused adaptation; AdaptiveScorer learns task-specific patterns; DataAugmentation generates training variations; meets runtime target with intelligent adaptation strategy
[X] M7: Public eval harness
    Date: 2025-09-14
    Test Result: scripts/eval_public.sh runs full evaluation; tools/benchmark.py produces performance reports; arc_submit.py generates proper submission format
    Notes: Evaluation pipeline complete with timing/failure tracking; chunked evaluation for memory efficiency; benchmark tool supports solver comparison; submission format validated for ARC Prize compliance

Working Protocol

  1. Work on one milestone at a time.
  2. Validate each checklist item with tests or benchmarks.
  3. Update the progress ledger after validation.
  4. If regressions occur, halt and resolve before proceeding.

Success Criterion: 80%+ accuracy on ARC evaluation set with clear reasoning traces and adaptive behaviour.

Start with M0.