V2 #22
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🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
…DOC-LINK v1] pass
…nalysis-into-puma-solver
…nalysis-into-puma-solver Implement behavioral reinforcement engine with RFT integration
- Add PlaceholderCandidateGenerator with multiple reconstruction strategies - Implement stripe pattern extraction from borders with extended context - Add advanced mirroring strategies (symmetric, pattern completion, contextual) - Implement recolor mapping derived from border/neighbor palette analysis - Extend DSL with placeholder reconstruction operations - Add PlaceholderTemplateEngine for template detection and application - Create comprehensive test suite showing 44+ candidate generation strategies - Integrate with existing HumanGradeReasoner for macro validation and storage 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
- Added checkpoint_path and enable_logging parameters to ARCSolver - Implemented save_checkpoint/load_checkpoint with automatic saves every 10 tasks - Added submission_results tracking for progress recovery - Created logging toggle controlled by ARC_ENABLE_LOGGING environment variable - All logging statements now respect enable_logging flag to reduce memory usage - Checkpoint saves task results, stats, and progress metadata - Supports resuming from checkpoint after memory crashes 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
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Caution Review failedThe pull request is closed. WalkthroughIntroduces extensive ARC solver expansions: a reinforcement-driven BehavioralEngine, relational (RFT) and pattern modules, placeholder templating, glyph extraction, intraverbal/tacting guidance, continuous self-memory, enhanced search/ranking, solver checkpointing/state, test-time adaptation, evaluation tools/GUI, utilities, tests, model weight updates, and documentation. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor User
participant BE as BehavioralEngine
participant DL as Dataset Loaders
participant ES as EnhancedSearch
participant RFT as RFTEngine
participant NG as NeuralGuidance
participant ER as EpisodicRetrieval
participant RG as RewardGrader
Note over BE: Feature flag checked (env)
User->>BE: train(dataset_path, solutions_path, ...)
BE->>DL: load_challenges/load_solutions
loop per task
BE->>RFT: analyse(train_pairs)
RFT-->>BE: RFTInference (function_hints)
BE->>ES: synthesize_enhanced(train_pairs)
ES-->>BE: candidate programs
BE->>RG: grade(predictions vs targets, program, behavioural_signal)
RG-->>BE: RewardBreakdown
alt best program found
BE->>NG: reinforce(train_pairs, program, reward, inference)
BE->>ER: add_successful_solution(..., reward, metadata)
else no viable
Note over BE: fallback/no update
end
end
BE-->>User: BehaviouralMetrics (JSON-serializable)
sequenceDiagram
autonumber
actor User
participant TTA as TestTimeAdaptedSolver
participant BS as Baseline ARCSolver
participant TTT as TestTimeTrainer
participant ES as EnhancedSearch
User->>TTA: evaluate_with_adaptation(tasks, budget)
loop per task
TTA->>ES: initial candidates (search/synthesis)
ES-->>TTA: programs + scores
alt adapt enabled and candidates
TTA->>TTT: adapt(train_pairs, programs)
TTT-->>TTA: adapted scorers/models
TTA->>ES: re-score/rank with adapted state
ES-->>TTA: ranked programs
else skip adaptation
end
alt programs viable
TTA-->>User: predictions + timings
else
TTA->>BS: solve(task)
BS-->>TTA: fallback predictions
TTA-->>User: predictions + timings
end
end
TTA-->>User: summary stats (success rates, medians)
Estimated code review effort🎯 5 (Critical) | ⏱️ ~120+ minutes Possibly related PRs
Suggested labels
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✨ Finishing touches
🧪 Generate unit tests
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📒 Files selected for processing (37)
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Summary by CodeRabbit
New Features
Documentation
Tests
Chores