Implement hypothesis generation framework

[tylerbessire]

Summary

• introduce Hypothesis dataclass and HypothesisEngine for generating and testing geometric, color, pattern, and translation hypotheses
• integrate hypothesis generation into ARCSolver as an optional early reasoning step
• add analogical reasoning via AnalogicalReasoner to enhance episodic retrieval
• add unit tests for hypothesis engine and analogical reasoning; update AGENTS progress log

Testing

• python -m py_compile arc_solver/neural/episodic.py arc_solver/neural/__init__.py tests/test_analogical_reasoner.py
• pytest -q

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https://chatgpt.com/codex/tasks/task_e_68c271455b108322a59fd2d7ecf848dc

Summary by CodeRabbit

• New Features

  • Introduced a hypothesis-driven reasoning path that can automatically solve tasks when a perfect transformation is detected, improving speed and accuracy.
  • Added a lightweight hypothesis engine covering geometric rotations, color remapping, pattern fills, and object translations.
  • Exposed analogical reasoning capabilities to find similar past tasks, map solution structures, and abstract common patterns.

• Documentation

  • Updated progress markers and notes to reflect completed milestones with dates and clearer outcomes.

• Tests

  • Added unit tests for analogical reasoning and hypothesis generation/validation to ensure robustness.

[coderabbitai]

Caution

Review failed

The pull request is closed.

Walkthrough

Introduces a hypothesis generation/testing module, integrates an early hypothesis path into ARCSolver, adds an AnalogicalReasoner to episodic reasoning and re-exports it, updates AGENTS.md progress markers, and adds unit tests for both AnalogicalReasoner and HypothesisEngine.

Changes

Cohort / File(s)	Summary
Documentation updates AGENTS.md	Marked Steps 2.1 and 2.2 as completed with dates and revised notes/results.
Hypothesis engine (new) arc_solver/hypothesis.py	Added Hypothesis dataclass and HypothesisEngine with generation, application, testing, and refinement of hypotheses (rotation, color swap, pattern fill, object translation).
Neural episodic API exposure arc_solver/neural/__init__.py	Re-exported AnalogicalReasoner; updated __all__.
Analogical reasoning (new class) arc_solver/neural/episodic.py	Added AnalogicalReasoner with methods for structural similarity retrieval, solution structure mapping, and pattern abstraction.
Solver integration arc_solver/solver.py	Integrated HypothesisEngine into ARCSolver with an early-exit path when a perfect hypothesis applies to tests; exported Hypothesis and HypothesisEngine.
Tests: analogical reasoner tests/test_analogical_reasoner.py	Added tests for structural analogies retrieval, recolor mapping in solution mapping, and pattern abstraction.
Tests: hypothesis engine tests/test_hypothesis_engine.py	Added tests for rotation and color-mapping hypothesis generation and hypothesis scoring.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  participant U as User
  participant S as ARCSolver
  participant H as HypothesisEngine
  participant T as TestInputs

  U->>S: solve_task(train_pairs, test_inputs)
  rect rgba(200,240,255,0.3)
    note right of S: New early hypothesis path
    S->>H: generate_hypotheses(train_pairs)
    H-->>S: hypotheses (sorted)
    alt any hypothesis
      S->>H: test_hypothesis(best_hypothesis, train_pairs)
      H-->>S: confidence
      alt confidence == 1.0
        S->>H: apply(best_hypothesis, each test input)
        H-->>S: transformed outputs or None
        alt all succeed
          S-->>U: return transformed results (early exit)
        else at least one failed
          note right of S: fallback to prior flow
          S->>T: per-input prediction (existing)
          S-->>U: return predictions
        end
      else not perfect
        S->>T: per-input prediction (existing)
        S-->>U: return predictions
      end
    else no hypotheses
      S->>T: per-input prediction (existing)
      S-->>U: return predictions
    end
  end

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sequenceDiagram
  autonumber
  participant C as Current Task
  participant M as EpisodicRetrieval
  participant R as AnalogicalReasoner

  C->>R: find_structural_analogies(C, M, threshold)
  R->>M: iterate episodes and compare features
  M-->>R: episodes with features
  R-->>C: similar episodes with scores (filtered/sorted)

  Note over R,C: Mapping a source solution to target
  participant S as Source Solution
  C->>R: map_solution_structure(S, C)
  R->>R: derive recolor mapping from C first pair
  R-->>C: transformed Program

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Estimated code review effort

🎯 4 (Complex) | ⏱️ ~60 minutes

Possibly related PRs

• Unify solver and reorganize neural components #4 — Also modifies solver integration and neural episodic/analogical surfaces, suggesting overlapping control-flow and API exposure changes.

Poem

In grids I hop with bright delight,
I test a hunch by pixel light.
A twist, a swap—aha, it fits!
Analogies thread clever bits.
Early I spring, solution found—
Hypotheses thump the ground. 🐇✨

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📜 Recent review details

Configuration used: CodeRabbit UI

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between cf784ba and c173300.

📒 Files selected for processing (7)

• AGENTS.md (2 hunks)
• arc_solver/hypothesis.py (1 hunks)
• arc_solver/neural/__init__.py (2 hunks)
• arc_solver/neural/episodic.py (1 hunks)
• arc_solver/solver.py (3 hunks)
• tests/test_analogical_reasoner.py (1 hunks)
• tests/test_hypothesis_engine.py (1 hunks)

✨ Finishing touches

• 📝 Generate Docstrings

🧪 Generate unit tests

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch codex/review-agents.md-for-implementation-steps

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