merge-diff-arc-agi-task

Merge Diff ARC-AGI Task

Overview

This skill addresses tasks that combine git branch merging with ARC-AGI pattern recognition challenges. These tasks typically involve:

1. Extracting and merging multiple git branches from bundles
2. Resolving merge conflicts between different algorithm implementations
3. Analyzing input/output grid examples to discover transformation patterns
4. Implementing a map function that generalizes to hidden test cases

Pre-flight Checklist

Before starting any git operations, complete these setup steps to avoid interruptions:

1. Configure git identity (prevents merge commit failures):

   git config user.email "agent@example.com"
   git config user.name "Agent"
   

2. Verify working directory is clean or stash changes

3. Identify the base branch to work from

Phase 1: Git Operations

Branch Extraction and Setup

1. Extract branches from bundle files:

   git bundle unbundle <bundle-file>
   

2. Create local branches from the extracted refs:

   git checkout -b <branch-name> <ref>
   

3. Before merging, examine each branch's implementation:

   • Read the algorithm files on each branch
   • Understand what approach each implementation takes
   • Note dependencies (numpy, etc.) and coding styles

Merge Conflict Resolution

When merge conflicts occur:

1. Do not blindly choose one side - both implementations may contain insights
2. Understand both approaches before resolving:
   • What pattern does each implementation detect?
   • What edge cases does each handle?
   • Can elements from both be combined?
3. Document the resolution rationale - explain why the final implementation was chosen

Phase 2: Pattern Analysis Framework

Systematic Analysis Process

Apply this structured approach to avoid jumping between hypotheses:

1. Inventory the data:

   • Grid dimensions for each example
   • Unique values present (zero vs non-zero)
   • Positions of each unique value

2. Compare input to output:

   • What values appear in output that weren't in input?
   • What spatial relationships change?
   • What remains constant?

3. Formulate a single hypothesis and test against ALL examples before moving on

4. Document findings in a structured format:

   Example N:
   - Input: [dimensions, unique values, positions]
   - Output: [dimensions, patterns observed]
   - Hypothesis test: [pass/fail with explanation]
   

Common ARC-AGI Patterns to Consider

• Tiling patterns: Values repeated across grid based on position formulas
• Diagonal patterns: Relationships based on i+j, i-j, or similar
• First occurrence mapping: Order determined by where values first appear
• Modular arithmetic: (i+j) % n or similar cyclic patterns
• Spatial transformations: Rotations, reflections, translations

Pattern Verification Checklist

Before finalizing an algorithm:

• Does the pattern explain ALL provided examples?
• Is the formula derivation logically justified (not just empirically fitted)?
• What happens with edge cases:
  • Empty grids or all-zero grids?
  • Single unique non-zero value?
  • Collision scenarios (e.g., two values with same modular position)?
• Would the pattern generalize to different grid sizes?

Phase 3: Implementation

Algorithm Development

1. Start with clear documentation:

   def map(grid: list[list[int]]) -> list[list[int]]:
       """
       Transform input grid to output grid.
   
       Algorithm:
       1. [Step 1 explanation]
       2. [Step 2 explanation]
   
       Edge cases handled:
       - [Case 1]
       - [Case 2]
       """
   

2. Handle edge cases explicitly:

   if not grid or not grid[0]:
       return grid
   unique_values = [v for v in set(flatten(grid)) if v != 0]
   if len(unique_values) == 0:
       return [[0] * len(grid[0]) for _ in range(len(grid))]
   

3. Avoid collision bugs: When mapping values to positions based on formulas, verify that no two values map to the same position, or handle the collision explicitly

Testing Strategy

Create a reusable test function early:

def test_map_function(examples_path: str) -> bool:
    """Test map function against all examples."""
    import json
    with open(examples_path) as f:
        examples = json.load(f)

    all_passed = True
    for i, ex in enumerate(examples):
        result = map(ex['input'])
        expected = ex['output']
        if result != expected:
            print(f"Example {i} FAILED")
            print(f"  Expected: {expected}")
            print(f"  Got: {result}")
            all_passed = False
        else:
            print(f"Example {i} PASSED")
    return all_passed

Run this after every algorithm change rather than writing ad-hoc test scripts.

Phase 4: Verification

Final Verification Checklist

Before declaring the task complete:

1. All examples pass: Run test function against examples.json
2. Algorithm is documented: Comments explain the "why" not just the "what"
3. Edge cases are handled: Empty inputs, single values, boundary conditions
4. Generalization is justified: The algorithm logic follows from the pattern, not from fitting to specific examples
5. Git state is clean: Merge is complete, no uncommitted changes

Generalization Confidence Assessment

Rate confidence that the solution will pass hidden tests:

• High confidence: Pattern logic is clearly derived from first principles; edge cases explicitly handled; formula is mathematically justified
• Medium confidence: Pattern works on all examples but some aspects were discovered empirically
• Low confidence: Solution was fitted to examples without clear logical derivation

If confidence is medium or low, revisit the pattern analysis phase.

Common Pitfalls

Resources

references/

The references/ directory contains detailed guidance:

• pattern_analysis_template.md - Structured template for documenting pattern analysis
• git_merge_checklist.md - Step-by-step git operations checklist