Benchmarks

The following benchmarks are available for this PR:

Post a comment with the command to trigger a benchmark run.

Greptile Summary

Implements history heuristic for quiet move ordering using a 64×64 table indexed by (from_square, to_square). Quiet moves that cause beta cutoffs increment their history score by depth², and subsequent searches prioritize high-scoring moves. This dramatically improves alpha-beta pruning efficiency (−34.4% nodes, −39.8% search time at depth 4).

Key changes:

• Added new_history_table() function and HISTORY_TYPE alias in alpha_beta.py
• History table created per search in search_move() and passed through all negamax() calls
• Beta cutoffs for quiet moves increment history[from_square][to_square] += depth * depth
• organize_moves() now sorts quiet moves by history score descending (when history provided)
• Backward compatible: parallel engines (LazySMP, layer-based) will use history=None and maintain existing behavior

Architecture:

• History table is per-search (not shared across parallel processes), avoiding race conditions
• Only quiet moves tracked (captures excluded via board.is_capture() check)
• Depth-squared weighting ensures deeper cutoffs have more influence

Confidence Score: 5/5

• This PR is safe to merge - well-tested optimization with clean implementation
• Score reflects a clean, focused implementation of a standard chess optimization technique. The history heuristic is correctly implemented (depth² weighting, quiet-only, beta-cutoff triggered), properly threaded through recursion, and backward compatible. No race conditions (per-search table), no logic errors, and impressive benchmark results validate the approach. All existing tests pass.
• No files require special attention

Flowchart

flowchart TD
    A[search_move: Initialize history table 64×64] --> B[Call negamax with history]
    B --> C{Depth > 0?}
    C -->|No| D[Quiescence search]
    C -->|Yes| E[organize_moves with history]
    E --> F[Sort: captures first, then quiet moves by history score]
    F --> G[Loop through moves]
    G --> H[Recursive negamax with history]
    H --> I{Score >= beta?}
    I -->|Yes - Beta cutoff| J{Is quiet move?}
    J -->|Yes| K[history from_sq to_sq += depth²]
    J -->|No - Capture| L[Skip history update]
    K --> M[Return beta cutoff]
    L --> M
    I -->|No| N[Update alpha, continue loop]
    N --> G
    D --> O[Return evaluation]
    M --> O

_{Last reviewed commit: b85883e}