figrid-board 0.7.1

A library for the Five-in-a-Row (Gomoku) game, powered by noru (Rust NNUE library).
Documentation

What's inside

figrid-board ships in two roles from a single crate:

  • Library (figrid_board) — board representation, rule logic, move generation, threat detection, transposition table, and an NNUE evaluation surface. Reusable from any Rust project that wants Gomoku game state and search primitives without an engine attached.
  • Engine binaries:
    • pbrain-figrid — the NNUE engine, powered by noru. Speaks the Piskvork pbrain protocol and is the binary intended for tournament play.
    • pbrain-figrid-legacy — preserves the original pre-NNUE engine by wuwbobo2021, kept as a reference baseline.

Features

  • Pure Rust, no C dependencies. Single static binary after build.
  • NNUE-based evaluation through noru, with incremental accumulator updates.
  • α-β search with transposition table, threat-aware move ordering, killer/history heuristics, late-move pruning, and a quiescence layer for forcing sequences.
  • Optional VCF / VCT tactical search at the search root.
  • Rule support: Freestyle and Standard (exact-five). Renju and Caro currently rejected at the protocol layer.
  • Optional avx512 cargo feature: opportunistic ~2× evaluation speedup on AVX-512 hardware, with automatic AVX-2 runtime fallback. Requires Rust ≥ 1.89; off by default so library users on older toolchains and crates.io itself can build.
  • Optional embed-weights feature: bake the NNUE weights into the binary at build time, producing a single self-contained executable suitable for tournament submission.

Quick start

Use as a Piskvork engine

Build the engine binary:

RUSTFLAGS="-C target-cpu=native" cargo build --release --bin pbrain-figrid --features embed-weights

Add target/release/pbrain-figrid (or .exe on Windows) to Piskvork as an AI player. The embed-weights feature bundles the NNUE weights into the executable so you do not have to ship them separately.

If you build without embed-weights, set FIGRID_WEIGHTS=path/to/weights.bin or place the file at ./models/ so the binary can locate it at startup.

Use as a library

[dependencies]
figrid-board = "0.6"

use figrid_board::{to_idx, Board};

let mut board = Board::new();
board.make_move(to_idx(7, 7)); // black H8 (row 7, col 7, 0-indexed)
board.make_move(to_idx(7, 8)); // white I8 (row 7, col 8)
println!("{:?}", board.side_to_move); // Black (the side about to move)

NNUE weights and the search struct are exposed for users who want to drive the engine programmatically rather than through the Piskvork protocol.

Build

Local / development — target the host CPU for maximum performance:

RUSTFLAGS="-C target-cpu=native" cargo build --release

Gomocup submission — -C target-cpu=native is wrong here, because the binary must run on the tournament's machines, not yours. The Gomocup 2026 announcement guarantees SSE4.1, SSE4.2, POPCNT, AVX, and AVX2, which is exactly x86_64-v3. Link the C runtime statically and embed the NNUE weights so the submission is one self-contained file:

RUSTFLAGS="-C target-feature=+crt-static -C target-cpu=x86-64-v3" \
    cargo build --release --target x86_64-pc-windows-gnu \
    --bin pbrain-figrid --features embed-weights

For an AVX-512-capable second submission entry, additionally enable the avx512 cargo feature (requires Rust ≥ 1.89 on the build host); GomocupJudge picks the fastest compatible binary on each match machine:

RUSTFLAGS="-C target-feature=+crt-static -C target-cpu=x86-64-v4" \
    cargo build --release --target x86_64-pc-windows-gnu \
    --bin pbrain-figrid --features embed-weights,avx512

Roadmap

Pushing the engine harder for Gomocup 2026 (submission deadline 2026-05-29 UTC, tournament June 5–7). Strength work continues on every layer — search, evaluation, and training data — because there is always more performance to squeeze out before the deadline.

Pre-NNUE technical debt inherited from the 0.3.x series is tracked in docs/INHERITED_TODO.md.

Maintainership

As of 2026-04-20, primary maintainership has been transferred from the original author wuwbobo2021 to nicotina04. Future development targets a stronger NNUE-based engine while preserving the existing board / rule / tree library surface.

Legacy users

Users who need the pre-Rust figrid-board as a Linux alternative to Renlib can download tag v0.20.

Acknowledgments

  • wuwbobo2021 for the original engine and for entrusting figrid-board to its current maintainer.
  • Rapfi for advancing public NNUE work in Gomoku and for serving as a reference point during evaluation development.
  • noru for the underlying Rust NNUE training and inference stack.

License

Dual-licensed under either of MIT or Apache-2.0 at your option, matching the SPDX identifier MIT OR Apache-2.0 declared in Cargo.toml.