Prerequisite for parallel search. NNUE held all state on one instance:
the immutable transposed L1 weight matrix alongside the mutable
accumulator stack, scratch buffers and eval cache. That made concurrent
eval calls corrupt shared buffers.
Extract the read-only parameters into NNUEWeights (heavy to build, safe
to share). NNUE now owns only per-instance mutable buffers and references
the shared weights, so many evaluators can run in parallel over one weight
matrix without duplicating it. Single-instance behaviour is unchanged —
EvaluationNNUE still uses one evaluator, so play is identical.
Also applies scalafmt alignment to the MopUp files.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Repetition: alpha-beta seeded the repetition map with only the root
position, so search was blind to positions already reached in the real
game and would happily shuffle into draws when ahead. Reconstruct the
full game-history position hashes by replaying moves and seed the search
state with them; treat a twofold occurrence at non-root nodes as a draw.
Contempt: draws are now scored CONTEMPT (25cp) away from zero, signed by
ply parity, so the bot avoids dead-equal repetitions instead of settling.
Endgame: pure NNUE lacks mating knowledge and stalls KX-vs-K conversions.
Add a MopUp correction (edge-driving + king-proximity) applied only in
lone-king endgames with sufficient mating material; zero elsewhere so
middlegame NNUE output is untouched.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Janis