u/CurrentVast4504

Hi there!
I have been working on this engine for the past 6 months now and I think it's now ripe to be thrown into the wild.
The engine features a NNUE trained using nnue-pytorch. It is comparatively very small.
To accommodate this I tried to make the engine's search as buffed as possible.

Anyway here's the specific details of the engine for the nerds:

Evaluation

• NNUE Architecture: HalfKAv2_hm^
• Network Layers: L1=128, L2=32, L3=8 with PSQT buckets
• Network File: Acherontia (88mm.nnue)
• Incremental NNUE Evaluation: Efficient position updates with feature transformer
• Tapered Material Evaluation: Smooth transition between middlegame and endgame
• Customizable NNUE Scaling: Adjust evaluation strength via UCI option

Search

• Core Algorithm: Alpha-Beta Negamax with fail-soft
• Move Ordering: Principal Variation Search (PVS)
• Depth: Iterative Deepening up to 64 plies
• Transposition Table: Configurable 1-4096 MB with generation-based aging
• Pruning Techniques:
  • Late Move Reductions (LMR) with logarithmic formula
  • Null Move Pruning
  • Reverse Futility Pruning
  • Futility Pruning
  • ProbCut
  • Delta Pruning in quiescence search
• Move Ordering Heuristics:
  • Killer Move Heuristic
  • History Heuristic
  • Continuation History (4 ply)
  • Countermove Heuristic
• Extensions:
  • Singular Extensions
  • Static exchange evaluation (SEE) for capture ordering and pruning
• Time Management: Aspiration Windows with soft and hard time limits
• Quiescence Search: Delta pruning and tactical awareness
• Parallel Search: Multi-threaded support (up to 8 threads)

And here's the Github release page for the testers and alike: https://github.com/Azrael337/Tiger-1-Chess-Engine/releases/tag/chess-engine