Benjamin Woo

A small, fast network for Hivemind, a UCI engine for Bughouse chess — the four-player variant played on two boards, where captured pieces are passed to your partner. The network drives a Monte Carlo Graph Search (MCGS) that evaluates both boards jointly. It was distilled from the 13.6 M-parameter RISEv3.3 network and then improved by self-play reinforcement learning. At 0.97 M parameters it searches about 3x as many nodes per second, and was stronger at 100 ms per move in our matches. A small network that runs the same trunk on each board separately, with shared weights, and lets the boards exchange information explicitly. RISE-style networks stack both boards as channels of one 8x8 image…

Networks for Hivemind, a UCI engine for Bughouse chess, trained to play on real clocks. The TwinBoard-S networks learned bughouse with a single time-advantage bit: one team may sit (wait instead of moving) for the whole game. This lineage continues from the last of them and learns from self-play on a simulated clock instead, where a team may sit only while it actually leads, and every action it takes while on turn on both boards costs it its own move time. Same TwinBoardNet (twin-s, no attention) as the TwinBoard-S networks, reading 39 planes per board (78 in all): the 37 planes of input representation version 3, plus two clock planes that both boards carry with the same values, from the…