Reference

Evidence boundary

Measured

The training loop's collection throughput on this workstation, measured September 18, 2026 with stage timers and matched ten-iteration runs (report): the move network's forward pass is 96% of a collection step on the small preset, and enabling the ROCm build's experimental attention kernels (now the training package's default) lifts collection from 4,300 to 10,100 environment steps per second and halves iteration wall time from 73.5 to 35.0 seconds. The environment's share of collection is 2.0% before and 5.4% after; torch.compile of the forward is recorded as unsafe on this inductor build (NaN rows on a quarter to a half of real batches).

The engine's throughput on this workstation, in release mode on September 18, 2026: 17 ns per applied move including the end-of-game check, 71 ns per legal-move generation, 4.5 million random plies per second on one core with the competitive rules, and 84 million random plies per second on all 32 threads. The state is 440 bytes. These numbers are from cargo run --release -p armies-engine --bin bench and are repeatable.

The engine's rules are covered by tests in ../server/crates/engine/tests: movement, battles, the two-square rule including the Scout extension, the continuous-chase rule with a twelve-threat lap around a lake, the draw rules, and generator-checker agreement over thousands of random positions.

Three registered experiments were run on September 18, 2026, each with a record and a report:

  • Baseline arena: greedy scored 90.2% effective against random on 200 paired games (Wilson 85.4% to 93.6%); the random self-match covered 50%. Decision rule met. Report.
  • Expectimax (16 worlds, depth 2) scored 69.5% against greedy on 200 paired games, per-seed contrast 0.39 (0.24 to 0.54), 9.8 ms per move. Decision rule met; expectimax is the current top of the measured ladder. Report.
  • Self-play smoke: twenty iterations trained without entropy collapse, but the network's 60% against random (44.6% to 73.7%) covers 50% and it stalls into battleless draws. Decision rule not met. Report.

Two more registrations ran later the same day, each with records in the replay library:

  • Expectimax samples-by-depth sweep: nine cells of 100 paired games against greedy. Every cell beat 50% by the point estimate; the best was sixteen worlds at depth 2 at 67.5% (57.8% to 75.9%) and 9.5 ms per move. Neither the depth claim nor the worlds claim reached separated intervals. Report.
  • Rollout rung: flat Monte Carlo with the registry defaults scored 19% (12.5% to 27.8%) against that expectimax cell on 100 paired games, per-seed contrast minus 0.62 (minus 0.81 to minus 0.43). Not promoted. Report.

The ladder asset carries these games and an Elo fit over the pairwise scores with random anchored at 1000 (greedy 1393, expectimax 1535, rollout 1283, the smoke network 1090); the n-tuple rung is unmeasured.

Setups are uniform draws for every engine player, and that costs games: on 1,200 unregistered tournament games (exploratory, raw output) a side with a front-row Flag lost 61.5% against 32.7% with the Flag on the back row. The initial placements approach registers the setup-only contrast; nothing is measured under a registration yet.

The replay library (records/games/, 64 records, 1.7 MB) holds six games for every pair of random, greedy, expectimax and rollout, seed 0 from both seats for every sweep cell, and seeds 0 to 4 from both seats of the rollout match. Every record replays through the engine.

Registered, not run

Nothing. The next registrations are the n-tuple leaf comparison against the 16 by 2 cell and a scaled self-play run.

Planned

The n-tuple rung, the belief network, the full Ataraxos preset and the DeepNash comparison are described on their approach pages with decision rules but have no registration. The research program adds nine proposed studies on the evening of September 18, 2026: knowledge-limited subgame solving against expectimax, the opponent portfolio, the learned belief as a sampler, update-equivalence search on the small checkpoint, the scaled run, the training loop's throughput, the run layout and the monitor, and the NashDreamer infoset model. Each is delegated to an agent that registers before it runs; none has a registration yet. The monitor study is complete the same evening as an engineering deliverable: armies-watch with its tests, no player strength measured.

Numbers from the papers

Every figure or table transcribed from the Ataraxos or DeepNash papers says so in its caption. The setup heatmap on the setup-network page is illustrative and encodes only the paper's qualitative description.