Where the engine can still go
An independent simulator audit found a bit-exact leaf memo that nearly doubles speed, a build-flag hazard, and three GPU paths that are not ready.
The overnight runs finished unattended and both answers are negative; they are recorded below the engine audit that occupied the small hours. The owner asked for a fresh audit of the board simulation: is there a way to evaluate boards in parallel on the GPU, or to make the existing mechanics cheaper?
The audit
A read-only subagent audited the fast engine's hot path and wrote
audit-06; Kimi K3 then
reviewed the audit adversarially, and the corrections are folded into it.
Every timing was taken on a machine running a 30-thread cohort, so the audit
claims ratios between arms run back to back, never absolute speeds.
A one-entry leaf memo is bit-exact and nearly doubles a decision
1.64x / 1.87x, indicativeInside one chance node the search evaluates the same move under five or seven sampled futures, and the only thing that differs between most of them is the next visible disc — which the leaf reads in exactly one term. Remembering the last board's features and recomputing that one term gives the same bits on every leaf (17,045 states compared, 0 mismatches) with identical actions, work, nodes and cache hits on 12 synthetic roots, for 1.64x (five strata) to 1.87x (seven) per decision; 1.93x with the flag change below. Kimi's review confirmed the argument and added the condition that makes it legal: the memo must sit below the search's work counter, or it becomes a new algorithmic candidate. The ratios are indicative until the port prints its hit rate on real seeds and survives thirty-way parallel execution.
-march=native silently changes the frozen leaf
17% of leaf valuesClang fuses multiply-adds when the target has FMA, and the leaf's dot product
then rounds differently. The three cohort builds are exact only because they
target baseline x86-64. One existing build script ships -march=native
unpinned, and a cross-build check showed its copy of the frozen leaf differs in
17.0% of values — while flipping none of 13 probe decisions, and its recorded
frozen arms reproduced an earlier 128-game cohort digit for digit, so no
recorded number is in doubt. -ffp-contract=off restores bit-identity and is
now pinned in the two build scripts this line of work owns; the third is the
owner's call.
GPU: not yet, and the experiment that would change that
Three verdicts, all accepted by the review. Batched leaf evaluation could be bit-exact in double precision but needs a record-and-replay driver, is capped near 2.4x per thread by Amdahl, and the integrated GPU's shader clock halves under the CPU load a cohort creates — do the CPU memo first, since it removes two thirds of the leaves a GPU would batch. Thousands of games in lockstep have easy RNG parity but a search that is not a GPU workload. A GPU-hosted neural leaf waits until a learned leaf beats the hand-written one on the CPU at fixed work. The first experiment for any of them: a HIP micro-benchmark of the leaf over the existing 225,183-state gate corpus, bit-compared to the CPU.
The memo is ported and gated: 1.58x / 1.63x on real positions
0 mismatches, hit rate 62-68%approaches/lifetime-objective/fast-engine-memo generates the memoised search
from the gated fast search by a checked substitution that also verifies the
memo sits below the work counter. On real probe positions the leaf is
bit-identical on 6,760 boards, the search agrees with the unchanged one on
every column, work count, node and cache hit over 210 moves (117.7 and 119.6
million leaf calls), and thread counts do not change results. The memo hits on
61.7% of leaf calls at five chance samples and 68.5% at seven, for interleaved
timing ratios of 1.58x and 1.63x — a little below the audit's synthetic roots,
and indicative until measured on an idle host. No running experiment was
rebuilt; the variant is there for the next cohort.
Morning: both overnight bets lost, cleanly
Evolving the leaf weights at depth 4 on fresh seeds
cost: 40 generations, 21,760 depth-4 games, one 64-game screenThe CMA-ES distribution mean after forty generations scored 289,720
against the frozen leaf's 320,020 on the 64 never-read held-out games:
paired delta −30,300 (bootstrap 95% lower bound −70,928, paired sd
197,434), W–T–L 28–0–36, both halves negative. The seven-sample transfer was
−47,119. Gate: fail (RS-20260822T120736Z-662b39ca). The population mean had
drifted below the control from generation 2 onward; Kimi's pre-run estimate
of a one-in-ten pass was the right prior. Fresh seeds per generation removed
overfitting, as designed — and left selection noise alone to steer, which at
32 games per candidate and a paired sd near 200,000 is a random walk. The
theory's claim is not supported for this optimiser and budget; the frozen
weights survive.
Survival instinct as a hard rule at the root
cost: 128 paired games x 3 armsTwo strengths of the owner's rule against the unchanged search on 128
never-read games (RS-20260822T233343Z-12becce9). The literal form —
never drop a 3 or higher where it can no longer clear vertically — is a
clear loss: −97,064 points and −25.65 moves (95% bounds −136,887 to
−59,500), overriding the search at 12.8% of decisions. The strict form —
refuse only when the landing is already entombed by its row — is a null:
−1,970 with bounds −27,738 to +22,313, overriding 1.97% of decisions, a hair
under the 2% coverage bar the protocol set, so it is recorded as inconclusive
by its own rule. The picture is consistent with the corpus analysis: the
search already avoids the placements that matter, and forbidding the rest
takes away columns it needed.
What survives from this line
The gated native policy on the leaderboard, the memoised engine (1.6x), the entombed-disc and pattern diagnostics, and the negative results themselves. If the rule is tried again it should be a soft penalty the search can weigh, not a constraint — and measured with the memo engine at seven strata.
Log entries are a narrative account written by the contributors listed above. They are not evidence records: run validity, scientific outcome and evidence tier live with the experiment and result records an entry refers to.