Unverified50% confidenceFactExact time
In the eight-player configuration, the win-rate gap between gpt-oss and the trained network had a one-sided Fisher exact test p = 0.0017, far beyond random fluctuation.
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50%
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Long-term
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10/16/2026
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UnverifiedIn the strictly comparable experiment with fifteen players and three impostors in the sixth league, the trained network's crew win rate over 300 rounds was 93.3%, while gpt-oss under the dialogue+intuition format won 16 of 20 rounds (80%), with a one-sided Fisher exact test p = 0.052.76% similarUnverifiedAfter adding the trained network's intuition to the eight-player configuration and using the turn-based Q&A format, gpt-oss as crew won 8 of 16 rounds, almost indistinguishable from the network baseline (p = 0.54).74% similarUnverifiedPerformance benchmark results are subject to statistical fluctuation, requiring multiple runs to take the median or P95/P99 percentiles for evaluation.71% similarUnverifiedThe figure "pretraining throughput reaching 170% of a single H100" is jointly affected by batch size, sequence length, model scale, precision format, and interconnect bandwidth, and is not a general conclusion; the ratio may change with a different model or hyperparameters.67% similarUnverifiedThe loss during training is calculated per token, and long samples contribute far more gradient than short samples, so a 10% share of rows can absolutely correspond to more than 50% of the token volume and gradient signal.67% similar
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