Unverified50% confidenceBenchmarkExact time
After 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).
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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 = 0.052.77% similarUnverifiedIn 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.74% similarUnverifiedIn the configuration with eight players and two impostors using second-league bots, the trained network's crew win rate was 52.0%, with an average of 0.99 impostors ejected per round, whereas gpt-oss in the turn-based Q&A format won only 2 of 16 rounds (12.5%), ejected 0.06 impostors per round, and wrongly ejected 0.94 innocent crewmates.70% similarUnverifiedWhen sampling eight candidates without constraints from the model, 10.2% of inputs cannot produce any valid output under limited training data, and this proportion drops to 0.4% when using the full training data65% similarUnverified残差连接支撑了152层超深网络的训练，并在2015年ImageNet竞赛中以低于4%的错误率首次超越人类水平（约5%）64% similar
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