Unverified50% confidenceBenchmarkExact time
In 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.
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Long-term
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10/16/2026
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UnverifiedAfter 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).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.76% 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.75% similarUnverifiedAfter 15 training epochs on the demonstration dataset, the text classification model achieves an accuracy of 99%, with a total of 110,000 model parameters (16,000 for Attention, 33,000 for FFN, and the remainder for Embedding)71% similarVerifiedIn a reproduction experiment training for 40 epochs on CIFAR-10, plain-56 achieved a training accuracy of 84.0% and plain-20 achieved a training accuracy of 95.1%, even though plain-56's parameter count (853,018) is approximately three times that of plain-20 (269,722).69% similar
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