Unverified50% confidenceBenchmarkExact time
The text-only baseline model achieved a decision accuracy of 33.33%, which improved to 93.33% after introducing dual evidence tokens.
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50%
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Long-term
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10/5/2026
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UnverifiedCompared to standard LoRA, counterfactual mDPO improved decision accuracy from 93.33% to 96.67%.79% similarUnverifiedThe BF16 precision model's baseline accuracy was 74%, which improved to 84% after applying the penalty, with reasoning tokens reduced by 19.4%.74% similarUnverifiedWhen using the multi-agent verification process, MARCH's accuracy was suppressed to 4.4%, whereas when the same model was directly asked which solution was better, accuracy reached 43.7%.73% similarUnverifiedPrecision bias refers to the tendency for people to assign higher credibility to statements containing precise numbers; experiments show that subjects rated the credibility of "41.3%" significantly higher than "approximately 40%".71% similarUnverifiedEvidence-driven pre-improvement validation improved precision from 58.6% to 72.3%, and reduced unsupported objectives per round from 1.93 to 0.50.70% similar
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