Unverified50% confidenceBenchmarkExact time
In the final round of experiments, the composite evaluation score improved by 0.0903, and zero-score conversations decreased by 40%.
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10/2/2026
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UnverifiedAblation analysis shows that fine-tuning on the segment paradigm yields a +5.29-point improvement on the test set, while replacing the first 4,500 transcript words with 2,000 retrieved words yields only a +1.55-point improvement.76% similarUnverifiedThe power-law exponent between test loss and compute is approximately 0.05, meaning that every 10x increase in compute yields approximately a 12% improvement in model performance.73% similarUnverifiedIn the experiments, the task resolution rate was 17% for zero-shot generation, 27% for generic self-correction, 23% for error-only feedback, and 90% for diagnostic counterexample feedback (A-CEGIS).72% similarUnverifiedAfter introducing a gating mechanism based on the measurement metrics, accuracy improved from 20.7% to 36.9%, while the system still answered half of all comparisons.70% similarUnverifiedWhen responses were frozen at the initial policy, increasing prompt breadth pulled accuracy down from 21.16% to 19.05%.69% similar
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