Unverified50% confidenceOpinionExact time
The gains in the evolution experiments come from targeted optimization of the diagnosed failure points, and their magnitude reflects the room for closing specific gaps rather than a broad improvement in general capability.
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11/2/2026
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UnverifiedThe experiments found that on most test instances the optimized schedule brought substantial performance gains, but in certain problem environments, an overly aggressive early elimination strategy instead incurred higher cost.79% similarUnverifiedThe sample efficiency of evolutionary strategies in high-dimensional parameter spaces is generally lower than that of gradient-based methods, and convergence speed and final accuracy may be limited.76% similarUnverifiedAs model capabilities improve, the tension between capability and alignment becomes increasingly pronounced, and the consequences of alignment failure are correspondingly amplified.76% similarUnverifiedExperimental validation throughput lags behind computational design capability by several orders of magnitude, becoming the rate-limiting step in the innovation cycle75% similarUnverifiedIgnoring error correlation causes the combined variance to be underestimated, and the underestimation factor grows with group size; this factor accurately predicts the degree of coverage failure.74% similar
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