Unverified50% confidenceFactExact time
The paper points out that for unstable mappings, the leave-one-out method may produce large bias.
1
Sources
50%
Confidence
Long-term
Relevance
11/2/2026
First Seen
Sources
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Related Claims
UnverifiedBias introduced by a single defective data point can leak into seemingly unrelated capability dimensions, causing the model to simultaneously regress across multiple independent metrics such as deceptiveness, compliance, and risk aversion.77% similarUnverifiedCritics point out that the low-probability, high-impact framework has room for manipulation—probabilities can be arbitrarily lowered and impacts can be arbitrarily raised—making it easy to fall into Pascal's-wager-style argumentation traps.76% similarUnverifiedPrevious research has mostly attributed over-refusal to static representation overlap, where benign prompts and harmful prompts are too close to each other in the model's internal representation space.76% similarUnverifiedPerformance varies significantly across evidence structures, meaning conclusions based on a limited set of structures may be biased.74% similarUnverifiedThe paper has not yet disclosed the failure modes of weight adjustment in extreme scenarios, nor the generalization performance of the critic in out-of-distribution scenarios.74% similar
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