Selective differential attention enhanced cartesian atomic moment machine learning interatomic potentials with cross-system transferability

· · 来源:user频道

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Hoare, C.A.R. “The Emperor’s Old Clothes.” Communications of the ACM 24(2), 1981. (1980 Turing Award Lecture)

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进一步分析发现,We have already explored the first part of the solution, which is to introduce provider traits to enable incoherent implementations. The next step is to figure out how to define explicit context types that bring back coherence at the local level.

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结合最新的市场动态,The BrokenMath benchmark (NeurIPS 2025 Math-AI Workshop) tested this in formal reasoning across 504 samples. Even GPT-5 produced sycophantic “proofs” of false theorems 29% of the time when the user implied the statement was true. The model generates a convincing but false proof because the user signaled that the conclusion should be positive. GPT-5 is not an early model. It’s also the least sycophantic in the BrokenMath table. The problem is structural to RLHF: preference data contains an agreement bias. Reward models learn to score agreeable outputs higher, and optimization widens the gap. Base models before RLHF were reported in one analysis to show no measurable sycophancy across tested sizes. Only after fine-tuning did sycophancy enter the chat. (literally)

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