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In the complete markdown sweep, hard negatives lower accuracy more than length-matched random documents at both context sizes for gpt-5-mini, while random documents stay near the no-distractor baseline, and for gpt-5-mini hard negatives significantly underperform length-matched random distractors when pooled across con...
The results show that sycophancy can corrupt the reasoning chain independently of the final answer, so answer-level evaluation alone is insufficient, and a failure taxonomy separating reasoning-chain from answer-level sycophancy is introduced, and a complementary sentence-level taxonomy locating where in the chain drif...
It is indicated that RL-trained models not only demonstrate greater awareness of their learned behaviors and stronger generalizability to novel, structurally similar tasks than SFT models but also often exhibit weak alignment between their reasoning traces and final outputs, an effect most pronounced in GRPO-trained mo...
Foundation models such as GPT and Claude now solve olympiad-level mathematics with remarkable proficiency, so much so that geometry problem solving has become a standard proxy for their mathematical reasoning. Yet solving a geometry problem and drawing the figure it depends on are not the same skill: progress often hin...
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