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Institute of Software, Chinese Academy of Sciences
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Forget text-dominance: Today's Omni-modal LLMs surprisingly favor visual inputs, creating new challenges for cross-modal reasoning.
LLMs exhibit a "Utopian bias" when simulating human behavior, converging towards an unrealistic "positive average person" and failing to capture individual differences and long-tail behaviors.
LLMs trained with reinforcement learning from verifiable rewards (RLVR) become overconfident in incorrect answers, but a simple fix鈥攄ecoupling reasoning and calibration objectives鈥攃an restore proper calibration without sacrificing accuracy.
