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Extended reasoning modes paradoxically degrade an LLM's ability to assess evidence sufficiency in dynamic diagnosis, driving models to prematurely lock into diagnoses that succeed only by chance.
Surviving modules can autonomously repair a damaged spacecraft, achieving over 80% connectivity even under severe fault conditions.
A novel U-Net framework significantly boosts extreme precipitation forecast accuracy, turning previously negligible predictions into operationally valuable insights.
Clinical agents face a staggering 62.3% accuracy ceiling in complex EHR reasoning, revealing significant gaps in current AI capabilities.
SlimSearcher cuts tool-call rounds by up to 58% without sacrificing accuracy, redefining efficiency in web agent training.
Forget static user profiles – LATTE forecasts where a user's preferences are *going*, not just where they've been, boosting personalized LLM generation.
Separating long-term preferences from short-term intentions via self-supervision boosts session-based recommendation, outperforming standard fusion methods.
