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Agent evolvers automate the design of the prompts, skills and workflows around language model agents, yet the optimization process they follow is still designed by hand: a fixed search loop decides how candidates are evaluated, which are kept and when the search stops. We propose FREEEVOLVE, which automates this proces...
While diffusion models excel at capturing complex data distributions, scientific discovery often requires steering generation toward specific, uncharacterized regions that maximize a target objective. These high-utility modes frequently reside in low-likelihood tail regions and are only revealed sequentially through in...
A critical limitation of existing confidence-based VF-TTS methods is demonstrated by showing that such methods catastrophically break down on complex tasks, and a novel selection framework, consilience, is introduced, a novel selection framework that explicitly evaluates the temporal asymmetry of confidence in reasonin...
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