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Microsoft Research introduces EvoLib, a framework that enables LLMs to continually learn from their own experience during inference by extracting reusable skills and insights, without model updates or external labels.
This paper introduces EvoBrowseComp, a dynamic benchmark of 400 English and 400 Chinese complex questions that are synthesized via live-web traversal to evaluate search agents without test-set contamination, ensuring robustness against parametric memorization.
EvoBrowseComp is an evolving benchmark with 800 contamination-free questions for evaluating search agents, designed to prevent parametric memorization and maintain temporal freshness through a three-agent framework.