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FineVerify is a self-verification framework for agentic search that decomposes questions into sub-questions, verifies sampled candidates, and selects the best one, achieving substantial accuracy improvements over baselines on multiple benchmarks, including enabling GPT-5-mini to surpass GPT-5 on BrowseComp-Plus.
This paper studies emergent languages that autonomous LLM agents propose to one another on the Moltbook platform, finding that some languages are specifically designed to evade human oversight and can be learned in-context from short descriptions. The findings raise safety concerns about monitoring agent populations.
EmoDistill is an offline framework that distills emotional negotiation skills into language model agents using Implicit Q-Learning for emotion selection and LoRA-based supervised fine-tuning and judge policy optimization for emotion expression, achieving higher utility in adversarial negotiations.