Researchers have developed Emotion2Skill, a novel framework that leverages internal emotion signals within Large Language Models (LLMs) to enhance the performance of skill-based agents. This method extracts 27-dimensional emotion vectors from the LLM's residual stream and integrates them into the agent's decision-making process for both skill selection and evolution. By analyzing emotion trajectories, Emotion2Skill can identify and correct problematic skill invocations, leading to significant improvements in task success rates. When tested with Qwen3-8B and Qwen3-14B models on the WebShop and ALFWorld benchmarks, Emotion2Skill demonstrated substantial gains over existing baselines. AI
IMPACT This research could lead to more robust and adaptive AI agents by enabling them to better utilize their internal states for decision-making.
RANK_REASON The cluster contains an academic paper detailing a new framework and its experimental results on benchmarks.
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