New framework uses LLM internal emotions to improve agent skill selection

Researchers have developed Emotion2Skill, a novel framework that leverages internal emotion signals from large language models (LLMs) to enhance the performance of skill-based agents. This method extracts a 27-dimensional emotion state from the LLM's residual stream and integrates it 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 improved task success rates. Experiments on the WebShop and ALFWorld benchmarks using Qwen3-8B and Qwen3-14B models demonstrated significant improvements over baseline methods. AI

IMPACT This research could lead to more robust and adaptive AI agents by enabling them to leverage internal states for better decision-making.

RANK_REASON The cluster contains an academic paper detailing a new method for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• ALFWorld
• Emotion2Skill
• Qwen3 14B
• Qwen3_8B
• Webshop

• paper
• model release

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