Emotion2Skill: Model-Internal Emotion Signals for Adaptive Skill Selection and Evolution

arXiv:2608.09248v3 Announce Type: replace Abstract: Skill-based LLM agents select reusable procedures from external libraries to solve complex tasks, yet their routing decisions rely primarily on task descriptions, verbal reflections, and experience-derived rules, without explicitly observing the model's internal decision state. Recent interpretability work has identified emotion representations that influence model behavior, but their use has largely focused on post-hoc analysis and output steering. Their potential to guide agent-level decisions remains underexplored. We propose Emotion2Skill, a framework that extracts LLM-internal emotion vectors and incorporates them into both skill selection and skill evolution. The framework uses these representations to inform skill choice and tracks their changes to identify problematic invocations, enabling targeted skill refinement beyond coarse outcome feedback. Across two backbones, Emotion2Skill outperforms seven baselines. On Qwen3-8B, it exceeds Trace2Skill by 3.1 percentage points in WebShop success and 5.7 in ALFWorld average success. Ablations and specificity controls support the utility of context-aligned emotion signals, while co-activation analysis reveals semantically coherent emotion--skill associations. The same emotion vectors also transfer to MATH and MBPP without re-extraction. These results establish LLM-internal emotion representations as an effective decision-level signal for orchestrating agent skill systems, extending their utility beyond interpretability and output steering. Code is available at https://github.com/BoHan-LIN04/Emotion2Skill

Abstract:Skill-based LLM agents select reusable procedures from external libraries to solve complex tasks, yet their routing decisions rely primarily on task descriptions, verbal reflections, and experience-derived rules, without explicitly observing the model's internal decision state. Recent interpretability work has identified emotion representations that influence model behavior, but their use has largely focused on post-hoc analysis and output steering. Their potential to guide agent-level decisions remains underexplored. We propose Emotion2Skill, a framework that extracts LLM-internal emotion vectors and incorporates them into both skill selection and skill evolution. The framework uses these representations to inform skill choice and tracks their changes to identify problematic invocations, enabling targeted skill refinement beyond coarse outcome feedback. Across two backbones, Emotion2Skill outperforms seven baselines. On Qwen3-8B, it exceeds Trace2Skill by 3.1 percentage points in WebShop success and 5.7 in ALFWorld average success. Ablations and specificity controls support the utility of context-aligned emotion signals, while co-activation analysis reveals semantically coherent emotion--skill associations. The same emotion vectors also transfer to MATH and MBPP without re-extraction. These results establish LLM-internal emotion representations as an effective decision-level signal for orchestrating agent skill systems, extending their utility beyond interpretability and output steering. Code is available at this https URL

Artificial Intelligence (cs.AI)

arXiv:2608.09248 [cs.AI]

(or arXiv:2608.09248v3 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.09248 Focus to learn more arXiv-issued DOI via DataCite

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Emotion2Skill: Model-Internal Emotion Signals for Adaptive Skill Selection and Evolution

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