How Emotion Shapes the Behavior of LLMs and Agents: A Mechanistic Study
Emotion plays an important role in human cognition and performance. Motivated by this, we investigate whether analogous emotional signals can shape the behavior of large language models (LLMs) and agents. Existing emotion-aware studies mainly treat emotion as a surface-level style factor or a perception target, overlooking its mechanistic role in task processing. To address this limitation, we propose E-STEER, an interpretable emotion steering framework that enables direct representation-level intervention in LLMs and agents. It embeds emotion as a structured, controllable variable in hidden states, and with it, we examine the impact of emotion on objective reasoning, subjective generatio...
| Authors | Moran Sun, Tianlin Li, Yuwei Zheng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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