EmoDistill: Offline Emotion Skill Distillation for Language Model Agents in Adversarial Negotiation
Post-trained LLMs are often optimized to align responses with human preferences, making them safe, polite, and conversationally appropriate. In adversarial negotiation, however, this alignment can become a vulnerability: emotionally framed language may steer agents toward the counterparty's interests. Using GoEmotions-based affective prompting, we show that emotion substantially shifts negotiation outcomes, suggesting that emotion is a strategic action channel rather than a surface style. Thus,
Record details
Published: 26 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (26 May 2026), “EmoDistill: Offline Emotion Skill Distillation for Language Model Agents in Adversarial Negotiation,” evidence record 3688, https://ethics.ai/record/3688 (originally published by arXiv).
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