{
  "id": 3688,
  "url": "https://arxiv.org/abs/2605.26785v1",
  "title": "EmoDistill: Offline Emotion Skill Distillation for Language Model Agents in Adversarial Negotiation",
  "summary": "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, ",
  "authors": "Yunbo Long, Haolang Zhao, Lukas Beckenbauer, Liming Xu, Alexandra Brintrup",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-26T09:54:53.000Z",
  "fetched_at": "2026-07-14T16:30:27.608Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3688",
  "original_url": "https://arxiv.org/abs/2605.26785v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}