{
  "id": 6195,
  "url": "https://arxiv.org/abs/2604.07003v2",
  "title": "EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration",
  "summary": "Large language models (LLMs) has been widely used for automated negotiation, but their high computational cost and privacy risks limit deployment in privacy-sensitive, on-device settings such as mobile assistants or rescue robots. Small language models (SLMs) offer a viable alternative, yet struggle with the complex emotional dynamics of high-stakes negotiation. We introduces EmoMAS, a Bayesian multi-agent framework that transforms emotional decision-making from reactive to strategic. EmoMAS lev",
  "authors": "Yunbo Long, Yuhan Liu, Liming Xu",
  "category": "research",
  "topics": "privacy-surveillance,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-08T12:20:06.000Z",
  "fetched_at": "2026-07-14T16:32:20.054Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6195",
  "original_url": "https://arxiv.org/abs/2604.07003v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}