{
  "id": 5285,
  "url": "https://arxiv.org/abs/2604.25088v1",
  "title": "Cooperate to Compete: Strategic Coordination in Multi-Agent Conquest",
  "summary": "Language Model (LM)-based agents remain largely untested in mixed-motive settings where agents must leverage short-term cooperation for long-term competitive goals (e.g., multi-party politics). We introduce Cooperate to Compete (C2C), a multi-agent environment where players can engage in private negotiations while competing to be the first to achieve their secret objective. Players have asymmetric objectives and negotiations are non-binding, allowing alliances to form and break as players' short",
  "authors": "Abigail O'Neill, Alan Zhu, Mihran Miroyan, Narges Norouzi, Joseph E. Gonzalez",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-28T00:48:20.000Z",
  "fetched_at": "2026-07-14T16:31:40.218Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5285",
  "original_url": "https://arxiv.org/abs/2604.25088v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}