{
  "id": 6079,
  "url": "https://arxiv.org/abs/2604.09746v1",
  "title": "CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation",
  "summary": "As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent environments has become an important alignment challenge. We take a neutral empirical stance and construct a controlled environment in which strategic behavior can be directly observed and measured. We introduce a large-scale multi-agent simulation in a simplified model of New York City, where LLM-driven agents interact under opposing incentives. Blue agents",
  "authors": "Aarush Sinha, Arion Das, Soumyadeep Nag, Charan Karnati, Shravani Nag, Chandra Vadhan Raj et al.",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy,environment",
  "orgs": null,
  "regions": "us",
  "published_at": "2026-04-10T06:33:57.000Z",
  "fetched_at": "2026-07-14T16:32:15.634Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6079",
  "original_url": "https://arxiv.org/abs/2604.09746v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}