{
  "id": 7266,
  "url": "https://arxiv.org/abs/2603.13189v1",
  "title": "LLM Constitutional Multi-Agent Governance",
  "summary": "Large Language Models (LLMs) can generate persuasive influence strategies that shift cooperative behavior in multi-agent populations, but a critical question remains: does the resulting cooperation reflect genuine prosocial alignment, or does it mask erosion of agent autonomy, epistemic integrity, and distributional fairness? We introduce Constitutional Multi-Agent Governance (CMAG), a two-stage framework that interposes between an LLM policy compiler and a networked agent population, combining ",
  "authors": "J. de Curtò, I. de Zarzà",
  "category": "research",
  "topics": "bias-fairness,regulation,safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-13T17:21:26.000Z",
  "fetched_at": "2026-07-14T16:33:08.010Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7266",
  "original_url": "https://arxiv.org/abs/2603.13189v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}