{
  "id": 7273,
  "url": "https://arxiv.org/abs/2604.16339v1",
  "title": "Semantic Consensus: Process-Aware Conflict Detection and Resolution for Enterprise Multi-Agent LLM Systems",
  "summary": "Multi-agent large language model (LLM) systems are rapidly emerging as the dominant architecture for enterprise AI automation, yet production deployments exhibit failure rates between 41% and 86.7%, with nearly 79% of failures originating from specification and coordination issues rather than model capability limitations. This paper identifies Semantic Intent Divergence--the phenomenon whereby cooperating LLM agents develop inconsistent interpretations of shared objectives due to siloed context ",
  "authors": "Vivek Acharya",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-13T14:55:38.000Z",
  "fetched_at": "2026-07-14T16:33:08.011Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7273",
  "original_url": "https://arxiv.org/abs/2604.16339v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}