{
  "id": 12971,
  "url": "https://arxiv.org/abs/2607.19985v1",
  "title": "Coordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing",
  "summary": "Dynamic manufacturing environments require multi-agent systems to coordinate effectively under frequent operational disturbances such as machine failures, urgent job arrivals, and processing time variations. Existing multi-agent reinforcement learning approaches treat each disturbance episode independently, discarding valuable coordination experience that could accelerate future adaptation. In this paper, we propose a Graph-Structured Experiential Memory (GSEM) framework for multi-agent coordina",
  "authors": "Chengxiao Dai, Zhanhui Lin, Zhaokun Yan, Youyang Ni, Chenjun Lei, Luyan Zhang",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-22T10:17:53.000Z",
  "fetched_at": "2026-07-23T05:10:49.458Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/12971",
  "original_url": "https://arxiv.org/abs/2607.19985v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}