{
  "id": 10338674,
  "title": "G2MAF: Test-Time Gradient Guidance for Multi-Agent Flow Policies",
  "url": "https://urgent.news/2026/09/25/g2maf-test-time-gradient-guidance-for-multi-agent-flow-policies",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-25T13:57:02.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.31286v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Offline multi-agent reinforcement learning (MARL) learns cooperative policies from fixed datasets without further environment interaction and a learned policy is frozen at deployment. Such a frozen policy typically proposes a single joint action and executes it directly at deployment time. However, this one-shot deployment often commits to a suboptimal proposal, even when better nearby…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}