{
  "id": 19170,
  "url": "https://arxiv.org/abs/2608.13317v1",
  "title": "StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems",
  "summary": "Large language model based multi-agent systems usually communicate in text, i.e., using discrete tokens. However, text introduces a discrete bottleneck. Converting the sender's continuous hidden states into discrete tokens discards information that token identities alone cannot capture. Recent work proposes latent communication as an alternative, where agents transmit hidden representations directly without converting them to text. However, existing latent methods either inject working memory la",
  "authors": "Yanwen Peng, Delvin Ce Zhang, Xi Wang, Nikolaos Aletras",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T14:40:59.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19170",
  "original_url": "https://arxiv.org/abs/2608.13317v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}