{
  "id": 6358,
  "url": "https://arxiv.org/abs/2604.03955v1",
  "title": "Symbolic-Vector Attention Fusion for Collective Intelligence",
  "summary": "When autonomous agents observe different domains of a shared environment, each signal they exchange mixes relevant and irrelevant dimensions. No existing mechanism lets the receiver evaluate which dimensions to absorb. We introduce Symbolic-Vector Attention Fusion (SVAF), the content-evaluation half of a two-level coupling engine for collective intelligence. SVAF decomposes each inter-agent signal into 7 typed semantic fields, evaluates each through a learned fusion gate, and produces a remix --",
  "authors": "Hongwei Xu",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-05T04:10:15.000Z",
  "fetched_at": "2026-07-14T16:32:24.293Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6358",
  "original_url": "https://arxiv.org/abs/2604.03955v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}