{
  "id": 5246,
  "url": "https://arxiv.org/abs/2604.25849v1",
  "title": "ADEMA: A Knowledge-State Orchestration Architecture for Long-Horizon Knowledge Synthesis with LLMAgents",
  "summary": "Long-horizon LLM tasks often fail not because a single answer is unattainable, but because knowledge states drift across rounds, intermediate commitments remain implicit, and interruption fractures the evolving evidence chain. This paper presents ADEMA as a knowledge-state orchestration architecture for long-horizon knowledge synthesis rather than as a generic multi-agent runtime. The architecture combines explicit epistemic bookkeeping, heterogeneous dual-evaluator governance, adaptive task-mod",
  "authors": "Zhou Hanlin, Chan Huah Yong",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-28T16:54:48.000Z",
  "fetched_at": "2026-07-14T16:31:35.576Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5246",
  "original_url": "https://arxiv.org/abs/2604.25849v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}