{
  "id": 5539,
  "url": "https://arxiv.org/abs/2604.19548v1",
  "title": "Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment",
  "summary": "Large Language Model agents have rapidly evolved from static text generators into dynamic systems capable of executing complex autonomous workflows. To enhance reliability, multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing. While such role-playing effectively leverages domain expert knowledge, we find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry (AOA). Specifically, an agent acting ",
  "authors": "Bobo Li, Rui Wu, Zibo Ji, Meishan Zhang, Hao Fei, Min Zhang et al.",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-21T15:05:58.000Z",
  "fetched_at": "2026-07-14T16:31:48.876Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5539",
  "original_url": "https://arxiv.org/abs/2604.19548v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}