{
  "id": 17018,
  "url": "https://arxiv.org/abs/2608.05166",
  "title": "Conditional Cognitive Biases in LLMs: How Biased User Turns Modulate In-Context Reasoning",
  "summary": "arXiv:2608.05166v1 Announce Type: cross Abstract: We present an evaluation of cognitive bias expression in state-of-the-art instruction-tuned LLMs under realistic multi-turn interaction settings. Our work introduces a novel three-condition experimental framework that disentangles the effect of exposure to a biased user turn from the effect of the turn's semantic content, alongside a benchmark of 24,300 jury-validated user prompts spanning all 81 cells of a 9x9 target-human bias interaction matri",
  "authors": "Sachini Weerasekara, Sagar Kamarthi, Jacqueline Isaacs",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T04:00:00.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17018",
  "original_url": "https://arxiv.org/abs/2608.05166",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}