{
  "id": 18725,
  "url": "https://arxiv.org/abs/2608.11410",
  "title": "Unmasking Toxic Mimicry in Medical Offline Reinforcement Learning for ICU Sepsis Management via Counterfactual Clinical Audits",
  "summary": "arXiv:2608.11410v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) offers considerable promise for optimizing ICU treatment decisions, yet standard evaluation metrics Mean Squared Error (MSE) and Fitted Q-Evaluation (FQE) assess only behavioral imitation and cannot detect Toxic Mimicry, a failure mode in which agents replicate harmful patterns such as treatment withdrawal during comfort-care transitions. Using the MIMIC-III database, we propose the Counterfactual Clinical Audi",
  "authors": "Hangqi Ren, Junyi Liao",
  "category": "research",
  "topics": "healthcare,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T04:00:00.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "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/18725",
  "original_url": "https://arxiv.org/abs/2608.11410",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}