{
  "id": 44,
  "url": "https://arxiv.org/abs/2607.10411v1",
  "title": "Mitigating LLM Sycophancy in Code Smell Detection Using Evidence-Guided Reasoning Prompts",
  "summary": "Large Language Models (LLMs) are increasingly used for code smell detection tasks due to their ability to interpret program semantics. However, their reliability in this context remains poorly explored, particularly under varying prompt conditions where model predictions may be influenced by external cues rather than code characteristics. One such limitation is sycophancy bias, where models tend to align their outputs with user-provided assumptions instead of performing objective analysis. In th",
  "authors": "Istiaq Ahmed Fahad, Kamruzzaman Asif, Md. Nurul Ahad Tawhid",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-11T17:35:25.000Z",
  "fetched_at": "2026-07-14T14:14:15.664Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/44",
  "original_url": "https://arxiv.org/abs/2607.10411v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}