{
  "id": 3784,
  "url": "https://arxiv.org/abs/2605.24960v1",
  "title": "Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization",
  "summary": "Chain-of-Thought (CoT) faithfulness, i.e., whether CoTs genuinely reflect large language models' (LLM) underlying behavior, is typically evaluated under two disjoint paradigms: contextual faithfulness, measured by perturbing the input or CoT trace, and parametric faithfulness, assessed by intervening on a model's parametric knowledge. Yet prior work compares them only descriptively. We fill this gap by proposing FaithMate, a unified preference-alignment interface for optimizing models towards ei",
  "authors": "Jingyi Sun, Qianli Wang, Pepa Atanasova, Nils Feldhus, Isabelle Augenstein",
  "category": "research",
  "topics": "safety-alignment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-24T09:16:55.000Z",
  "fetched_at": "2026-07-14T16:30:31.920Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3784",
  "original_url": "https://arxiv.org/abs/2605.24960v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}