{
  "id": 12004,
  "url": "https://arxiv.org/abs/2607.17188v1",
  "title": "Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment",
  "summary": "Test-time scaling empowers Large Reasoning Models (LRMs) to tackle complex tasks via extensive Chain-of-Thought (CoT). However, this often induces the \"overthinking\" paradox, where redundant reasoning increases computational overhead without guaranteeing accuracy. Existing test-time efficiency optimization methods primarily fall into two categories: information-theoretic approaches, which are prone to \"deceptive convergence\" where low uncertainty masks hallucinations, and latent representation a",
  "authors": "Cheng Yan, Guangyang Ye, Wuyang Zhang, Fan Xu, Zhijun Fan, Xiang Xia et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-19T11:00:59.000Z",
  "fetched_at": "2026-07-21T05:10:12.656Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/12004",
  "original_url": "https://arxiv.org/abs/2607.17188v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}