{
  "id": 18809,
  "url": "https://arxiv.org/abs/2608.11354v1",
  "title": "Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces",
  "summary": "Modern recommender systems treat observed actions as reliable proxies for user preferences, yet interactions often reflect exploration or comparison rather than stable preference expression. As interfaces evolve from static layouts toward generative UIs and immersive extended reality (XR), the need for deeper, modality-agnostic user understanding grows: these adaptive environments must decide not only what to present but where, when, how prominently, and most importantly why a user acts. We prop",
  "authors": "Mengyu Chen, Feiyu Lu, Chun-Fu Chen, Lucas Vinh Tran, Jay Katukuri",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-11T18:58:28.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18809",
  "original_url": "https://arxiv.org/abs/2608.11354v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}