{
  "id": 19040,
  "url": "https://arxiv.org/abs/2608.12062v1",
  "title": "Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations",
  "summary": "Developing dialogue systems capable of engaging in multi-turn, goal-oriented conversations remains a significant challenge, especially in specialized domains with limited data. This research proposes a novel framework called Preference Tree Optimization (PTO), designed to iteratively improve agent models in such dialogue systems, by generating preference data using a method called Preference Tree with Look-Ahead. Focusing on Motivational Interviewing (MI) -- a counseling technique aimed at facil",
  "authors": "Lior Baruch, Moshe Butman, Kfir Bar, Doron Friedman",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T13:48:30.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19040",
  "original_url": "https://arxiv.org/abs/2608.12062v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}