{
  "id": 11749,
  "url": "https://arxiv.org/abs/2607.15591",
  "title": "RecGPT-V3 Technical Report",
  "summary": "Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecGPT-V1 pioneered this paradigm on Taobao by centering user understanding, and RecGPT-V2 scaled it via coordinated multi-agent reasoning; both are deployed in production with consistent gains in user experience and commercial outcomes. However, operating RecGPT at scale reveals three challenges: (1) stateless behavior mo",
  "authors": "Bowen Zheng, Chao Yi, Dian Chen, Gaoyang Guo, Han Zhu, Jiakai Tang",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-16T20:00:00.000Z",
  "fetched_at": "2026-07-20T05:10:09.534Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/11749",
  "original_url": "https://arxiv.org/abs/2607.15591",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}