{
  "id": 3740,
  "url": "https://arxiv.org/abs/2605.25749v1",
  "title": "DeGRe: Dense-supervised Generative Reranking for Recommendation",
  "summary": "In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lies in exploring optimal sequences within an exponentially large permutation space. Recent studies have shifted towards end-to-end generative frameworks, which typically leverage list-wise rewards or preference alignment to guide generator training. However, these methods still face two critical issues. First is the heuristic label bias. Existing met",
  "authors": "Chaotian Song, Jingyao Zhang, Chenghao Chen, Zisen Sang, Dehai Zhao, Guodong Cao et al.",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-25T12:00:42.000Z",
  "fetched_at": "2026-07-14T16:30:27.611Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3740",
  "original_url": "https://arxiv.org/abs/2605.25749v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}