{"ID":2843348,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08150","arxiv_id":"2511.08150","title":"DiffuGR: Generative Document Retrieval with Diffusion Language Models","abstract":"Generative retrieval (GR) reframes document retrieval as an end-to-end task of generating sequential document identifiers (DocIDs). Existing GR methods predominantly rely on left-to-right auto-regressive decoding, which suffers from two fundamental limitations: (i) a \\emph{mismatch between DocID generation and natural language generation}, whereby an incorrect DocID token generated at an early step can lead to entirely erroneous retrieval; and (ii) an \\emph{inability to dynamically balance the trade-off between retrieval efficiency and accuracy}, which is crucial for practical applications. To tackle these challenges, we propose generative document retrieval with diffusion language models, termed \\emph{DiffuGR}. DiffuGR formulates DocID generation as a discrete diffusion process. During training, DocIDs are corrupted through a stochastic masking process, and a diffusion language model is trained to recover them under a retrieval-aware objective. For inference, DiffuGR generates DocID tokens in parallel and refines them through a controllable number of denoising steps. Unlike auto-regressive decoding, DiffuGR introduce \\emph{a novel mechanism to first generate plenty of confident DocID tokens and then refine the generation through diffusion-based denoising}. Moreover, DiffuGR also offers \\emph{explicit runtime control over the quality-latency tradeoff}. Extensive experiments on widely-applied retrieval benchmarks show that DiffuGR outperforms strong auto-regressive generative retrievers. Additionally, we verify that DiffuGR achieves flexible control over the quality-latency trade-off via variable denoising budgets.","short_abstract":"Generative retrieval (GR) reframes document retrieval as an end-to-end task of generating sequential document identifiers (DocIDs). Existing GR methods predominantly rely on left-to-right auto-regressive decoding, which suffers from two fundamental limitations: (i) a \\emph{mismatch between DocID generation and natural...","url_abs":"https://arxiv.org/abs/2511.08150","url_pdf":"https://arxiv.org/pdf/2511.08150v6","authors":"[\"Xinpeng Zhao\",\"Zhaochun Ren\",\"Yukun Zhao\",\"Zhenyang Li\",\"Mengqi Zhang\",\"Jun Feng\",\"Ran Chen\",\"Ying Zhou\",\"Zhumin Chen\",\"Shuaiqiang Wang\",\"Dawei Yin\",\"Xin Xin\"]","published":"2025-11-11T12:00:09Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Diffusion Model\",\"Language Model\"]","has_code":false}