Generative Retrieval

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3 papers in the last four weeks, down 57% on the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 27

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A Systematic Study of Semantic ID Spaces for Generative Information Retrieval
Oct 6, 2026Alexia Allal, Hicham Randrianarivo, Sylvain LamprierResidual VQ Vector Quantization
Artefact Research Center Paris, France · LERIA, Angers University Angers, France
Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval
Oct 6, 2026Hicham Randrianarivo, Logan Renaud, Alexia AllalGenerative Retrieval Memorization in Generative Models
Artefact Research Center France
UniPolicy: Unified Objective-Specific Policies for Generative Search Advertising
Sep 17, 2026Kun Yao, Yuhang Zhou, Yichi Zhang +6Online Advertising Generative Retrieval
Meituan, Beijing, China
Generative Retrieval for Unsupervised Text-Based Person Search
Sep 14, 2026Mang Ye, Yucheng Ji, Yang Bai +4Unsupervised Learning Image-Text Retrieval
School of Computer Science, Wuhan University, Wuhan, China · School of Computer Science and Technology, Soochow University, SuZhou, China · Zhipu AI, Beijing 100089, China +1
Exploring Bottom-Up Clustering for Creating Semantic IDs
Sep 8, 2026Leah Woldemariam, Sudhanshu Garg, Taha Belkhouja +2Generative Retrieval
Department of Electrical and Computer Engineering Cornell Tech, Cornell University New York, NY, USA · PayPal San Jose, California, USA
STAIR (STructure Aware Information Retriever): A novel dataset and LLM based retriever for document structure augmentation
Sep 3, 2026Vineet Kumar, Meghanadh Pulivarthi, vishwajeet kumar +3Retrieval-Augmented Generation Generative Retrieval
IBM
WIDE: Wildcard Inference with Dynamic Expansion for Cross-Modal Generative Retrieval
Sep 3, 2026Teng Guo, Xin Wang, Jiayou Xu +3Generative Retrieval Information Retrieval
It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning
Sep 1, 2026Runpeng Dai, Kaili Huang, Changsung Kang +1Reinforcement Learning Generative Retrieval
University of North Carolina at Chapel Hill · Apple
Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster
Aug 31, 2026Songtao Fang, Zihao Xu, Shaowei Wei +2Generative Retrieval Information Retrieval
Alibaba Group HangZhou, CHINA
Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval
Aug 31, 2026Gaoming Zhang, Angqing Jiang, Jianchun Song +4Generative Retrieval Autoregressive Generation
University of Science and Technology of China · Meituan
PRQ-KMeans: Projection Residual Quantization for Semantic ID Tokenization
Aug 25, 2026Yunxiao Luo, Siyuan Wang, Ben Chen +2Residual VQ Generative Retrieval
1Kuaishou Technology
Generative Universal Multimodal Retrieval with Dual-role Identifiers
Aug 13, 2026Kaipeng Li, Haitao Yu, Xuanchen ZhouMultimodal IR Generative Retrieval
Independent Researcher, Tsukuba, Ibaraki, Japan · Institute of Library, Information and Media Science, University of Tsukuba, Tsukuba, Ibaraki, Japan · College of Knowledge and Library Sciences, University of Tsukuba, Tsukuba, Japan
SurgNarrator: A Generative Retrieval Framework for Surgical Video Understanding
Aug 5, 2026Yuqing Feng, Jiawei Ma, Kevin Qinghong Lin +6Surgical Video Understanding Generative Retrieval
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, U.K. · Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China · Department of Engineering, University of Oxford, Oxford OX1 3PJ, U.K. +2
UniGD: A Unified Generative-Discriminative Framework for Industrial Retrieval
Aug 4, 2026Shujie Ji, Yawei Kong, Yilin Zhao +3Learning to Rank Online Advertising
Kuaishou Technology, Beijing, China
Generative Chinese Statute Retrieval
Jul 13, 2026Yiteng Tu, Zitao Su, Weihang Su +5Legal IR Generative Retrieval
Tsinghua University Quancheng Laboratory Beijing, China
FlashTrie: A GPU-Accelerated Constrained Beam Search for Generative Retrieval
Jul 10, 2026Dakshitha Anandakumar, Anurag Mukkara, Wenxiang Hu +5Constrained Decoding GPU Kernel Optimization
Microsoft, Redmond, WA, USA · Nvidia, Santa Clara, CA, USA
MORPH: Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization
Jun 25, 2026Chenghao Liu, Yu Zhang, Zhongtao Jiang +8Generative Retrieval Preference Optimization
Peking University
ICICLE: Expanding Retrieval with In-Context Documents
May 26, 2026Yu-Chen Den, Yung-Yu Shih, Zhi Rui Tam +4In-Context Retrieval Generative Retrieval
1National Taiwan University, Taipei, Taiwan · 2Johns Hopkins University, Baltimore, US
Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search
May 18, 2026Yifan Wang, Yixuan Wang, YiDan Liang +2Cold-Start Recommendation E-Commerce Search
Taobao & Tmall Group of Alibaba · HangZhou, China
Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
May 14, 2026Jianbo Zhu, Xing Fang, Jing Wang +5Semantic ID Recommendation E-Commerce Search
Taobao & Tmall Group of Alibaba Hangzhou, China
Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact
May 13, 2026Haofei Xu, Umar Iqbal, Jacob M. MontgomeryCitation Verification Generative Retrieval
Washington University in St. Louis
ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging
May 12, 2026Neha Verma, Nikhil Mehta, Shao-Chuan Wang +7Continual Learning for LLMs LLM Fine-Tuning
Johns Hopkins University · Google DeepMind · Google
How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews
Apr 30, 2026Riley Grossman, Songjiang Liu, Michael K. Chen +3Generative Engine Optimization Generative Retrieval
New Jersey Institute of Technology Newark, NJ, USA · Nanyang Technological University Singapore, Singapore · Indiana University Bloomington Bloomington, IN, USA
Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval
Apr 25, 2026Kidist Amde Mekonnen, Yongkang Li, Yubao Tang +2Language Model Decoding Generative Retrieval
University of Amsterdam Amsterdam, The Netherlands
A Parametric Memory Head for Continual Generative Retrieval
Apr 25, 2026Kidist Amde Mekonnen, Yubao Tang, Maarten de RijkeMemory-Augmented Neural Networks LLM Memory
University of Amsterdam Amsterdam, The Netherlands
GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
Apr 16, 2026Yanyan Zou, Junbo Qi, Lunsong Huang +7Generative Retrieval Recommender Systems
JD.com Beijing, China · Waseda University Tokyo, Japan
Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators
Feb 26, 2026Zhengyang Su, Isay Katsman, Yueqi Wang +10Constrained Decoding Generative Retrieval
Google Mountain View, CA, USA · Yale University New Haven, CT, USA

Oct 6, 2026, cs.IRJ/K move · Enter open · S save

A Systematic Study of Semantic ID Spaces for Generative Information Retrieval

Alexia Allal, Hicham Randrianarivo, Sylvain Lamprier

Artefact Research Center Paris, France · LERIA, Angers University Angers, France

Residual VQ Vector Quantization Generative Retrieval IR Evaluation Product Quantization

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Generative Information Retrieval (GIR) has emerged as a transformative paradigm, shifting document retrieval from a traditional "retrieve-and-rank" workflow to sequence-to-sequence generation, where a model directly predicts document identifiers (DocIDs). While the semantic design of these DocIDs is known to be critical for performance, a fundamental question remains under-explored: what makes a good DocID? Current approaches rely heavily on computationally expensive downstream evaluations, hindering systematic analysis and rapid iteration. In this work, we address this challenge by presenting a comprehensive study on the properties, metrics, and trade-offs that define effective numerical DocIDs. Specifically, our contributions are threefold: First, we propose a unified framework that unifies Product Quantization (PQ) and Residual Quantization (RQ), and their hybrid variants within a single design space. This enables us to systematically study key DocID properties, such as hierarchy versus parallelism, as well as the impact of hyperparameters like DocID length and codebook size. Second, we define a suite of training-free, intrinsic metrics, to quantify DocID quality and evaluate structural fidelity without the overhead of full model training. Through extensive experiments on MS MARCO 300K and NQ320K, we analyze how these structural properties influence retrieval effectiveness.

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