Learning to Rank

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22 papers in the last four weeks, up 38% on the four weeks before. 0.2% of all new papers.

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Latest papers 171

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ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression
Apr 24, 2026Xiaojie Ke, Shuai Zhang, Liansheng Sun +6Learning to Rank LLM Reranking
Qwen Applications Business Group of Alibaba
UniCVR: From Alignment to Reranking for Unified Zero-Shot Composed Visual Retrieval
Apr 22, 2026Haokun Wen, Xuemeng Song, Haoyu Zhang +3Cross-Modal Learning Learning to Rank
Harbin Institute of Technology (Shenzhen) Shenzhen, China · Southern University of Science and Technology Shenzhen, China · Pengcheng Laboratory Shenzhen, China +1
ACT: Anti-Crosstalk Learning for Cross-Sectional Stock Ranking via Temporal Disentanglement and Structural Purification
Apr 22, 2026Juntao Li, Liang ZhangDisentangled Representation Learning Quantitative Finance
School of Computing and Data Science, University of Hong Kong, Hong Kong SAR. · Thrust of Financial Technology, Hong Kong University of Science and Technology (Guangzhou), China.
SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization
Apr 21, 2026Bo-Jyun Wang, Ying-Jia Lin, Hung-Yu KaoLearning to Rank Text Summarization
Department of Computer Science and Information Engineering, National Cheng Kung University · Department of Artificial Intelligence, Chang Gung University · Artificial Intelligence Research Center, Chang Gung University +1
Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems
Apr 20, 2026Junyi Yao, Zihao Zheng, Jiayu LongAlgorithmic Auditing Learning to Rank
Washington University in St. Louis USA
Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment via LLM-Synthesized Data
Apr 20, 2026Zhaohua Liang, Zhilin Wang, Renjie Cao +1Learning to Rank Semantic Search
OpenJobs AI · San Francisico, CA, USA
CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
Apr 19, 2026Hansi Zeng, Liam Collins, Bhuvesh Kumar +2Reinforcement Learning Learning to Rank
Center for Intelligent Information Retrieval, University of Massachusetts Amherst · Snap Inc.
HeadRank: Decoding-Free Passage Reranking via Preference-Aligned Attention Heads
Apr 19, 2026Juyuan Wang, Chenxing Wang, Yuchen Fang +8Attention Head Analysis Self-Attention
Weixin Group, Tencent, China · 2South China University of Technology, Guangzhou, China
Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking
Apr 17, 2026Jun Feng, Jiahui Tang, Zhicheng He +5Retrieval-Augmented Generation Learning to Rank
Hefei University of Technology · University of Science and Technology of China
Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation
Apr 16, 2026Camilo Gomez, Pengyang Wang, Yanjie FuLearning to Rank
Dept. of Statistics & Data Science University of Central Florida Orlando, USA · Department of CIS, SKL-IOTSC University of Macau Macao, China · School of Computing and AI Arizona State University Tempe, USA
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Apr 2, 2026Yuhang Wu, Xiangqing Shen, Fanfan Wang +4Retrieval-Augmented Generation Reinforcement Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology, China · School of Intelligence Science and Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China
Beyond Logit Adjustment: A Residual Decomposition Framework for Long-Tailed Reranking
Apr 2, 2026Zhanliang Wang, Hongzhuo Chen, Quan Minh Nguyen +2Long-Tailed Classification Learning to Rank
University of Pennsylvania, Philadelphia, PA, USA · Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System
Feb 20, 2026Longfei Yun, Yihan Wu, Haoran Liu +11Learning to Rank
Meta
Aligning Language Model Benchmarks with Pairwise Preferences
Feb 2, 2026Marco Gutierrez, Xinyi Leng, Hannah Cyberey +3LLM Evaluation Learning to Rank
School of Data Science, University of Virginia · Imperial College London · Thomson Reuters Foundational Research +1
TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance
Oct 9, 2025Jianhui Yang, Yiming Jin, Pengkun Jiao +6RL for Language Models Reward Shaping
Tsinghua University Beijing, China · Taobao & Tmall Group of Alibaba Beijing, China · Taobao & Tmall Group of Alibaba Hangzhou, China +1
Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank
Sep 25, 2025Yiheng Tao, Yihe Zhang, Matthew Dearing +4LLM Serving Learning to Rank
University of Illinois Chicago, Chicago, IL, USA · Argonne National Laboratory, Lemont, IL, USA
Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
Aug 16, 2025Jenny Y. Huang, Yunyi Shen, Dennis Wei +1Pairwise Preference Learning LLM Evaluation
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology · MIT-IBM Watson AI Lab · IBM Research
Algorithmic Shortlisting in Participatory Budgeting
Aug 7, 2025Juan Ignacio Zambrano, Clément Contet, Jairo Gudiño-Rosero +3Learning to Rank Privacy-Preserving ML
IRIT · Université Toulouse Capitole · Université de Toulouse +3
GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
May 19, 2025Pengyue Jia, Seongheon Park, Song Gao +2Geospatial Representation Learning Learning to Rank
Department of Data Science, City University of Hong Kong · Department of Computer Sciences, University of Wisconsin-Madison · Department of Geography, University of Wisconsin-Madison
Pairwise Comparisons without Stochastic Transitivity: Model, Theory and Applications
Jan 13, 2025Sze Ming Lee, Yunxiao ChenPairwise Comparison Pairwise Preference Evaluation
Department of Statistics · London School of Economics and Political Science · London WC2A 2AE, United Kingdom
Personalized Execution Time Optimization for Billion-Scale Scheduled Jobs
Mar 11, 2022Yang Liu, Juan Wang, Idris Malik +7Learning to Rank
Meta Hacker Way, Menlo Park, CA, USA, 94025

Apr 24, 2026, cs.IRJ/K move · Enter open · S save

ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression

Xiaojie Ke, Shuai Zhang, Liansheng Sun, Yongjin Wang+5

Qwen Applications Business Group of Alibaba

Learning to Rank LLM Reranking LLM Inference Acceleration Dense Retrieval Context Compression

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Large language model (LLM) based listwise reranking has emerged as the dominant paradigm for achieving state-of-the-art ranking effectiveness in information retrieval. However, its reliance on feeding full passage texts into the LLM introduces two critical bottlenecks: the "lost in the middle" phenomenon degrades ranking quality as input length grows, and the inference latency scales super-linearly with sequence length, rendering it impractical for industrial deployment. In this paper, we present ResRank, a unified retrieval-reranking framework that fundamentally addresses both challenges. Inspired by multimodal LLMs that project visual inputs into compact token representations, ResRank employs an Encoder-LLM to compress each candidate passage into a single embedding, which is then fed alongside the query text into a Reranker-LLM for listwise ranking. To alleviate the misalignment between the compressed representation space and the ranking space, we introduce a residual connection structure that combines encoder embeddings with contextualized hidden states from the reranker. Furthermore, we replace the conventional autoregressive decoding with a one-step cosine-similarity-based scoring mechanism, eliminating the generation bottleneck entirely. ResRank is trained through a carefully designed dual-stage, multi-task, end-to-end joint optimization strategy that simultaneously trains the encoder and reranker, achieving learning objective alignment between retrieval and reranking while substantially reducing training complexity. Extensive experiments on TREC Deep Learning and eight BEIR benchmark datasets demonstrate that ResRank achieves competitive or superior ranking effectiveness compared to existing approaches while requiring zero generated tokens and processing only one token per passage, yielding a fundamentally better balance between effectiveness and efficiency.

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