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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Ranked by Position: Order Sensitivity as an Exploitable Attack Surface in LLM Listwise Recommenders
Jul 26, 2026Ge Zhang, Jingru Cheng, Huiyuan ChenLearning to Rank Adversarial Attacks
Stanford University · Independent Researcher
SalesLoop: Reinforcement Learning from Performance Feedback for Sales Lead Ranking
Jul 22, 2026Chenyu ZhangLearning to Rank
Li Auto Inc.
Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking
Jul 22, 2026Kailin Jiang, Lei Liu, Jian Xi +7Learning to Rank Rubric-Based Evaluation
University of Science and Technology of China · Yuanbao Team, Tencent · University of Chinese Academy of Sciences +1
GATE-3D: Geometry-Aware Test-time Adaptive Reranking for Open-Set 3D Shape Retrieval
Jul 21, 2026Hao Wu, Heyi Lin, Zilin Wang +3Learning to Rank Cross-Modal Retrieval
Hong Kong University of Science and Technology Hong Kong, China · Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China
Exposure-Based Reinforcement Learning to Rank
Jul 21, 2026Harrie Oosterhuis, Rolf Jagerman, Zhen Qin +1Reinforcement Learning Learning to Rank
University of Amsterdam · Google DeepMind
Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes
Jul 21, 2026Zhaoxuan Li, Jiale Yang, Yifei Lu +1Traveling Salesperson Problem Algorithm Selection
Duke Kunshan University, 8 Duke Avenue, Kunshan, 215316, China
When Does Machine Learning Beat Value Sorting? A Three-Dataset Diagnostic of Exposure-Weighted Shipment Prioritization
Jul 20, 2026Jize LiLearning to Rank Supply Chain Management
Boston University, United States
FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering
Jul 20, 2026Jijun Chi, Zhenghan Tai, Hanwei Wu +21Learning to Rank Financial QA
University of Toronto · McMaster University · University of California, Los Angeles +8
MagicSelector: Joint Optimization for Agent Tool Selection via Counterfactual Decomposition and Progressive Reranking
Jul 20, 2026HONOR Agentic Search Team, Zhengzong Chen, Lei Tang +27Learning to Rank LLM Planning
HONOR Agentic Search Team
SynH-Rank: Quality-Aware Code Search via Diverse Data Synthesis and Hierarchical Ranking Training
Jul 19, 2026Keyu Liang, Haoye Wang, Yanfu Yan +2Learning to Rank Software Engineering Benchmarks
College of Computer Science and Technology and The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou, China · Hangzhou City University Hangzhou, China · Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou, China
Evidence Interfaces Shape How Retrieval-Augmented Readers Use Support
Jul 19, 2026Junchi Liao, Jiawen Deng, Fuji RenMulti-Hop QA Learning to Rank
Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning
Jul 15, 2026Dingsu Wang, Filip Ryzner, Kelly He +12Reward Modeling Learning to Rank
Pinterest San Francisco, CA, USA
SlimPer: Make Personalization Model Slim and Smart
Jul 14, 2026Siqi Wang, Xianjie Chen, Shaofeng Deng +42Learning to Rank Memory-Efficient Inference
MMRM: A Multiplex Multimodal Representation Model for Product Ranking in E-commerce Search
Jul 13, 2026Zhen-Lin Chen, Maosen Sheng, Peng Lin +4Learning to Rank E-Commerce Search
JD.COM Beijing, China
Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
Jul 9, 2026Chaewon Lee, Seon-Ho Lee, Chang-Su KimLearning to Rank Noisy-Label Learning
School of Electrical Engineering, Korea University, Seoul, Korea · Amazon AGI, Seattle, USA
InfluMatch: Frontier-Quality KOL Search at 4B-Model Cost
Jul 7, 2026Krittanon Kaewtawee, Petmongkon Pornpichitsuwan, Natchaya Temyingyong +4Learning to Rank Cost-Aware Inference
Amity AI Holdings Co.,Ltd.
Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking
Jul 7, 2026Manning Gao, Tingyi Liu, Leheng Zhang +3Representation Learning Learning to Rank
South China Normal University, China · Sun Yat-sen University, China
AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking
Jul 7, 2026Zhiyuan Chen, Jing Hu, Junzhe Wang +4Learning to Rank Meta-Learning
Henlius Shanghai, China
Be Indiscrete: The Benefits of Learning Continuous Spine Degeneration Severity Scores
Jul 6, 2026Maria Monzon, Andrew Zisserman, Robin Y. Park +2Learning to Rank Medical Diagnosis
Biomedical Data Science Lab, Dept. D-HEST, ETH Zurich, Zurich, Switzerland · Swiss Institute of Bioinformatics (SIB), Lausanne, 1015, Switzerland · Visual Geometry Group, Dept. of Engineering Science, University of Oxford, UK
Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls
Jul 3, 2026Kirill Shevkunov, Andrey Ploskonosov, Liudmila ProkhorenkovaLearning to Rank Information Retrieval
1Yandex.
Ask the Right Comparison:Bias-Aware Bayesian Active Top- $k$ Ranking with LLM Judges
Jul 2, 2026Jian Xu, Delu Zeng, John Paisley +1Pairwise Comparison LLM-as-a-Judge
RIKEN iTHEMS · RIKEN AIP · South China University of Technology +1
How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies
Jun 29, 2026Jhon G. Botello, Jose J. Padilla, Erika Frydenlund +2Text Embeddings Learning to Rank
Dept. of Computer Science, Old Dominion University, Norfolk, VA, USA · Virginia Modeling, Analysis, and Simulation Center, Old Dominion University, Norfolk, VA, USA · Virginia Digital Maritime Center, Old Dominion University, Norfolk, VA, USA +1
MR-IQA: A Unified Margin View of Regression and Ranking for Blind Image Quality Assessment
Jun 29, 2026Yuan Li, Youyuan Lin, Zitang Sun +4Learning to Rank Image Quality Assessment
Graduate School of Informatics, Kyoto University
Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
Jun 24, 2026Shubham Singh, Ian A. Kash, Mesrob I. OhannessianLearning to Rank Algorithmic Fairness
Department of Computer Science University of Illinois Chicago Chicago, IL 60607 · Department of Electrical and Computer Engineering University of Illinois Chicago Chicago, IL 60607
KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking
Jun 22, 2026Xinping Zhao, Jiaxin Xu, Ziqi Dai +9Representation Learning Learning to Rank
Harbin Institute of Technology (Shenzhen) · Shenzhen Loop Area Institute (SLAI)
Leakage-Aware Benchmarking of LLM Forecasting: Real-Time Nowcasts as the Decision-Time Input for Macro Factor Ranking
Jun 21, 2026Mao Guan, Qian ChenLearning to Rank Financial Forecasting
Independent Researcher.
Venice-H1: Failure-Aware Query Re-Ranking with Multi-Scale Grid Signatures for Referring Image Segmentation
Jun 21, 2026Nicolò SavioliLearning to Rank Referring Image Segmentation
OdaxAI Research
JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling
Jun 17, 2026Daochen Zha, Chun How Tan, Xin Liu +9Learning to Rank User Modeling
Airbnb, San Francisco, USA
Aerial-ground LiDAR place recognition with patch-level self-supervised learning and expanded reciprocal re-ranking
Jun 17, 2026Yandi Yang, Xianghong Zou, Jianping Li +4LiDAR Place Recognition Learning to Rank
Department of Geomatics Engineering, University of Calgary, Calgary, T2N 1N4, Canada · School of Advanced Manufacturing, Nanchang University, Nanchang, 330031, China · School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore +1
Beyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?
Jun 14, 2026Sadanand Singh, Allam Reddy, Manan ChopraCross-Encoder Reranking Learning to Rank
Cascade Research

Jul 26, 2026, cs.IRJ/K move · Enter open · S save

Ranked by Position: Order Sensitivity as an Exploitable Attack Surface in LLM Listwise Recommenders

Ge Zhang, Jingru Cheng, Huiyuan Chen

Stanford University · Independent Researcher

Learning to Rank Adversarial Attacks Adversarial Attacks on LLMs LLM Reranking Recommender Systems Position Bias

Open paper AskPDF arXiv

Large language models (LLMs) used as listwise rerankers in recommendation systems suffer from position bias when serializing candidate sets into prompts. We show this order sensitivity creates an exploitable attack surface: an attacker can promote a label-0 target into the top-

k

solely by reordering candidates, without changing item content, labels, or model parameters. We introduce

\mathrm{promo}@k

to quantify this vulnerability, measuring the fraction of label-0 targets that can be elevated into top-

k

rankings via permutation. Evaluating across three domains (MovieLens, Amazon Books, and Amazon Fashion),

\mathrm{promo}@5

reaches up to 0.57 at an attack budget of

R

= 50 orderings. Furthermore, ordinary permutation stability predicts vulnerability without running the attack. While a bidirectional T5 encoder scorer reduces exposure, permutation-consistency regularization and architectural invariance effectively mitigate it. Pointwise scoring avoids the bias issue but degrades ranking quality. These results demonstrate that input candidate order in listwise LLM reranking is a security-relevant attack vector. Code and data are available at https://github.com/geoz-lab/position_bias_attack.

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