Ranking

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Period ending 2026-09-14

3 new papers

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Period ending 2026-09-07

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A weekly snapshot of new work published in Ranking.

116 papers

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TOPSIS-RAD: Ranking According to Desires
Jun 5, 2026Leonardo Fernandes Costa, Helder Gomes Costa, Diogo Lima +1Generalizable Ranking Purpose Ranking
Representation Curriculum: Stagewise Training for Robust Ranking and Allocation
Jun 3, 2026Ehsan Ebrahimzadeh, Sina Baharlouei, Abraham BagherjeiranGeneralizable Ranking Purpose Ranking
Token Rankings are Unforgeable Language Model Signatures
Jun 3, 2026Matthew Finlayson, Andreas Grivas, Xiang Ren +1Ranking Fingerprint
Can LLM Rerankers Predict Their Own Ranking Performance?
Jun 2, 2026Shiyu Ni, Keping Bi, Jiafeng Guo +3Generative Reasoning Re-Rankers Cross-Encoder Reranker
Ranking vs. Assignment: The Metric Mismatch in Multi-View Object Association
Jun 1, 2026Matvei Shelukhan, Timur Mamedov, Aleksandr Chukhrov +1Multi-View Computer Vision
UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale
May 29, 2026Hanyu Li, Yi-Ping Hsu, Aditya Mantha +17Recommender Systems Ranking
HyBIRD: Hyperbolic Bridge Retrieval and Diagnosis for Methodology Inspiration Retrieval
May 29, 2026Yang Yang, Boyun Xu, Hao Fu +6Biomedical Bridge Models
On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets
May 29, 2026Ana Gjorgjevikj, Barbara Koroušić Seljak, Tome EftimovPatient-Specific Textual Embeddings Multilingual
RAISE: RAG Design as an Architecture Search Problem
May 28, 2026Zhen Chen, Yibing Liu, Weihao Xie +3Hievi-Rag Retrieval-Augmented Generation
UniNote: A Unified Embedding Model for Multimodal Representation and Ranking
May 28, 2026Jinghan Zhao, Wenwei Jin, Anqi Li +5Multimodal Retrieval Ranking
Surfacing Isolated Learners with Outcome-Independent Mediation of Feedback between Teachers and Students Using AI
May 28, 2026Junsoo Park, Youssef Medhat, Htet Phyo Wai +2Teacher Natural Language Feedback
MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality
May 28, 2026Yujin Park, Haejun Chung, Ikbeom JangImage Quality Assessment Ranking
GEO-Bench: Benchmarking Ranking Manipulation in Generative Engine Optimization
May 27, 2026Ojas Nimase, Zhe Chen, Gengpei Qi +2Language Model Evasion Attacks Ranking
First, do no harm: Breaking suicidogenic echo chambers in media recommendation
May 24, 2026Alberto Díaz-Álvarez, Raúl Lara-Cabrera, Fernando Ortega-Requena +1Suicide Risk Recommendation
How Hard is it to Rig a Benchmark? A Social Choice Analysis of Leaderboard Robustness
May 22, 2026Polina Gordienko, Georg Schollmeyer, Frauke Kreuter +1Leaderboard Ranking
Cross-Species RSA Reveals Conserved Early Visual Alignment but Divergent Higher-Area Rankings Across Human fMRI and Macaque Electrophysiology
May 21, 2026Nils LeuteneggerHuman Visual Cortex Neural Recordings
RankJudge: A Multi-Turn LLM-as-a-Judge Synthetic Benchmark Generator
May 20, 2026Zhenwei Tang, Zhaoyan Liu, Rasa Hosseinzadeh +3Llm-As-A-Judge Chatbots
RankE: End-to-End Post-Training for Discrete Text-to-Image Generation with Decoder Co-Evolution
May 20, 2026Siyong Jian, Siyuan Li, Luyuan Zhang +5Text-To-Image Co-Evolution
CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning
May 14, 2026Sining Ang, Yuguang Yang, Canyu Chen +1Closed-Loop Performance Scorer
Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty
May 14, 2026Tianjue Lin, Jianan Zhou, Jieyi Bi +4Multi-Objective Optimization Heuristics
Unweighted ranking for value-based decision making with uncertainty
May 13, 2026Aarón López García, Natalia Criado, Jose SuchHuman Values Decisions
Margin-calibrated Classifier Guidance for Property-driven Synthesis Planning
May 13, 2026Najwa Laabid, Vikas GargSynthesis Classifier-Free Guidance
F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking
May 13, 2026Rohan Surana, Gagan Mundada, Junda Wu +9Group Relative Policy Optimization Gradients Fine-Grained Credit Assignment
The End Justifies the Mean: A Linear Ranking Rule for Proportional Sequential Decisions
May 12, 2026Carmel Baharav, Niclas Boehmer, Bailey Flanigan +1Democracy Fairness
RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking
May 11, 2026Andrew Choi, Wei XuOffline Safe Reinforcement Learning Ranking
Qwen Goes Brrr: Off-the-Shelf RAG for Ukrainian Multi-Domain Document Understanding
May 11, 2026Anton Bazdyrev, Ivan Bashtovyi, Ivan Havlytskyi +2Multimodal Query Retrieval-Augmented Generation
Generative AI Fuels Solo Entrepreneurship, but Teams Still Lead at the Top
May 11, 2026Hyunso Kim, Hyo Kang, Jaeyong SongGenerative Artificial Intelligence Team Formation
FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
May 11, 2026Qingchuan Zhang, He Cao, Hao Li +6Molecular Optimization Candidate
HBEE: Human Behavioral Entropy Engine -- Pre-Registered Multi-Agent LLM Simulation of Peer-Suspicion-Based Detection Inversion
May 8, 2026Vickson FerrelThreat Models Artificial Intelligence Detection
An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
May 8, 2026Haoyu Han, Li Ma, Hanbing Wang +9Recommendation Architecture Heuristics
Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML
May 7, 2026Jai Moondra, Ayela Chughtai, Bhargavi Lanka +1Ranking Large Language
Low Rank Tensor Completion via Adaptive ADMM
May 5, 2026Niclas Führling, Getuar Rexhepi, Giuseppe Thadeu Freitas de AbreuTensor Completion Alternating Direction Method Of Multiplier
Rethinking the Rank Threshold for LoRA Fine-Tuning
May 5, 2026Juneyoung ParkCross-Entropy Losses Ranking
TRAP: Tail-aware Ranking Attack for World-Model Planning
May 3, 2026Siyuan Duan, Ke Zhang, Xizhao LuoClassical Planning Backdoor Attacks
Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid
May 2, 2026Alessio Donvito, Antonio LietoCognitive Science Analogy
LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction
May 1, 2026Yan Lin, Yihong Su, Yi YangGeneralizable Ranking Purpose Ranking
BoostLoRA: Growing Effective Rank by Boosting Adapters
Apr 30, 2026Raviteja Anantha, Nick Levato, Layne C. PriceParameter-Efficient Fine-Tuning Methods Adapters
R $^3$ -SQL: Ranking Reward and Resampling for Text-to-SQLR^3-SQL: Ranking Reward and Resampling for Text-to-SQL
Apr 28, 2026Hojae Han, Yeonseok Jeong, Seung-won Hwang +2Text-To-Sql Sql
Explanation Quality Assessment as Ranking with Listwise Rewards
Apr 27, 2026Thomas Bailleux, Tanmoy Mukherjee, Emmanuel Lonca +2Generalizable Ranking Purpose Ranking
Rank, Head-Channel Non-Identifiability, and Symmetry Breaking: A Precise Analysis of Representational Collapse in Transformers
Apr 26, 2026Giansalvo CirrincioneMulti-Head Attention Batch Normalization
Unstable Rankings in Bayesian Deep Learning Evaluation
Apr 25, 2026Qishi Zhan, Minxuan Hu, Guansu Wang +2Bayesian Neural Networks Sample Size
Who Defines "Best"? Towards Interactive, User-Defined Evaluation of LLM Leaderboards
Apr 23, 2026Minji Jung, Minjae Lee, Yejin Kim +2Leaderboard Large Language Model Evaluation
In-Situ Behavioral Evaluation for LLM Fairness, Not Standardized-Test Scores
Apr 21, 2026Zeyu Tang, Sang T. Truong, Deonna Owens +4Large Language Model Bias Large Language Model Evaluation
Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems
Apr 20, 2026Junyi Yao, Zihao Zheng, Jiayu LongGeneralizable Ranking Purpose Ranking
Learning to Control Summaries with Score Ranking
Apr 19, 2026Hongye Liu, Liang Ding, Ricardo HenaoSummarization Ranking
Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering
Apr 18, 2026Zixiao Zhao, Amirreza Esmaeili, Fatemeh FardLlm-As-A-Judge Code Quality
RankGuide: Tensor-Rank-Guided Routing and Steering for Efficient Reasoning
Apr 17, 2026Jiayi Tian, Yupeng Su, Ryan Solgi +2Reasoning Benchmark Inference Latency
Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking
Apr 17, 2026Jun Feng, Jiahui Tang, Zhicheng He +5Large Language Model Adaptation Generative Reasoning Re-Rankers
LRConv-NeRV: Low Rank Convolution for Efficient Neural Video Compression
Mar 18, 2026Tamer ShanablehNeural Video Compression Transcoder
Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization
Oct 8, 2025Tiancheng Xing, Jerry Li, Yixuan Du +1Ranking
Hearing the Order: Investigating Position Bias in Large Audio-Language Models
Oct 1, 2025Yu-Xiang Lin, Chen-An Li, Sheng-Lun Wei +3Large Audio Language Models Bias Detection and Mitigation
Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank
Sep 25, 2025Yiheng Tao, Yihe Zhang, Matthew Dearing +4Large Language Ranking
PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains
Aug 29, 2025Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema +7Large Reasoning Models Ranking
Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
Aug 16, 2025Jenny Y. Huang, Yunyi Shen, Dennis Wei +1Generalizable Ranking Purpose Ranking
Pairwise Comparisons without Stochastic Transitivity: Model, Theory and Applications
Jan 13, 2025Sze Ming Lee, Yunxiao ChenComparison Ranking
Consistent Distributed Ranking of Generative Models via Kernel Distances
Oct 18, 2023Zixiao Wang, Farzan Farnia, Zhenghao Lin +2Generalizable Ranking Purpose Model Evaluation

Jun 5, 2026 · cs.AIJ/K move · Enter open · S save

TOPSIS-RAD: Ranking According to Desires

Leonardo Fernandes Costa, Helder Gomes Costa, Diogo Lima, Brunno Rodrigues

Traditional TOPSIS derives its reference points -- the Positive Ideal Solution (

PIS

) and Negative Ideal Solution (

NIS

) -- from the observed alternative set, making rankings susceptible to misalignment with decision-maker (DM) requirements, sensitivity to outlier performances, and rank reversal. This paper proposes TOPSIS-RAD, which addresses these issues by incorporating two arrays of DM-defined reference levels. Vetoed Performance Levels (

VPL

) exclude non-viable alternatives before normalisation, preventing them from distorting the ranking frontiers. Desired Performance Levels (

DPL

) cap performances at the DM's desired level before normalisation, anchoring the

PIS

in explicit aspirations rather than dataset extremes. Three toy examples demonstrate each mechanism:

VPL

reshapes normalisation boundaries by removing a non-viable alternative; fixed

DPL

frontiers stabilise rankings by limiting the influence of performances well above the desired level. The method preserves the familiar distance-based structure of TOPSIS while grounding the ranking in stable, DM-specified boundaries. Limitations and future research directions are also discussed.

Generalizable Ranking Purpose Ranking Normalization Negatives Abstention

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