Pairwise Ranking

Follow this topic Ask about this topic

Momentum

2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 50

All topics

Scientific Discovery under Validation Congestion via Multi-Fidelity Pairwise Rankings
Oct 1, 2026Kevin Tirta Wijaya, Alston Lo, Michael Sun +2Pairwise Ranking Autonomous Scientific Discovery
University of Bonn · Fraunhofer SCAI · Massachusetts Institute of Technology
OSCAR: Order-aware Scoring and Calibration for AI Rankings
Sep 21, 2026You Liu, Yue Liu, Quanchao Lu +1LLM Evaluation LLM-as-a-Judge
CliffRank: A Dual-Branch Framework for Activity-Cliff Ranking Prediction
Sep 1, 2026Kewei Li, Rongying Zhang, Peiyu Yang +4Learning to Rank Pairwise Ranking
College of Computer Science and Technology, Jilin University, Changchun, 130012, China. · Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China. · Greenwich High School, Greenwich, CT, 06830, United States of America. +3
Are Near-Tied LLM Rankings Robust to Family-DIF-Guided Benchmark Recomposition?
Aug 31, 2026Qiaoyuan Zheng, Yiqu YangLLM Evaluation Pairwise Ranking
ETH Zurich Zurich, Switzerland
Isotonic Bradley-Terry Model for Paired Comparison Data
Aug 3, 2026Ryoya YamasakiPairwise Preference Learning Pairwise Comparison
Hitotsubashi Institute for Advanced Study, Hitotsubashi University, 2-1 Naka, Kunitachi, 186-8601, Tokyo, Japan.
On the Limits of Machine-Learned Ranking for Modern Microarchitectural Policies
Aug 2, 2026Yanxin Zhang, Shayne Wadle, Yuxuan Xiong +3Learning to Rank Electronic Design Automation
NVIDIA · University of Wisconsin–Madison
Optimal Top- $k$ Identification from Pairwise Comparisons
Jul 9, 2026Motti Goldberger, Nils RudiPairwise Preference Learning Multi-Armed Bandits
Yale University, New Haven, CT, USA.
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
Attention Limited Reward Learning
Jul 6, 2026Wenqian XingPairwise Preference Learning Reward Modeling
Management Science and Engineering Department, Stanford University
Internal Pluralism and the Limits of Pairwise Comparisons
Jul 2, 2026Bailey Flanigan, Michelle SiPairwise Preference Learning Pairwise Comparison
MIT, USA · Harvard, USA
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
Can LLMs Rank? A Tale of Triads and Triage
Jun 29, 2026Gaurab Pokharel, Shafkat Farabi, Patrick J. Fowler +1Pairwise Comparison Social Choice Theory
Virginia Tech Alexandria, VA · Washington University in St. Louis St. Louis, MO
The Geometry of Sequential Learning: Lie-Bracket Prediction of Transfer Order
Jun 23, 2026John SweeneyPairwise Ranking Cross-Domain Transfer Learning
Sideplane AI
ParaPairAudioBench: Paralinguistic Pairwise Audio Benchmark for LALM-as-a-Judge
Jun 23, 2026Jisu Jeon, Seungyeon Jwa, Joosung Lee +6Audio-Language Model Evaluation Pairwise Comparison
Hongik University, South Korea · Seoul National University, South Korea · NAVER Cloud, South Korea +1
Prompt Perturbation for Reliable LLM Evaluation over Comparison Graphs
Jun 16, 2026Dong Huang, Jianbo Sun, Pengkun YangLLM Evaluation Pairwise Comparison
Department of Statistics and Data Science, Tsinghua University
TuneJury: An Open Metric for Improving Music Generation Preference Alignment
Jun 15, 2026Yonghyun Kim, Junwon Lee, Haiwen Xia +5Text-to-Music Generation Music Generation Evaluation
Georgia Tech · KAIST · Peking University +3
Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling
Jun 14, 2026Yujin Park, Haejun Chung, Ikbeom JangPairwise Preference Learning Pairwise Comparison
Hanyang University, Seoul, Republic of Korea · Hankuk University of Foreign Studies, Yongin, Republic of Korea
Comparison Patrols on Drifting Orders: Certified Rank Maintenance, Evolving Planar Maxima, and Selection under Drifting Fitness
Jun 12, 2026Faruk Alpay, Levent SariogluPairwise Comparison Evolutionary Optimization
Department of Computer Engineering, Bahçeşehir University, Istanbul, Turkey
From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation
Jun 11, 2026Bora Kargi, David SalinasLLM Evaluation Pairwise Comparison
ELLIS Institute Tübingen · OpenEuroLLM
Correct Looks Better: Pairwise Comparisons Reveal Accuracy Rankings
Jun 8, 2026Mina Remeli, Moritz HardtPairwise Comparison LLM-as-a-Judge
Max Planck Institute for Intelligent Systems, Tübingen, Germany, Tübingen AI Center
Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
Jun 5, 2026Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov +8Algorithm Selection Pairwise Comparison
HSE University Moscow, Russian Federation
A Training-Efficient Transformer-Based Anti-Spoofing Network for Logical Access in ASVspoof 5
Jun 2, 2026Sidan Yin, Bo ZhaoAudio Deepfake Detection Speaker Verification
San Domenico School, San Anselmo, CA, United States · University of Washington, Seattle, WA, United States
Ranking vs. Assignment: The Metric Mismatch in Multi-View Object Association
Jun 1, 2026Matvei Shelukhan, Timur Mamedov, Aleksandr Chukhrov +1Pairwise Ranking Data Association
Tevian Moscow, Russia · Lomonosov Moscow State University Moscow, Russia
The Representation-Rationalizability Tradeoff in Reward Learning
May 29, 2026Jing Dong, Yaoliang Yu, Pascal PourpartPairwise Preference Learning Reward Modeling
Vector Institute · University of Waterloo & Vector Institute
Pairwise Reference Alignment as a Model-Level Ordinal Observable
May 29, 2026Mujing LiHuman Preference Evaluation LLM Evaluation
Independent Researcher
Reward Learning from Best-of- $N$ Preference Data: Targets, Tradeoffs, and Design Principles
May 28, 2026Rattana Pukdee, Maria-Florina Balcan, Pradeep RavikumarPairwise Preference Learning Reward Modeling
Machine Learning Department Carnegie Mellon University
Resolution Diagnostics for Paired LLM Evaluation
May 28, 2026Anany KotawalaLLM Evaluation Pairwise Comparison
Princeton University, Princeton, NJ, USA.
Argument Quality Assessment with Large Language Models: A Pairwise Bradley-Terry Approach
May 27, 2026Nicolás Benjamín Ocampo, Agnes Paullate Nyiranziza, Davide CeolinLLM Evaluation Pairwise Ranking
1Centrum Wiskunde & Informatica, The Netherlands · 2Vrije Universiteit Amsterdam, The Netherlands
Preferred, Not Safer: Pairwise Preference Is a Poor Proxy for Clinical Safety
May 25, 2026Fay Elhassan, David Sasu, Alexandra Kulinkina +2LLM Safety Benchmarks Human Preference Evaluation
LiGHT Laboratory, EPFL, Lausanne, Switzerland
SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarks
May 25, 2026Yanhang Li, Zhichao Fan, Zexin ZhuangPairwise Comparison Pairwise Preference Evaluation
Northeastern University, Boston, MA, USA · University of Illinois Urbana-Champaign, Urbana, IL, USA · Southern Methodist University, Dallas, TX, USA

Oct 1, 2026, cs.LGJ/K move · Enter open · S save

Scientific Discovery under Validation Congestion via Multi-Fidelity Pairwise Rankings

Kevin Tirta Wijaya, Alston Lo, Michael Sun, Wojciech Matusik+1

University of Bonn · Fraunhofer SCAI · Massachusetts Institute of Technology

Pairwise Ranking Autonomous Scientific Discovery

Open paper AskPDF arXiv

Modern computational methods can now propose candidate molecules, materials, and other scientific designs at an unprecedented scale, creating a validation congestion where candidates are abundant, but experimental capacity to physically evaluate them remains scarce. Discovering novel scientific designs has therefore become increasingly dependent on curation: selecting a small set of promising designs for slow and costly experiments. Existing curation methods typically rely on data-driven regression models that predict absolute scores, but training these models requires substantial experimental data to begin with. Yet, useful curation signals do not have to take the form of absolute measurements, as scientific design discovery is often comparative in nature. Here, we propose that curation can instead be primarily driven by expert pairwise rankings, which are substantially easier to gather. The expertise can come from computational tools or human input of multiple levels of fidelity, ranging from empirical rules of thumb to agentic workflows and experienced scientists. We introduce PRISMS, a framework that uses pairwise rankings from one or more experts, potentially spanning multiple levels of expertise, to identify the most promising candidates without relying on data-hungry regressors. When experts differ in fidelity and cost, PRISMS escalates pairwise queries from lower- to higher-fidelity rankers based on a Fisher-information criterion. In iterative screening that selects designs from fixed drug discovery libraries, PRISMS achieves 50% top-10 discovery recall in ~42% fewer rounds than regression-only active learning, and in ~15% fewer rounds than the ranking-based method with no selective escalation. In optimization that generates new designs without restriction to a predefined library, PRISMS achieves ~18.8% higher hypervolume than the Bayesian optimization baseline.

Momentum

Related topics

Latest papers 50