Pairwise Comparison

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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
LAUKIN: A Multi-jurisdictional Common Law Contract Dataset
Jun 11, 2026Amrita Singh, Aditya Joshi, Jiaojiao Jiang +2Legal NLP Pairwise Comparison
Computer Science and Engineering, UNSW, Sydney, Australia · Law and Justice, UNSW, Sydney, Australia
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
Differences in Detection: Explainability Where it Matters
Jun 5, 2026Johannes Theodoridis, Johannes Maucher, Andreas SchillingPairwise Comparison Object Detection
University of T ubingen · Institute for Applied AI · Hochschule der Medien Stuttgart
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
Language Models Compare Quantities Using Number-specific and Unit-specific Heuristics
Jun 2, 2026Mutsumi Sasaki, Go kamoda, Ryosuke Takahashi +4Numerical Reasoning in Language Models LLM Evaluation
Tohoku University · SOKENDAI · NINJAL +2
Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization
Jun 1, 2026Louise Davy, Stephan Clémençon, Charlotte LaclauPairwise Comparison Importance Sampling
IDS, LTCI Télécom Paris Palaiseau, France
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
Improving Selective Classification with Pairwise Queries for Binary Classification
May 28, 2026Harsh Vardhan, Sunav Choudhary, Natwar Modani +1Pairwise Comparison Selective Prediction
CSE, UCSD, 9500 Gilman Drive, La Jolla, 92092, CA, USA. · Adobe Research, 345 Park Ave, San Jose, 95110, CA, USA. · Adobe Research, Marathahalli, Bengaluru, 560087, Karnataka, India. +1
Resolution Diagnostics for Paired LLM Evaluation
May 28, 2026Anany KotawalaLLM Evaluation Pairwise Comparison
Princeton University, Princeton, NJ, USA.
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
JudgmentBench: Comparing Rubric and Preference Evaluation for Quality Assessment
May 24, 2026Russell Yang, Ruishi Chen, Pierce Kelaita +6Human Preference Evaluation LLM Evaluation
Stanford University · Snorkel AI · Harvey
RankJudge: A Multi-Turn LLM-as-a-Judge Synthetic Benchmark Generator
May 20, 2026Zhenwei Tang, Zhaoyan Liu, Rasa Hosseinzadeh +3LLM Evaluation Pairwise Comparison
Layer 6 AI · University of Toronto
VersusQ: Pairwise Margin Reasoning for Generalizable Video Quality Assessment
May 20, 2026Shibei Meng, Binxin Yang, Yuan Liu +4Pairwise Comparison Learning to Rank
The Chinese University of Hong Kong · Beijing Normal University · WeChat Vision, Tencent Inc. +2
Preferences Order, Ratings Anchor: From Fused Expert Aesthetic Ground Truth to Self-Distillation
May 19, 2026Yuanpei Zhao, Jie Lin, Chao Zhang +5Pairwise Preference Learning Personalized Image Aesthetics Assessment
1Sichuan University · 2NetEase Fuxi AI Lab · †Work done during an internship at NetEase Fuxi AI Lab. +1
CriterAlign: Criterion-Centric Rationale Alignment for Code Preference Judging
May 19, 2026Zhenyu Li, Aleksandar Cvejic, Zehui Chen +1Pairwise Preference Learning Human Preference Evaluation
KAUST · USTC
iDiff: Interpretable Difference-aware Framework for Pairwise Image Quality Assessment
May 19, 2026Xinli Yue, JianHui Sun, Tao Shao +3Pairwise Comparison Image Quality Assessment
WeChat, Tencent
CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning
May 15, 2026Fangzhou Lin, Shuo Xing, Peiran Li +6Pairwise Comparison Pairwise Preference Evaluation
Texas A&M University · Georgia Institute of Technology · Tohoku University +1
OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation
May 14, 2026Shang Zhou, Wenhao Chai, Kaiyuan Liu +3Pairwise Comparison Test-Time Optimization
1UC San Diego · 2Princeton University · University of Washington +1
Generalized Priority-Aware Shapley Value
May 14, 2026Kiljae Lee, Ziqi Liu, Weijing Tang +1Pairwise Comparison Game Theory
The Ohio State University · Carnegie Mellon University
Active Learners as Efficient PRP Rerankers
May 14, 2026Jeremías Figueiredo Paschmann, Juan Kaplan, Francisco Nattero +3Pairwise Preference Learning Pairwise Comparison
ELIAS Lab, Departamento de Ingeniería, Universidad de San Andrés
RTLC -- Research, Teach-to-Learn, Critique: A three-stage prompting paradigm inspired by the Feynman Learning Technique that lifts LLM-as-judge accuracy on JudgeBench with no fine-tuning
May 13, 2026Andrea MorandiLLM Evaluation Pairwise Comparison
Cisco Systems, Inc.
Efficient Ensemble Selection from Binary and Pairwise Feedback
May 10, 2026Tzeh Yuan Neoh, Nicholas Teh, Je Qin Chooi +2Pairwise Comparison Pairwise Preference Evaluation
Harvard University, USA · University of Oxford, UK
When Style Similarity Scores Fail: Diagnosing Raw CSD Cosine in Artist-Style Evaluation
May 9, 2026Jörg FrochtePairwise Comparison Image Generation Evaluation
Bochum University of Applied Sciences
Sufficient conditions for a Heuristic Rating Estimation Method application
May 9, 2026Jacek Szybowski, Konrad Kułakowski, Jiri MazurekPairwise Comparison Pairwise Preference Evaluation
AGH University of Science and Technology, Faculty of Applied Mathematics, Krakow, Poland · AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Krakow, Poland · Silesian University in Opava, Department of Informatics and Mathematics, School of Business Administration in Karvina, Czech Republic
Accurate and Efficient Statistical Testing for Word Semantic Breadth
May 8, 2026Yo EharaTwo-Sample Testing Pairwise Comparison
Tokyo Gakugei University
Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML
May 7, 2026Jai Moondra, Ayela Chughtai, Bhargavi Lanka +1Multilingual Language Model Evaluation LLM Evaluation
Carnegie Mellon University · MIT Sloan School of Management

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Comparison Patrols on Drifting Orders: Certified Rank Maintenance, Evolving Planar Maxima, and Selection under Drifting Fitness

Faruk Alpay, Levent Sarioglu

Department of Computer Engineering, Bahçeşehir University, Istanbul, Turkey

Pairwise Comparison Evolutionary Optimization Pairwise Ranking Runtime Analysis of Evolutionary Algorithms

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Rank-based selection in dynamic environments acts on order information that becomes stale while it is being used. Tournaments, elitism, truncation, and Pareto selection may therefore consume rankings that no longer match the current fitness order, while full re-evaluation competes with search for the same budget. This paper formulates the missing information layer as a data-structure problem. A hidden total order on

n

items drifts by adjacent transpositions, while a maintainer receives one truthful pairwise comparison per step and must answer rank queries continuously. We introduce the comparison patrol, a constant-time maintained-order structure using

3n+O(1)

words, one comparison per update, deterministic verification-age bounds, and per-item displacement certificates. We prove lower bounds showing that oblivious and location-oblivious maintainers incur expected Kendall error

Ω(\min(α,1)n)

, and show that the patrol operates at the same order. A bump invariant yields exact self-stabilization after drift-free corruption: if the maximum rank overstatement is

L

, recovery takes at most

L

aligned cycles and cannot finish before

L-1

. This gives a deterministic shock-recovery calculus and a crossover with full rebuild near

L\approx \log_2 n

. The maintained order is then transferred to evolving planar maxima and to evolutionary selection rules, giving deterministic bounds for truncation, tournament, elitist, and two-objective Pareto decisions under drifting fitness. Experiments up to

n=65{,}536

audit the certificates, recovery laws, equilibrium behavior, and equal-budget dynamic evolutionary loops, identifying when certified local rank maintenance outperforms global re-evaluation and when it should hand over.

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