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

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FairTest: Search-Based Fairness Testing for Multi-Agent Reinforcement Learning Systems
Sep 23, 2026Xiaotong Wang, Xuan XieMulti-Agent Reinforcement Learning Algorithmic Fairness
School of Computer Science and Engineering, Macau University of Science and Technology, Macao SAR, China
Quieter Than the Room: Representation Drift and Task Robustness in Speech Encoders
Sep 23, 2026Vsevolod Kovalev, Pranay ManochaSpeech Encoder Frozen Encoder
Boston University, Boston, MA, USA · Princeton University, Princeton, NJ, USA
Linguistic Triggers of Gender and Racial Bias in Open-Weight LLMs Applied to Recruitment
Sep 16, 2026Kosuke Kitahara, Nobuhiro YamaguchiLarge Language Model Bias Gender
Recruit Co., Ltd. 1-9-2 Marunouchi Chiyoda-ku, Tokyo 100-6640 Japan
Attributing Cohen's d: Training Data Attribution for Disease-Related Effects in Normative Age Biomarkers
Sep 7, 2026Jakob Snel, Marc-Andre SchulzVascular Age Cohort
Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany · Tübingen AI Center, University of Tübingen, Tübingen, Germany · Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany +1
The Dice Roll Method: A Standardized Protocol for Repeated-Query Auditing of Large Language Model Brand Recommendations
Sep 3, 2026Dmitrij ŻatuchinModel Auditing Effect Sizes
Department of Information Technologies, Estonian Entrepreneurship University of Applied Sciences (EUAS), Tallinn, Estonia. · Rankfor.AI, Tallinn, Estonia.
CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation
Aug 10, 2026Yaoning Yu, Kai-Min Chang, Ye Yu +3User Simulation Card
University of Illinois Urbana-Champaign United States · U.S. Bank United States · Stanford University United States +1
The Learning Objective Governs Perceptual Narrowing: A Cross-Lingual, Layer-Wise, Ten-Seed Study of Self-Supervised Speech Encoders
Aug 1, 2026Sejin YooSpeech Encoder Grapheme-To-Phoneme
Independent Researcher
Filtering Harmful Actions Isn't Enough: Phantom Transfer in Agentic SDF
Jul 12, 2026Chinmayi DixitSynthetic Training Data Agentic Learning
ERA Fellowship
CAFE: A Compound-AI Factorial Evaluation Framework
Jul 11, 2026Fabian Lukassen, Christoph Weisser, Thomas Kneib +1Artificial Intelligence Evaluation Artificial Intelligence Systems
University of Göttingen · Bielefeld University of Applied Sciences and Arts (HSBI)
Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
Jul 9, 2026Ofir Arviv, Kristjan Greenewald, Yotam Perlitz +3Model Evaluation Benchmark Saturation
IBM Research
From Technical Metrics to User Perception: A User Study of a Multimodal Human-Robot Interaction System for Object Detection and Grasping
Jul 1, 2026Jian Song, Tian Zi, Shen GuantingHuman-Robot Interaction Object Detection
The authors are with the Dalian University of Technology, Dalian, China.
When Certainty Is an Artifact: Keyword Lexicon Blindness and the (Mis)Measurement of Rhetorical Stance
Jun 24, 2026Bo ChenRhetoric Attitude
Institute of Computing Technology, Chinese Academy of Sciences
Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure
Jun 24, 2026Maxwell Twelftree, David Lemphers, An-chi He +1Workload Artificial Intelligence Infrastructure
Maincode, Melbourne, Australia
AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice
Jun 17, 2026Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Clara Brandt +2Tutors Computerized Adaptive Testing
Carnegie Mellon University, Pittsburgh, PA, USA · Vanderbilt University, Nashville, TN, USA
ZIVARI-TLBO: A Zero-Cost Inter-Group Evaluated-Elite Relay Mechanism for Teaching-Learning-Based Optimization
Jun 12, 2026Pezhman ZivariOptimistic Follow-The-Regularized-Leader Effect Sizes
Independent Researcher, Istanbul, Türkiye
Automated reproducibility assessments in the social and behavioral sciences using large language models
Jun 11, 2026Tobias Holtdirk, Pietro Marcolongo, Anna Steinberg Schulten +7Reproducibility Effect Sizes
1LMU Munich, Munich, Germany · 2Munich Center for Machine Learning, Munich, Germany · University of Maryland, College Park, USA +1
A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget
Jun 11, 2026Joe DwyerLarge Language Model Training Small Large Language Models
Department of Computer Information Science ECPI University Virginia Beach, VA 23462
From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features
Jun 10, 2026Juliette Murris, Bernadette Stolz, Karsten BorgwardtPersistent Homology Topology
Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, Germany
Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles
Jun 4, 2026Upasana ChatterjeeIdeology Political Discourse
Department of Computer Science Columbia University
Analysis of Ethnic Disparities in Autism Spectrum Disorder among Toddlers
May 31, 2026Aadithya Prabha Ramaharsha, Deevna Reddy, Uma RanjanAutism Spectrum Disorder Disparities
Sri Ramachandra Institute of Higher Education and Research
Persona Conditioning of Brand Recommendations in Retrieval-Augmented Commercial Chat: A Prominence-Stratified Cross-Provider Audit
May 28, 2026Will Jack, Noah Lehman, Keller Maloney +1Brand-As-Memory Recommendation
Unusual
Predicting Causal Effects from Natural Language Queries using Structured Representations
May 28, 2026Giuliano Martinelli, Piriyakorn Piriyatamwong, Abelardo Carlos Martinez Lorenzo +7Causal Inferences Effect Sizes
1The World Bank Group · University of Oxford · 3New York University
Pre-Registering the Detectable Effect: A Paired-MDE Budget for 4-bit Quantization Benchmarks, with a Pilot Audit
May 25, 2026Zexin Zhuang, Yanhang Li, Zhichao FanQuantized Effect Sizes
1Southern Methodist University · 2Northeastern University · University of Illinois Urbana-Champaign
Exploring Profiles of Cognitive Distortions Associated with Mental Health Disorders
May 24, 2026Alina Anikejeva, Kairit SirtsMental Health Cognitive Science
Institute of Computer Science · University of Tartu · Tartu, Estonia
MRecover: A Conditional Generative Model for Recovering Motion-Corrupted MR images Using AI Generated Contrast
May 20, 2026Jinghang Li, Tales Santini, Courtney Clark +13Magnetic Resonance Imaging Generative Models
Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA · School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA · Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA +1
Kinematic Discriminants of Deceleration Behavior Modes in Car-Following: Evidence from NGSIM Trajectory Data
May 6, 2026Eni Solomon LaughterHuman Driving Acceleration
COLLEGE OF TRANSPORTATION ENGINEERING, CHANG'AN UNIVERSITY, XI'AN 710064, CHINA
Beyond the Mean: Within-Model Reliable Change Detection for LLM Evaluation
Apr 30, 2026Jon-Paul CacioliLarge Language Model Evaluation Change
Independent Researcher, Melbourne, Australia
Propensity Inference: Environmental Contributors to LLM Behaviour
Apr 22, 2026Olli Järviniemi, Oliver Makins, Jacob Merizian +2Artificial Intelligence Alignment Cognitive Science
UK AI Security Institute · Work done while at UK AISI.

Sep 23, 2026, cs.SEJ/K move · Enter open · S save

FairTest: Search-Based Fairness Testing for Multi-Agent Reinforcement Learning Systems

Xiaotong Wang, Xuan Xie

School of Computer Science and Engineering, Macau University of Science and Technology, Macao SAR, China

Multi-Agent Reinforcement Learning Algorithmic Fairness Effect Sizes

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Multi-agent Reinforcement Learning (MARL) trains a team of agents that share one environment and learn their policies together. Training maximizes the team return, and a high return does not imply that the rewards are shared fairly among the agents in every episode. Testing is an established way to discover the failures of deep reinforcement learning, yet few methods address the fairness of MARL. In this work, we propose FairTest, a search-based testing approach that seeks the unfair executions of a MARL policy. The design combines search guidance with test prioritization. The guidance scores each candidate with three fitness functions. One measures the fairness of the runs already performed, another predicts the fairness from abstract states and fairness features, and the third reads the decision uncertainty from the policy. Crossover and mutation derive further candidates from the observed executions. The prioritization ranks the candidates by the predicted fairness and the decision uncertainty, so that the runs reach the candidates where failures are expected. FairTest is evaluated on three environments and two MARL algorithms, and four baselines are given the same budget. It detects the most fairness failures compared to three baselines with statistical significance and large effect sizes. The failure count exceeds that of the strongest baseline by 221% on average and coverage improves by an average of 23%.

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