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5 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Latest papers 32

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Synthetic Data Characterization via Training Dynamics
Sep 30, 2026Irene Lago, Ana Ezquerro, David VilaresSynthetic Data Learnability
Universidade da Coruña, CITIC, Spain · Graz University of Technology, IML, Austria
Augmented Feature Boosting for Multicalibration
Sep 27, 2026Ira Globus-Harris, Inbal Livni NavonCalibrated Uncertainty Recalibration
Cornell University · Ben-Gurion University of the Negev
Diffusion Reward Models
Sep 27, 2026Xiangyang Wang, Bingxiang He, Zeyuan Liu +13Progress Reward Modeling Diffusion Language Models
Tsinghua University · University of Illinois Urbana-Champaign
Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective
Sep 21, 2026Larry Preuett, Qiuyi Zhang, Muhammad Aurangzeb AhmadPartially Observable Markov Decision Process Distributional Reinforcement Learning
University of Washington, Tacoma, USA · Google DeepMind, California, USA · University of Washington, Bothell, USA
Translation Indeterminacy and the Distributional Fallacy
Sep 7, 2026Michael CarlCross-Lingual Consistency Large Language Models Fail
Kent State University Modern and Classical Language Studies
TraveL: Transformer-based Multi-view Path Distributional Representation Learning
Sep 3, 2026Fang He, Tao-yang Fu, Wang-chien LeeTransport Pathways
The Pennsylvania State University
Dynamic Distribution-Aware Uncertainty Tracking in Vision-Language Representation Learning
Aug 10, 2026Ao Zhou, Zhiwei Jiang, Zifeng Cheng +4Recent Vision-Language Models Uncertainty
State Key Laboratory of Novel Software Technology, Nanjing University
Relation Geometry in Semantic Space of Language Models
Jul 29, 2026Zhihan Cao, Hiroaki Yamada, Simone Teufel +4Semantic Relationships Language Modeling
Graduate School of Information Science and Technology, The University of Tokyo · Faculty of Systems Design, Tokyo Metropolitan University · Department of Computer Science and Technology, University of Cambridge +5
CEL: Comprehensive Counterfactual Explanations Library and Benchmark
Jul 24, 2026Oleksii Furman, Łukasz Lenkiewicz, Marcel Musiałek +1Counterfactual Explanation Distributional Information
Wrocław University of Science and Technology, Wrocław, Poland
Distributional Validity of a Korean Synthetic Persona Panel: Evidence From the Korea Media Panel Survey
Jul 23, 2026Howard Kim, Keuntae ChoArtificial Intelligence Personas Survey
College of AI Convergence, Seoul Cyber University, Seoul 01133, South Korea · Department of Management of Technology, Graduate School, Sungkyunkwan University, Suwon 16419, South Korea · Department of Systems Management Engineering, Sungkyunkwan University, Suwon 16419, South Korea
A Unifying Framework for Concept-Based Representational Similarity
Jun 8, 2026Grégoire Dhimoïla, Victor Boutin, Agustin Martin Picard +2Representational Similarity Analysis Unified Framework
Brown University · ENS Paris Saclay · CNRS +2
Beyond Averages: Evaluating LLMs on Human Survey Replication at the Distributional Level
Jun 8, 2026Jeonghyeon Moon, Jiwon Kim, Yeheum Lah +2Survey Large Language Model Responses
Ewha Womans University
UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs
Jun 4, 2026Amirhossein Abaskohi, Amirhossein Dabiriaghdam, Liang Luo +4Distributional Information User Simulation
University of British Columbia · 2Independent Researcher
Computational conceptual history of scientific concepts: From early digital methods to LLMs
Jun 2, 2026Michael Zichert, Arno SimonsHistory Distributional Information
VISReg: Variance-Invariance-Sketching Regularization for JEPA training
Jun 1, 2026Haiyu Wu, Randall Balestriero, Morgan LevineRegularization Covariance
Altos Labs · Brown University
Before and After Temperature: A Distributional View of Creative LLM Generation
May 31, 2026V. S. Raghu Parupudi, Harsha Ponnada, Aditi Kaushal +3Creativity Large Language Model Generation
University of California, San Diego · Indian Institute of Technology, Kharagpur · Delhi Technological University +3
Measuring Poverty and Inequality with Reduced Data: A Machine Learning Approach Using Nigerian Household Data
May 29, 2026Vanesa Jordá, Miguel Niño-ZarazúaConsumption Economies
Department of Economics, Cantabria University · Department of Economics, SOAS University of London · United Nations University World Institute for Development Economics Research (UNU-WIDER)
idSCD: Identifying Training Datasets through Semantic Correlation Descriptors
May 28, 2026Andrada Gobeaja, Ionut Hodoroaga, Elena Burceanu +1Membership Inference Cross-Dataset Benchmark
POLITEHNICA University of Bucharest · Bitdefender, Romania · Institute of Mathematics of the Romanian Academy
Probabilistic Attribution For Large Language Models
May 20, 2026Shilpika Shilpika, Carlo Graziani, Bethany Lusch +2Distributional Information
Argonne Leadership Computing Facility Argonne National Laboratory Lemont, IL, USA · Mathematics and Computer Science Division Argonne National Laboratory Lemont, IL, USA · Department of Computer Science University of Illinois Chicago Chicago, IL, USA
A Distributional View for Visual Mechanistic Interpretability: KL-Minimal Soft-Constraint Principle
May 17, 2026Guancheng Zhou, Yisi Luo, Zhengfu He +5Mechanistic Interpretability Interpretability
School of Mathematics and Statistics, Xi’an Jiaotong University · Shanghai Innovation Institute · OpenMOSS Team +2
Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning
May 15, 2026Shireen Kudukkil Manchingal, Abhey Kalia, Fernanda Gonçalves +1LLM Reasoning Strategies Large Language Model Uncertainty
Oxford Dynamics · Harwell Science and Innovation Campus, United Kingdom
RoSHAP: A Distributional Framework and Robust Metric for Stable Feature Attribution
May 14, 2026Lanxin Xiang, Liang Shi, Youhui Ye +3Shapley Additive Explanations Model-Based Bootstrap
Department of Statistics Virginia Tech · Department of Statistics Virginia Tech Transportation Institute · Department of Computer Science Virginia Tech
Mixing Times of Glauber Dynamics on Masked Language Models
May 11, 2026Suvadip Sana, Sami Wolf, Neer Mehta +4Markov Distributional Information
Department of Statistics and Data Science Cornell University · Cornell University · Department Cornell of University Mathematics
Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression
May 2, 2026Yao Du, Shanshan Song, Xiaomeng LiMultimodal Large Language Models Distributional Information
The Hong Kong University of Science and Technology, Hong Kong SAR, China.
LLMs Capture Emotion Labels, Not Emotion Uncertainty: Distributional Analysis and Calibration of Human-LLM Judgment Gaps
Apr 30, 2026Keito Inoshita, Xiaokang Zhou, Akira Kawai +1Large Language Model Annotations Emotion Recognition
Faculty of Business and Commerce, Kansai University, Osaka, Japan · Faculty of Business Data Science, Kansai University, Osaka, Japan · RIKEN Center for Advanced Intelligence Project, Tokyo, Japan +2
Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations
Apr 20, 2026Emily Reif, Claire Yang, Jared Hwang +3Large Language Model Generation Distributional Information
University of Washington, Seattle, WA, USA · Allen Institute for AI
Alignment Imprint: Zero-Shot AI-Generated Text Detection via Provable Preference Discrepancy
Apr 18, 2026Junxi Wu, Kailin Huang, Dongjian Hu +4Machine-Generated Text Detection Large Language Model Alignment
Nankai University · Tsinghua University · Wuhan University +3
Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
Mar 9, 2026Jonas Landsgesell, Pascal Knoll, Tizian WenzelTabular Foundation Models Distributional Information
University of Stuttgart (Stuttgart, Germany) · Ludwig Maximilian University of Munich (Munich, Germany) · Munich Center for Machine Learning (Munich, Germany)
Logit Distance Bounds Representational Similarity
Feb 17, 2026Beatrix M. G. Nielsen, Emanuele Marconato, Luigi Gresele +2Kullback-Leibler Divergence Logit Lens
IT University of Copenhagen, Denmark · University of Trento, Italy · University of Copenhagen, Denmark +2
How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off
Oct 1, 2025Waïss Azizian, Ali HasanIn-Context Learning Distributional Information
Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France · Work done during an internship at Morgan Stanley Machine Learning Research. · Machine Learning Research, Morgan Stanley, New York, USA
Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning
Dec 14, 2024Julia Witte Zimmerman, Denis Hudon, Kathryn Cramer +9Single-Token Output Distributions Distributional Information
Computational Story Lab, Vermont Complex Systems Institute, University of Vermont, Burlington, VT 05405, USA · Vermont Complex Systems Institute, University of Vermont, Burlington, VT 05405, USA · Department of Computer Science, St. Michael’s College, Colchester, VT 05439, USA +3
Universal Topological Regularity of Syntactic Structures
Jan 31, 2023Fermín Moscoso del Prado MartínSyntactic Structure Phylogenetic Inference
Department of Language and Communication & Center for Language Studies Radboud University Erasmuslaan 1, 6525 NL, Nijmegen, The Netherlands

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Synthetic Data Characterization via Training Dynamics

Irene Lago, Ana Ezquerro, David Vilares

Universidade da Coruña, CITIC, Spain · Graz University of Technology, IML, Austria

Synthetic Data Learnability Distributional Information Multi-Label Classification

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Interpreting properties of LLM-generated data is important for understanding its utility and limitations across learning tasks. In this work, we characterize synthetic data through sample-level learnability, studying variation among LLM families and scales, alongside human-written data as a reference. We first generate synthetic datasets spanning single- and multi-label classification, labeling, and tree prediction tasks. We then derive empirical data distributions from encoder training dynamics for both machine and organic data, and estimate the robustness of these distributions across encoders. Finally, we evaluate how data selection strategies based on these learnability signals affect both data sources differently.

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