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14 papers in the last four weeks, down 18% on the four weeks before. 0.2% of all new papers.

Jul 6Week of Sep 21

Latest papers 197

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Learning to Predict Distributions over Weight Updates for Test-Time Adaptation
Oct 1, 2026Azal Ahmad Khan, Keshav Ramji, Tahira Naseem +2Large Language Model Adaptation Hypernetwork
University of Minnesota · IBM Research AI
Zero Flux: Flow-Based Comparison of High-Dimensional Discrete Distributions
Oct 1, 2026Leyang Wang, Yakun Wang, Song Liu +1Distributions Comparison
Department of Mathematical Informatics, The University of Tokyo, Japan · Center for Advanced Intelligence Project, RIKEN, Japan · School of Mathematics, University of Bristol, United Kingdom
Evaluating LLM-Generated Preference Distributions
Oct 1, 2026Fan Huang, Minsuk Kim, C. Tyler Diggans +1Preference Alignment Learning Distributions
Indiana University Bloomington Bloomington, IN, USA · Massachusetts Institute of Technology Cambridge, MA, USA · Root Dynamix LLC Sewickley, PA, USA
A Matched-Budget Audit Framework for Recaptioned Image-Text Supervision Distributions
Oct 1, 2026Giyeong Oh, Junghun Park, Yuhan Bae +1Video Captioning Text-To-Image
Seoul National University
Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
Sep 30, 2026Duong M. Nguyen, Trong Nghia Hoang, Hang Thi Nguyen +3Whole-Slide Images Digital Pathology
University of Illinois Urbana-Champaign · Washington State University · Hanoi Medical University +2
Fast and Scalable Multi-Agent Distribution Matching via Partitioned Optimal Transport
Sep 30, 2026Kooktae Lee, Ruchika SinghDistribution Matching Latent Transport
Department of Mechanical and Aerospace Engineering, Texas Tech University, Lubbock, TX 79409, USA
Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions
Sep 29, 2026Meng-Chen Wu, Qipin Chen, Ansh Jain +3Internal Pluralism Demographics
Amazon Science
DMA $^2$ : Pixel-space Distribution Matching with Adversarial and Anchor Losses
Sep 29, 2026Xin Lin, Zhifei Zhang, Yuqian Zhou +6Latent Diffusion Model Distribution Matching Distillation
UC San Diego · Adobe Research · University of Virginia +1
Reactive Real-Time Flow Policies via Asynchronous Distribution Alignment
Sep 29, 2026Moritz Zoellner, Reece O'Mahoney, Ioannis Havoutis +1Asynchronous Execution Robot Policies
Department of Computer Science, Purdue University · Oxford Robotics Institute, University of Oxford
PDMD: Projected Distribution Matching Distillation for Video Diffusion Models
Sep 28, 2026Zimo Wang, Junkun Yuan, Angtian Wang +9Video Diffusion Models Distribution Matching Distillation
University of California, San Diego · ByteDance Inc.
Beyond Gradient Flow: Identifiability and Recovery from Distribution Snapshots
Sep 28, 2026Nam D. Nguyen, Valeriya MalyshevaFokker--Planck Equation Snapshots
VIB, Center for Molecular Neurology, Antwerp, Belgium · VIB, Center for AI and Computational Biology, Leuven, Belgium · Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, University of Antwerp, Antwerp, Belgium +2
Sample What You Say: Aligning Language Models to Sample the Distributions They State
Sep 28, 2026Kasra Arabi, Virginia Smith, Chhavi YadavDistributions Parallel Rollouts
New York University · Carnegie Mellon University
Simulating Respondents, Not Single Questions: Coherent Survey Generation with Large Language Models
Sep 28, 2026Ji Huang, Mengfei Li, Shuai ShaoSurvey Large Language Model Responses
School of Computer Science, University of Science and Technology of China · School of Management, Fudan University
DisKO: Deep Koopman Learning in Distribution Space from Unpaired Snapshots
Sep 28, 2026He Ma, Xiaochen Liu, Wanfeng Lu +3Koopman Operator Learning Dynamics
School of Mathematical Sciences, Fudan University, China. · Research Institute of Intelligent Complex Systems, Fudan University, China. · Shanghai Center for Mathematical Sciences, Fudan University, China. +2
Epistemic Learning from Imprecise Annotation
Sep 28, 2026Kaizheng Wang, Siu Lun ChauEpistemic Integrity Distributions
Epistemic Intelligence & Computation Lab, College of Computing & Data Science, Nanyang Technological University, Singapore
Investigating Human--AI Discrepancies via Multiple-Solution Problems
Sep 28, 2026Zihao Wang, Francesco Insulla, Andrea MontanariLarge Reasoning Models Discrepancy
Department of Mathematics, Stanford University · Institute of Computational and Mathematical Engineering, Stanford University · Department of Statistics and Department of Mathematics, Stanford University
Benign Overfitting for General Norms and Distributions
Sep 27, 2026Daniel Barzilai, Ohad ShamirOverfitting Spectral Norm
Weizmann Institute of Science · University of Toronto & Vector Institute
From Distributions to Stochastic Processes: Neural Approximation of Measure-Valued Maps
Sep 27, 2026Yichen Wang, Ziyi Wang, Wenlian Lu +5Probability Measures Stochastic Processes
Shanghai Center for Mathematical Sciences, Shanghai, China · Department of Mathematics, Fudan University, Shanghai, China · Center for Applied Mathematics, Fudan University, Shanghai, China +2
Next Thoughts Are Distributions: Generative Autoregressive Reasoning in the Latent Space
Sep 27, 2026Yang Li, Yi Wang, Shiyuan Huang +3Thoughts Reasoning Skills
Rutgers University · Amazon · University of Illinois at Urbana-Champaign
Bridging Reconstruction and Generation: A Latent Distribution Perspective on Evaluation and Improvement
Sep 21, 2026Xianghong Fang, Wenjie Shu, Tongda Xu +3Generative Models Fréchet Inception Distance
University of Toronto · Independent · Vijil
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sep 17, 2026Sambit Mishra, Yingying Wang, Christine K. Johnson +1Causal Discovery Methods Causal Graph
University of Southern California · University of California, Davis
Improving Cross-Lingual Transfer for Sequential Sentence Classification in Research Papers via Structural Similarity
Sep 17, 2026Kazuhiro Yamauchi, Marie KatsuraiCross-Lingual Transfer Linguistics
Doshisha University Kyotanabe, Japan
Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels
Sep 16, 2026Souhardya Chattopadhyay, Julian Oelhaf, Antonia Schoening +5Accurate Short-Term Electricity Load Forecasting Chronos
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg · Siemens AG, Smart Infrastructure · Department of Electrical Engineering, Media and Computer Science, Ostbayerische Technische Hochschule Amberg-Weiden
Bridging the Gap Between Homogeneous and Heterogeneous Asynchronous Optimization Is Surprisingly Difficult
Sep 15, 2026Alexander TyurinHeterogeneity Stochastic Gradient Descent
AXXX, Moscow, Russia · Applied AI Institute, Moscow, Russia
Breaking the 1.58-bit Barrier for Ternary LLMs
Sep 14, 2026Evangelos Georganas, Alexander Heinecke, Pradeep DubeyLarge Language Model Quantization Model Weights
Intel Corporation
Representation-based Masked Diffusion Model
Sep 14, 2026Yangrong Hu, Ding Huang, Xueyu Zhou +1Masked Diffusion Language Models Diffusion Models
Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University, Hong Kong SAR, China · Department of Applied Mathematics The Hong Kong Polytechnic University, Hong Kong SAR, China · ByteDance Seed, Beijing, China
AUC Maximization from Biased Positive-unlabeled Data with Confidence
Sep 12, 2026Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi +3Positive--Unlabeled Learning Receiver Operating Characteristic Area Under The Curve
NTT, Inc.
Closed-Form of the Local Galactic Potential and Stellar Distribution Function from Gaia DR3
Sep 8, 2026Indranil Das, Adam Kamoski, Dora Demiri +3General Relativity Fokker--Planck Equation
Department of Physics University of Massachusetts Boston NSF Institute for AI and Fundamental Interactions · Lebedev Physical Institute Russian Academy of Sciences Moscow 119991, Russia
From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix
Sep 1, 2026Olga Tsymboi, Dmitrii Stoianov, Ramil Latypov +11Post-Training Dialogue Benchmarks
T-Tech
Staged Linguistic Seeding: Grounded Query Expansion for Verified-Unit QA in AI Contact Centers
Sep 1, 2026Hyeonseop Yoon, Jeong-Eun ParkQuestion-Answer Pairs Question-Answering Benchmarks
Independent Researcher Seoul, South Korea
Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning
Aug 13, 2026Yikai Xu, Zhao Chen, Jian HuangOutliers Wasserstein Distance
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China. · School of Data Science, Fudan University, Shanghai, China. · Department of Data Science and Artificial Intelligence, and Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China.
Distribution Steering via Sliced Optimal Transport Control
Aug 13, 2026Kaito Ito, Anqi DongOptimal Transport Approach Optimal Control
Department of Information Physics and Computing The University of Tokyo, Tokyo 113-8654, Japan · Department of Mathematics and Digital Futures KTH Royal Institute of Technology, 100 44 Stockholm, Sweden
Proportional Analogies on Probability Distributions via Bayesian Updating
Aug 12, 2026Pierre-Alexandre MurenaAnalogy Distributions
Hamburg University of Technology (TUHH) Research Group on Human-Centric Machine Learning
A Local Sinkhorn Framework for Conditional Distribution Reconstruction of Multidimensional Random Fields
Aug 12, 2026Mingtao Xia, Qijing ShenFused Sinkhorn-Localized Similarity Conditional Distribution
School of Mathematics, University of Birmingham, Watson Building, Edgbaston, Birmingham, B15 2TT, United Kingdom · Department of Mathematics, University of Houston, 3551 Cullen Blvd, Houston, 77204, Texas, United States · Nuffield Department of Medicine, University of Oxford, United Kingdom, Old Road Campus, Oxford, OX3 7BN, United Kingdom
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
Do AI Forecast Ensembles Sample the Correct Conditional Distribution?
Aug 9, 2026Lucas J. Howard, Elizabeth A. BarnesLarge Language Model Forecasting Conditional Distribution
Boston University, Faculty of Computing and Data Sciences
A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning
Aug 9, 2026Jianhan Zhang, Jitao Wang, John D. Piette +3Counterfactual Learning Distributional Reinforcement Learning
University of Michigan · LSE
Artificial Intelligence Can Match Domain Experts in Evidence Extraction and Critical Appraisal of Microbial Oncogenesis Research Publications
Aug 7, 2026Kaela Kokkas, Hairong Wang, Richard Klein +11Systematic Review Biomedical Text
Department of Clinical Microbiology and Infectious Diseases, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa · School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa · Wits Machine Intelligence and Neural Discovery (MIND) Institute, University of the Witwatersrand, Johannesburg, South Africa +13
FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks
Aug 7, 2026Majid Kundroo, Tinku Singh, Taehong KimFederated Learning Loss Function
aSchool of Information and Communication Engineering, Chungbuk National University, Cheongju, 28644, Republic of Korea · bSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida, 201310, India
LLM Inference Under Bursty Workload Distribution: Modifying the WAIT Algorithm
Aug 6, 2026Anjali Gangadhar Katageria, Shobha Rani, Raghu Nandan SenguptaWorkload Queue Control
Department of Mathematics, Indian Institute of Technology Dharwad, Dharwad, Karnataka, India · Department of Management Sciences, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
Diverse and Plausible Algorithmic Recourse via Tractable Recourse Distributions
Aug 5, 2026Anagha Sabu, Hrithik Suresh, Narayanan C. KrishnanAlgorithmic Cores Distributions
Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Palakkad Palakkad, Kerala, India
GenOS: Compositional Certificates for Semantic Robustness in AI Code Generation
Aug 4, 2026Corrado PriamiAgentic Workflows Certificates
Dipartimento di Informatica, Università di Pisa and Fondazione Start Attractor
Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning
Aug 4, 2026Kejian Zhu, Zhuoran Jin, Dongqi Huang +4Multimodal Agents Synthetic Environments
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts
Aug 4, 2026Malena Loza, Felipe Grijalva, Eva Milara +3Tabular Learning Distribution Shifts
Colegio de Ciencias e Ingenierías, Universidad San Francisco de Quito (USFQ), Quito, Ecuador · Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain · Faculty of Experimental Sciences, Universidad Francisco de Vitoria, Madrid, Spain
GraspMeanFlow: SE(3)-Equivariant MeanFlow for Few-Step 6-DoF Grasp Generation
Aug 4, 2026Jiyong Kwon, Yikun Bai, Amirhossein Mollaali +1Robotic Grasping Meanflow
School of Mechanical Engineering, Purdue University · Department of Mathematics, Purdue University
A Simple Approximation to the Distribution of the Ridge Regression Estimator
Aug 3, 2026José Luis Montiel Olea, Ryan Strong, Amilcar Velez +2Kernel Ridge Regression Heteroskedasticity
Department of Economics at Cornell University
Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning
Aug 3, 2026Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei ChenFederated Learning Distributions
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Learning the Pareto Frontier of Predictive Models under Distribution Shift
Aug 1, 2026Yiming Dong, Jiwei Zhao, Yang Young LuDistribution Shifts Target Domain
Department of Biostatistics & Medical Informatics, University of Wisconsin–Madison · Department of Statistics, University of Wisconsin–Madison · Department of Biomedical Engineering, University of Wisconsin–Madison
Visual Distribution Anchoring for Efficient Prompt Tuning
Jul 31, 2026Pouya Parsa, Raoof Zare Moayedi, Seongjin ChoiVision-Language Model Adaptation Soft Prompt Tuning
University of Minnesota USA
Rethinking Total Absorption Gamma Spectroscopy Deconvolution: Supervised Machine Learning vs Response-Matrix Methods
Jul 30, 2026J. Balibrea-Correa, E. N{á}cher, C. Fonseca-Vargas +1Cosmic-Ray Syntactic Pool Laser-Induced Breakdown Spectroscopy
Instituto de Física Corpuscular, CSIC - Universidad de Valencia, Spain, 46980, Valencia, Spain
LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models
Jul 28, 2026Bowen Chen, Shreshth Saini, Balu Adsumilli +1Diffusion Models High Dynamic Range
The University of Texas at Austin · Google/YouTube · University of Colorado Boulder
Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance
Jul 28, 2026Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch +4Bayesian Neural Networks Variational Inference
Scientific Computing Center, Karlsruhe Institute of Technology · Scientific Computing Center, Karlsruhe Institute of Technology; and Helmholtz AI
Instruction-Tuned Language Models Cannot Sample from Distributions They Can Describe
Jul 28, 2026Chaemin Jang, Dongman Lee, Jihee KimInstruction-Tuned Models Persona Consistency
School of Computing, KAIST · School of Business and Technology Management, KAIST
Generative Distributionally Robust Optimization
Jul 27, 2026Ziwei Zhang, Jonathan Yu-Meng Li, Zhihao JinDistributionally-Robust Optimization Robust Optimization
1Telfer School of Management, University of Ottawa · Department of Electrical and Computer Engineering, Western University
Test-Time Adaptation via Dual Distillation for Videos Under Severe Distribution Shifts
Jul 27, 2026André Sacilotti, Samuel Felipe dos Santos, Jurandy AlmeidaTest-Time Adaptation Open-Vocabulary Action Recognition
University of São Paulo São Carlos, São Paulo, Brazil · Federal University of São Carlos Sorocaba, São Paulo, Brazil
Why does Greedy Search produce Optimal Clustering Outcomes? A Fixed-Core Assignment Theory
Jul 27, 2026Kaifeng Zhang, Kai Ming Ting, Sanjay ChawlaClustering Greedy Algorithm
School of Artificial Intelligence Nanjing University, China · Qatar Computing Research Institute, HBKU
D3O: Dynamic Distribution Distillation for Ordinal Regression
Jul 26, 2026Chunlai Dong, Yaojun Hu, Yuyang Xu +2Dataset Distillation Probe-Logit Distillation
Zhejiang University Hangzhou, China
Distributional Determinantal Point Process for Repulsive Clustering of Distributions
Jul 23, 2026Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga +1Determinantal Point Process Mixture Models
Department of Statistics and Data Sciences, University of Texas at Austin · Department of Biostatistics and Informatics, University of Colorado Anschutz · Department of Mathematics, University of Texas at Austin
Long-Term Sequential Decision Making under Risk
Jul 22, 2026Irmaan, Mirzanejad, Nadjet Bourdache +1Target Risk Sequential Decision Making
Mohammad · Université de Caen Normandie, ENSICAEN, CNRS, Normandie Univ, GREYC UMR 6072, France
Analytic Distribution of Classifier-Free Guidance for Schedule Design
Jul 22, 2026Enze Jiang, Zheng MaClassifier-Free Guidance Distributions
School of Mathematical Sciences Shanghai Jiao Tong University Shanghai, China

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Learning to Predict Distributions over Weight Updates for Test-Time Adaptation

Azal Ahmad Khan, Keshav Ramji, Tahira Naseem, Ali Anwar+1

University of Minnesota · IBM Research AI

Large Language Model Adaptation Hypernetwork Distributions

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Hypernetworks have recently shown success in dynamically adapting the parameters of Large Language Models (LLMs) at runtime based on signals such as task descriptions or additional demostrations. Here we ask: how much adaptation signal can be obtained using only the input query to an LLM?. To answer this, we study query-conditioned Hypernetworks for LoRA estimation. Further, we introduce distributional Hypernetworks, able to produce not only point estimates of parameter adaptors, but also a distribution over possible LoRAs. For this we propose a simple end-to-end loss using a differentiable Monte Carlo approximation and explore multiple distribution parametrizations including regression and convex combination variants. Results show that even using the mean of the learned distribution can outperform deterministic hypernetworks. Crucially, the learned distribution enables a different form of test-time scaling: instead of spending additional compute only by sampling more token sequences from a fixed model, we sample weight updates, yielding multiple adapted models for the same query. Performance improves as more weight samples are considered and remains stronger than corresponding token-sampling adaptation baselines. Finally, we find that generated updates can transfer across queries, suggesting that the hypernetwork learns reusable structure in how the model should adapt. Together, these results show that query-conditioned distributions over weight updates can support both adaptation and test-time scaling.

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