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5 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Graph Representation via Elements of Discrete Morse and Cobordism Theories
Oct 1, 2026Jennifer Rozenblit, Chenguang Yang, Yuxin Liu +2Topology Graph Representation Learning
Department of Mathematics; University of Texas, Austin · Department of Statistics; University of California, Riverside · Department of Statistics; Virginia Tech
Can large language models unlock discrete data in ophthalmic diagnostic reports?
Sep 30, 2026Umair A. Zaidi, An-Lun Wu, Wei-Chun Lin +2Ophthalmology Medical Report Generation
Division of Informatics, Clinical Epidemiology and Translational Data Science, Oregon Health & Science University, Portland, OR · Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, OR · Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL
Data-driven discrete-time deep recurrent neural network-based modeling for dissipative systems
Sep 23, 2026Tuan Luong, Hyungpil MoonParametric Physics-Informed Neural Network Recurrent Neural Networks
Department of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
Statistical Rates for Entropic Optimal Transport in the Discrete to SubGaussian Regime
Sep 22, 2026Tomas Gonzalez, Gonzalo MenaOptimal Transport Approach Non-Gaussianity
Department of Machine Learning, Carnegie Mellon University · Department of Statistics & Data Science, Carnegie Mellon University
Discrete vs. Continuous: A Comprehensive Study of Unified Audio Understanding in LALMs
Sep 19, 2026Jing Peng, Zichao Nie, Zhisheng Zhang +2Large Audio Language Models Audio Understanding
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Optimal estimation for Functional Linear Regression with Noisy Discretized Data
Sep 8, 2026Sixtine SphabmixayCovariates Noisy Data
Université Paris Cité, CNRS, MAP5, F-75006 Paris, France
Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings
Sep 2, 2026Frank Hu, Shriram Chennakesavalu, David GraffAdaptive Optimizers Optimization Modeling
Prescient Design, Genentech South San Francisco, CA, USA
Latent-Kernel Discrete Flow Maps for Few-Step Generation
Jul 29, 2026Mansoor Ahmed, Yue-Tsz Fan, Hemanth Venkateswara +1Latent Flow Few-Step Generation
Georgia State University, Atlanta, GA, USA · Georgia Institute of Technology, Atlanta, GA, USA
Born Discrete, Made Smooth: Variational Formulation of Shallow Neural Networks
Jul 2, 2026Matej Benko, Pierre Bousquet, Iwona Chlebicka +1Variational Neural Network
Institute of Mathematics, Faculty of Mechanical Engineering, Brno University of Technology · Technická 2896/2, 616 69, Brno, Czech Republic · Université de Toulouse, INSA Toulouse, CNRS, IMT, F-31062 Toulouse Cedex 9, France +2
Discrete Diffusion Language Models for Interactive Radiology Report Drafting
Jul 1, 2026Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge +1Radiology Report Generation Diffusion Language Models
Department of Biomedical Data Science, Stanford University School of Medicine · Department of Mathematical Modelling, Statistics & Bioinformatics, Ghent University · Department of Electrical Engineering, Stanford University
HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models
Jun 26, 2026Artem Ploujnikov, Francesco Verdini, Samir Sadok +1Discrete Speech Representations Hybrid Transformer
Mila, Quebec AI Institute, Canada · Concordia University, Canada · Sapienza University of Rome, Italy +1
Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data
Jun 25, 2026Erhan Bayraktar, Martin Hernandez, Qinxin Yan +1Stochastic Optimal Control Model-Free
Department of Mathematics, University of Michigan, Ann Arbor, MI, USA. · Department of Statistics and Data Science, University of California, Los Angeles, CA, USA. · Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.
OmniPMNet: Bridging discrete and gridded PM10 forecasts via omni-query neural processes
Jun 12, 2026Shuangshuang He, Shuo WangAir Pollution Spatio-Temporal Forecasting
ColorfulClouds Technology Co., Ltd., 100875 Beijing, China. · School of Systems Science, Beijing Normal University, 100875 Beijing, China. · D-ITET, ETH Zurich, 8092 Zurich, Switzerland.
IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
Jun 9, 2026Yitong Chen, Zijie Diao, Junke Wang +5Autoregressive Image Generation Autoencoder Architectures
Institute of Trustworthy Embodied AI, Fudan University · Shanghai Innovation Institute · University of Maryland, College Park
KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data
Jun 9, 2026Guoliang Xu, James E. CorterBayesian Networks Graph Representation Learning
Columbia University
Geometry of Semantic Space: Comparative Study of Discrete and Continuous Models
Jun 5, 2026Gabriel Bounias, Sabine PlouxContextual Embeddings Semantic Relationships
ISC-PIF (Institut des Systemes Complexes de Paris IdF), CNRS, France · CAMS (Centre d’analyse et de mathématique sociales), CNRS & EHESS, Paris, France
Bregman meets Lévy: Stochastic mirror descent with heavy-tailed noise in continuous and discrete time
Jun 2, 2026Pierre-Louis Cauvin, Panayotis MertikopoulosMirror Descent Stochastic Gradient Descent
Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France.
Masked Diffusion Modeling for Anomaly Detection
May 28, 2026Lixing Zhang, Yuchen Liang, Liyan XieGraph Anomaly Detection Anomaly Score
University of Minnesota · Ohio State University
When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
May 22, 2026Boxiao Wang, Kai Li, Zhiwei Chen +5Symbolic Regression Unified Path-Integral Update
Institute of Automation, Chinese Academy of Sciences
Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
May 14, 2026Xun Fang, Yunchen Li, Hang Yuan +1Diffusion Language Models Speculative Decoding
East China Normal University · Beijing Zhongguancun Academy
VRA: Grounding Discrete-Time Joint Acceleration in Voltage-Constrained Actuation
May 11, 2026Lingwei Zhang, Jiaming Wang, Tianlin Zhang +5Actuator Acceleration
Department of Mechanical and Automation Engineering; Hong Kong Embodied AI Lab, The Chinese University of Hong Kong
Kinetic-Optimal Scheduling with Moment Correction for Metric-Induced Discrete Flow Matching in Zero-Shot Text-to-Speech
May 10, 2026Dong Yang, Yiyi Cai, Haoyu Zhang +2Flow-Matching Text-To-Speech Speaker Similarity
1The University of Tokyo · 2Independent Researcher
Discrete Langevin-Inspired Posterior Sampling
May 10, 2026Chaitanya Amballa, Sattwik Basu, Jorge Vančo Sampedro +1Posterior Sampling Bayesian Inverse Problems
University of Illinois Urbana-Champaign
Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency
May 8, 2026Yuxiang Luo, Andrew PerraultNeural Ordinary Differential Equations Velocity Field
Department of Computer Science and Engineering The Ohio State University
Continuous First, Discrete Later: VQ-VAEs Without Dimensional Collapse
May 7, 2026Xinyu Zhao, Nikita Karagodin, Hamed Hassani +3Vector-Quantized Variational Autoencoder Codebooks
MIT
Discrete Elastic Ribbons: A Unified Discrete Differential Geometry Framework for One-Dimensional Energy Models
May 7, 2026Shivam Kumar Panda, M Khalid JawedCosserat Rod Theory Finite Element Method
University of California, Los Angeles, 420 Westwood Plaza, Los Angeles, 90095, CA, United States of America
Skipping the Zeros in Diffusion Models for Sparse Data Generation
May 3, 2026Phil Sidney Ostheimer, Mayank Nagda, Andriy Balinskyy +6Diffusion Models Sparsity
1RPTU University Kaiserslautern-Landau · 2Heidelberg Univer · University of California, Irvine.
Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots
May 1, 2026Junda Ying, Yuxuan Wang, Bowen Yang +2Schrödinger Bridges Population Dynamics
Beijing International Center for Mathematical Research, Peking University · Center for Data Science, Peking University · School of Mathematical Sciences, Peking University +4
DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors
Apr 27, 2026Pengcheng Wang, Kaiwen Hong, Chensheng Peng +4Diffusion Policies Asynchronous Execution
UC Berkeley1 · UIUC2 · UT Austin3 +1
Properties and limitations of geometric tempering for gradient flow dynamics
Apr 22, 2026Francesca Romana Crucinio, Sahani PathirajaWasserstein Gradient Flows Convergence
ESOMAS, University of Turin, Italy · Collegio Carlo Alberto, Turin, Italy · School of Mathematics & Statistics, UNSW Sydney, Australia
Achieving $\widetilde{O}(1/ε)$ Sample Complexity for Bilinear Systems Identification under Bounded Noises
Mar 21, 2026Hongyu Yi, Chenbei Lu, Jing YuSystem Identification Optimal Sample Complexity
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA. · Cornell University AI for Science Institute, Cornell University, Ithaca, NY, USA.
Beyond Discrete Samples: High Information Density Replay for Efficient Lifelong Person Re-Identification
Aug 3, 2025Mingyu Wang, Haojie Liu, Zhiyong Li +3Re-Identification Discrete
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Topological data analysis using persistent discrete homology
Jun 17, 2025Chris Kapulkin, Nathan KershawPersistent Homology Topology
Limits of Discrete Energy of Families of Increasing Sets
Apr 15, 2025Hari Sarang NathanFractal Dimension Discrete

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

Graph Representation via Elements of Discrete Morse and Cobordism Theories

Jennifer Rozenblit, Chenguang Yang, Yuxin Liu, Yuzhou Chen+1

Department of Mathematics; University of Texas, Austin · Department of Statistics; University of California, Riverside · Department of Statistics; Virginia Tech

Topology Graph Representation Learning Graph Representations Discrete

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Topology is, by its nature and design, suited to structure that is nonlinear, multiscale, and nonstationary - however, within machine learning, its use remains largely confined to topological data analysis. We advocate that tools from low-dimensional topology which have remained almost exclusively contained within the domain of pure mathematics (such as Morse theory) offer a strong, complementary, and yet virtually unexplored perspective on the hidden structure of data-generating processes and learning tasks built upon them. Here we introduce concepts from cobordism theory and harness tools from discrete Morse theory to improve the performance of graph diffusion models through our pipeline MG-Diff. Further, we derive theoretical guarantees and sufficient conditions so that under a positive decision-gap, the Morse-theoretic tools and their application for induced diffusion guidance are stable under small perturbations. Finally, we illustrate the utility of discrete Morse theory in application to graph diffusion models for spatio-temporal graph forecasting and graph regeneration, and argue that these applications are only a small window into the part of what low-dimensional topology can offer to the field of machine learning.

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