Latest Machine Learning Research Papers

The newest Machine Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Machine Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Investigating the effects of vaping on lung structure and function with pulmonary imaging
  A. H. SCHMIDT · cIRcle (University of Briti... · Jan 1, 2027

  The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires....

• LGAN: An Efficient High-Order Graph Neural Network via the Line Graph Aggregation
  Lin Du, Lu Bai, Jincheng Li, Lixin Cui et al. · AAAI 2026 · Dec 31, 2026

  Graph Neural Networks (GNNs) have emerged as a dominant paradigm for graph classification. Specifically, most existing GNNs mainly rely on the message passing strategy between neighbor nodes, where the expressivity is limited by the 1-dimen…

• Exposure with response prevention in virtual reality for obsessive-compulsive disorder: A randomized controlled trial
  L. Rolvien, L. Jelinek, L. Lohse, S. Moritz et al. · MPG.PuRe (Max Planck Society) · Dec 31, 2026
• The Mediation Effect of Price to Book Value on Financial Ratio to Stock Return
  Almuzayyad Almuzayyad, Guntur Kusuma Wardana Guntur Kusuma Wardana · Research Repository Univers... · Dec 30, 2026

  Stock returns are an essential indicator for investors in assessing the performance of Islamic financial institutions listed on the Indonesia Stock Exchange (IDX). This study examines the effect of Return on Assets (ROA), Debt to Equity Rat…

• 3D-Aware VLMs with Implicit and Explicit Geometries
  Wenhao Li, Xueying Jiang, Quanhao Qian, Deli Zhao et al. · arXiv · Jul 23, 2026

  Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tasks that require fine-grained spatial understanding and reasoning. To bridge this gap, we present VLM-…

• Expanding Flow Maps
  Sophia Tang, Pranam Chatterjee · arXiv · Jul 23, 2026

  Flow-based generative models have enabled remarkable progress in fast and controllable generation across continuous and discrete state spaces, yet existing parameterizations are constrained to fixed dimensions or fixed sequence lengths. Her…

• Barzilai-Borwein Fails Superlinear Convergence on an Open Set of Quadratics for Every Dimension $n\geq 4$
  Dawei Li, Xiaotian Jiang, Mingyi Hong · arXiv · Jul 23, 2026

  Barzilai--Borwein (BB) method has shown strong practical performance in continuous optimization, yet its convergence dynamics remains poorly understood. In particular, a central unresolved question is whether BB converges superlinearly for …

• Synthetic data generation framework for quality control automation in gravure printing
  Korota Arsène Coulibaly, Mohamed Hamlich, Khalid Hmali, Andrea Trombin · arXiv · Jul 23, 2026

  Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection. Automated surface defect detection is critical for maintaining high-quality standards in rotogravure printin…

• Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity
  Hongnan Ma, Yiwei Shi, Mengyue Yang, Weiru Liu · arXiv · Jul 23, 2026

  Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction, but also necessary for maintaining it. However, existing sufficiency-oriented methods can…

• Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling
  Aaron Feller, Kris Deibler, Maxim Secor · arXiv · Jul 23, 2026

  Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembles in solution. We introduce EnsembleEGNN, a molecular ensemble foundation model that en…

• MIRROR: Learning from the Other View for Multi-Modal Reasoning
  Wen Ye, Yuxiao Qu, Aviral Kumar, Xuezhe Ma · arXiv · Jul 23, 2026

  Unlike large language models (LLMs) that exhibit strong reasoning capabilities, vision-language models (VLMs) struggle with visual reasoning, even on geometry problems that admit equivalent text, diagram, and combined diagram+text views. We…

• X$^3$-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment
  Dongjie Fu, Di Cao, Xize Cheng, Zihan Zhang et al. · arXiv · Jul 23, 2026

  While large audio-language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning, primarily due to the scarcity of high-quality audio reasoning data…

• Neural solutions of coupled ghost and gluon Dyson--Schwinger equations in Landau gauge
  Rodrigo Carmo Terin · arXiv · Jul 23, 2026

  The coupled ghost and gluon Dyson--Schwinger equations (DSEs) of four-dimensional Landau-gauge Yang--Mills (YM) theory are solved with a neural representation trained only from renormalized equation residuals. The neural and fixed-point sol…

• The Boundaries of Automation: A Theory of Persistent Human Participation
  Fares Fourati, Hinrich Schütze, Eyke Hüllermeier, Iryna Gurevych · arXiv · Jul 23, 2026

  The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible. Implicit in this pursuit is the assumption that humans remain in the loop only because curren…

• Zero-Flow Two-Sample Tests
  Yakun Wang, Leyang Wang, Song Liu, Taiji Suzuki · arXiv · Jul 23, 2026

  We propose a new approach to two-sample testing for deciding whether two sets of samples are drawn from the same distribution. The test is built on a statistical discrepancy based on the zero-flow criterion, termed zero-flow discrepancy (ZF…

• Windowed-MTP: Removing the Full-Context Draft-KV Tax at Million-Token Context
  Alagappan Valliappan · arXiv · Jul 23, 2026

  Speculative decoding accelerates autoregressive generation by having a cheap draft propose tokens that a target verifies in parallel. Frontier models increasingly ship a built-in Multi-Token-Prediction (MTP/NEXTN) draft head under the assum…

• Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models
  Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao · arXiv · Jul 23, 2026

  Cognitive impairment (CI) is a growing public health concern. Early and accurate diagnosis is critical for enabling timely intervention and improving patient outcomes. Speech-based CI detection has emerged as a promising non-invasive approa…

• What, Where, and How: Disentangling the Roles of Task, Language, and Model in Code Model Representations
  Piotr Wilam · arXiv · Jul 23, 2026

  Do independently trained language models come to represent the same thing in the same way? We answer for code, extending a recently introduced concept-circuit extraction method to a 2x2 design -- Python and Rust crossed with Qwen2.5-Coder-7…

• Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
  Gil Lifshits, Igal Bilik, Gilad Katz · arXiv · Jul 23, 2026

  Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information…

• Finite-Sample Coverage Audits for High-Recall Candidate Generation: Certification and Learning-Theoretic Design
  Martin Anthony, Kaveh Salehzadeh Nobari · arXiv · Jul 23, 2026

  An initial high-recall stage in an empirical pipeline decides which items pass to later review, labelling, or modelling, and relevant items it misses are lost to every subsequent stage. We study how many audit labels are needed to certify, …

• Error Certificates for KV-Cache Eviction via Randomized Design
  Peng Xie · arXiv · Jul 23, 2026

  Deterministic KV-cache eviction keeps the top-$k$ tokens under an importance score and deletes the rest. We prove that this design cannot know what it destroyed: evicted values can be altered so that everything the serving system retains is…

• Test-Time Scaling via Error Localization
  Rajiv Shailesh Chitale, Rahul Madhavan, Taneesh Gupta, Deepanway Ghosal et al. · arXiv · Jul 23, 2026

  Scaling inference-time computation has emerged as a reliable method to improve the performance of large language models on complex reasoning and programming tasks. However, standard approaches such as independent sampling and sequential mul…

• KroQuant: Kronecker-Structured Block Transforms for Efficient Post-Training Quantization of Diffusion Transformers
  Yann Bouquet, Alireza Khodamoradi, Kristof Denolf, Mathieu Salzmann · arXiv · Jul 23, 2026

  Post-training quantization (PTQ) of diffusion transformers (DiTs) to W4A4 severely degrades output quality, because activations entering each linear layer contain outliers that 4-bit formats cannot represent. The standard fix applies an inv…

• Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble framework for preventive maintenance and low-carbon mobility
  Cande Lian, Wentao Zeng, Jiabin Wu, Yiming Bie et al. · arXiv · Jul 23, 2026

  Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and humidity increasingly raises equipment fault risk and undermi…

• Token Budget Saturation and Mechanistic Early Detection of Reasoning Non-Convergence in Chain-of-Thought Models
  Renuka Oladri, Niveda Jawahar, Abdirisak Mohamed · arXiv · Jul 23, 2026

  Chain-of-thought reasoning models such as DeepSeek-R1-Distill-Qwen-7B exhibit a bimodal convergence pattern: generations either terminate within a token budget (converged) or exhaust it without reaching a conclusion (non-converged). We char…

• Lipschitzian SLLNs for random functions
  Lai Tian, Johannes O. Royset · arXiv · Jul 22, 2026

  We prove strong laws of large numbers for locally Lipschitz functions in the Lipschitz pseudometric. Our results hold under either a topological or a model-theoretic condition, with the latter encompassing functions jointly definable in o-m…

• Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning
  Nicolas Kosanovic, Jordan Dowdy, Jean Chagas Vaz · arXiv · Jul 22, 2026

  Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments. A popular control method used by manufacturers utilizes Virtual Reality for upper-body teleope…

• PG-KINN: A Physics-Informed Petrov-Galerkin Kolmogorov-Arnold Network for Solving Forward and Inverse PDEs
  Amirhossein Sadr, Nima Soltani, Vahideh Moghtadaiee, Aida Pakniyat et al. · arXiv · Jul 22, 2026

  Physics-informed learning of partial differential equations (PDEs) has been dominated by multilayer perceptrons (MLPs), whose spectral bias and dense parameterization limit both accuracy and interpretability. Kolmogorov Arnold Networks (KAN…

• Statevector-Referenced Geometry Survival of a Four-Qubit ZZ Quantum Kernel on IBM Quantum Hardware: A Fixed-Subset Diagnostic Across Three Execution Configurations
  Rostyslav Sipakov · arXiv · Jul 22, 2026

  Quantum-kernel methods encode a dataset's geometry in a Gram matrix, so learning claims on hardware kernels assume the intended geometry survives execution. We measure that survival for one frozen four-qubit ZZ feature-map kernel on $N=24$ …

• Online Variance Reduction for Domain Adaptation on Streaming Data
  Andrea Napoli · arXiv · Jul 22, 2026

  This paper studies the problem of stochastic variance reduction (SVR) for the maximum mean discrepancy (MMD) and correlation alignment (CORAL) loss functions. Although various offline SVR algorithms for these losses have been proposed, thes…