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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 176
HullProbe: Measuring Interventional Part Reliance in Maritime Vessel Classifiers
Recognizing a vessel's type from an image supports maritime surveillance and sanctions monitoring, but a classifier can reach high accuracy by leaning on the generic hull instead of the more…Spectral Adaptive Graph Transformer for Efficient and Scalable Traffic Forecasting
Traffic forecasting models based on spectral graph convolution offer an interpretable account of how information is mixed between sensors, but they rely on an eigendecomposition of the graph…FHKAN: Fractional Hermite Kolmogorov-Arnold Networks for Data-Driven and Physics-Informed Learning
Kolmogorov-Arnold Networks (KANs) replace fixed scalar weights by learnable univariate maps, yet most spectral KANs still rely on Chebyshev polynomials that live on a bounded interval and therefore…Learning without Overwriting: Structural Knowledge Fusion for Continual Learning
Catastrophic forgetting in sequential deep learning arises from a fixed-capacity architectural constraint: parameter updates encoding new tasks perturb weight subspaces supporting prior…Indistinguishable Agents: Training Agent Privacy as Twin-World Indistinguishability
LLM agents search, shop and send messages for their users, and every action is seen by some party.M-JEPA: Predictive Self-Supervised Learning for Molecular Graphs with Scaffold-Shift Evaluation on Tox21
Self-supervised molecular representation learning is increasingly used to improve transfer on small, label-limited property prediction tasks, yet commonly used contrastive objectives can be…World Models, Trees and Foundation Models for Retail Forecasting: Evidence from Four Public Data Sets
A retailer makes different decisions at different grains of the product, store and time hierarchy: store orders for an item in a store, supplier funding for an item across stores, category…An Information-Geometric Framework for OCS-Reconfigurable Interconnect Topology in Large-Scale AI Clusters
Optical Circuit Switches (OCS) make inter-pod wiring in AI clusters programmable, posing design questions on wiring choice, symmetry departure, and reconfigurations.Dialogue Generation for Conversational Recommendation Using User Search Interactions and Knowledge Graphs
Large-scale conversational recommendation datasets typically require manual dialogue construction or annotation.Sampling and Curvature in First-Exit Comparisons for Momentum SGD
First-exit prediction for momentum SGD depends on sampling information that pointwise gradient laws omit.Shallow Probes Are Sufficient for Near-Oracle Personalization Depth Allocation in Federated Learning
Rather than proposing a new federated optimizer, we study whether a small client-specific measurement budget can support cost-aware personalization-depth decisions under a utility that trades…ChronoChart: Setup-Agnostic Channel Charting for Label-Free Indoor Localization
Accurate radio-based indoor localization remains challenging in multipath-rich and dynamic indoor environments.RAIL-5G: A Challenging Real-World Dataset and Benchmark for AI-Based 5G Indoor Localization
Reliable radio-based indoor localization is essential for industrial automation, yet existing methods are often evaluated in environments that do not fully reflect the severe multipath and…ViCoR: Estimating Visual Necessity via Counterfactual Residuals for Multimodal Medical Data Selection
Medical vision-language models often exhibit hidden stratification, where high overall performance masks failures on subtle visual findings and overlapping clinical conditions.NeuroAct: Advancing EEG Analysis and Decoding via Dual-Loop Autonomous Research
EEG analysis and decoding underpin brain-computer interfaces, cognitive assessment, and sleep monitoring.Calibration-Free Early Stopping for Chain-of-Thought Reasoning via E-processes
Reasoning large language models (LLMs) benefit from test-time scaling, motivating adaptive policies that spend tokens when they help and stop early otherwise.LiftDiff: Guided Discrete Diffusion for Quantum Code Construction
The realization of fault-tolerant quantum computation relies on Quantum Error-Correcting Codes (QECC).DROD: Dual-Ratio Ordered Dropout for Federated Continual Learning on Heterogeneous Devices
Federated learning for capacity-heterogeneous clients and federated continual learning are each well studied on their own, but their intersection -- clients holding differently sized models while…Assessing the Empirical Evidence for Loss of Control from Agentic General-Purpose AI
Theoretical literature warns that advanced generalist agentic systems might pursue instrumental goals that are misaligned with human preferences, thus posing risks of irreversible loss of human…Estimating Causal Bounds from Observational Data With Neural Generative Models
Identifying causal bounds from discrete observational data is a fundamental problem in causal inference.