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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 120
Implicit Neural Representations for Variational Problems on Graphons
We propose a neural exploratory framework for variational problems over graphons, i.e., symmetric measurable functions $[0,1]^2 \to [0,1]$, that arise as limits of dense graph sequences.Sparsity-driven emergence of functional modularity in multitask neural networks
Neural networks can learn multiple tasks, but how learning organizes shared computation into specialized and reusable components remains poorly understood.Solving and Learning Navier-Stokes Dynamics with the Neural Basis Method
Physics-informed machine learning has become a major direction in predictive modeling of fluid dynamics, combining governing physics with learned representations.Curvature-Aware Clipping: Mitigating Bias in DPSGD via Approximate Second-Order Information
Differentially Private Stochastic Gradient Descent (DPSGD) has become the de facto standard for training deep neural networks with rigorous privacy guarantees.$Ψ$-0.6: Adaptive Streams of Thought in a Unified Vision-Language World Model
Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds.Federated Nonparametric Regression with Heterogeneous Clients
Whether a federated predictor benefits an individual client depends on both population heterogeneity and estimation uncertainty.Searching with Sparse Histories: Cross-Task Architecture Search from Incomplete Search Logs and Zero-Cost Proxies
Neural architecture search (NAS) is expensive, but cross-task transfer can reduce costs by reusing knowledge from previous tasks.Not All Information Is Worth Asking For: Decision-Informed Information Seeking with LLMs
Large language models (LLMs) are increasingly used to support decisions under incomplete information, where deciding what to look up and when to stop matters as much as the final answer.Depth-Dependent Safety Geometry in Vision-Language Models: Layer-Wise Localization and Bidirectional Refusal Control
Vision-Language Models (VLMs) are widely deployed, yet the internal mechanisms behind their safety behavior remain poorly understood.CFBrainAug: Modularity-Induced Counterfactual Brain Network Augmentation for Interpretable fMRI-based Autism Diagnosis
Machine learning on resting-state functional magnetic resonance imaging (rs-fMRI) brain networks is promising for diagnosing Autism Spectrum Disorder (ASD), but existing methods are prone to…EVOLVE: Uncertainty-Aware Multivariate Time Series Anomaly Prediction with Adaptive Prototype Memories
Unsupervised multivariate time series anomaly prediction (AP) aims to estimate future anomaly risks from current observations, providing early-warning signals for proactive intervention and…Dual-Adaptive Z-Score PPO: Dynamic Clipping Using Action Confidence and Normalized Advantage Magnitude
Proximal Policy Optimization (PPO) remains a popular on-policy reinforcement learning algorithm owing to its stability and ease of implementation.Principia: Test-Time Hypotheses to Principle Construction for Scientific Discovery
Scientific discovery requires identifying the scientific principles underlying observed phenomena to guide further investigation.SWE-Shift: Evaluating Coding Agents in Evolving Workspaces
Repository-level coding benchmarks largely assume that tasks and environments remain unchanged throughout execution.Neural Disjunctive Normal Forms for Learning Exact Rank Filters
Gradient-based learning of morphological operators is hindered by locally sparse derivatives with respect to the structuring elements.Do VLAs Know They Will Fail? Interpretable Success and Failure Patterns in Physical AI Systems
Vision-Language-Action (VLA) models have emerged as powerful general-purpose robot policies, but their successes and failures remain difficult to diagnose and understand.Head Dimension Matters in Language Models for Long-Context Retrieval
Multi-head attention retrieves and combines context representations through query--key interactions and value aggregation, with head dimension constraining the per-head representation space.Democratizing LLM Training with the Edge: Challenges, Systems, and the Road Ahead
The growing computational power of modern GPU data centers has let a few industrial giants train and deploy large language models (LLMs) on massive amounts of data.RoboCode: Learning Reusable Action Primitives for Robot Manipulation
Recent robot manipulation policies commonly predict continuous action chunks directly from observations, without explicitly modeling reusable structure in the action space.TileShare: Training-Free Acceleration of Overlapping Tiled ViT Decoding
Fixed-window Vision Transformer (ViT) decoders run at deployment resolution as overlapping tiles: every token in an overlap band is processed by several full decoder instances.