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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 155
ABACUS: A Benchmark for Image Editing Models to Assess Object Category and Counting Under Scene Conservation
Instruction-based image editing models can now add, remove a specified number of objects or change their category while preserving the scene.Stabilizing Filters for Graph Signal Generation
Generating graph signals is an important task in machine learning, and incorporating the underlying graph into a generative model enables structurally-informed distribution modeling.Manifold-Informed Neural Networks for Low-Label ECG Electrolyte Inference
Physics-informed neural networks (PINNs) supervise on the residual of a known differential operator.Observation Assimilation Transformer: Probabilistic Weather Analysis from Sparse Station Observations
We present the Observation Assimilation Transformer (OAT), a geometry-aware neural process that corrects and refines a prior numerical weather prediction (NWP) field into a high-resolution posterior…Identifying Dependent Components from Multi-domain Linear Mixtures
We study a linear mixing model with dependent latent components, assuming multiple data domains.Characterizing Cross-Relay Generalization Failure in Machine Learning-Based Power System Protection
Machine learning-based fault detectors are often evaluated at the relay locations used for training, although local voltage and current measurements vary across power networks.Beyond the Point Mass: Acceptance-Derived Weights and Distributional Losses for Speculative Decoding
In speculative decoding, a lightweight draft model proposes tokens that the target model verifies in parallel, speeding up generation without changing the target distribution.CountVideoBench: A Benchmark to Evaluate Prediction Biases on the Object Counting task by Video-Language Models
Object counting in videos remains a poorly characterized capability of multimodal large language models.Confessions of a Masked MDP
In reinforcement learning, one presumes the action set to be known ahead of time, and, thus, one must know if any state-action pairs are not permissible ahead of computations.Relative Representations Enable Portable White-Box Monitoring Across Language Models
Independently trained autoregressive language models often acquire similar capabilities, but it remains unclear whether their internal representations organize behaviorally relevant information in…Don't Skip the Baseline: A Controlled Comparison of Dynamic Expert Skipping in MoEs
A growing body of work proposes dynamic expert-skipping methods for Mixture-of-Experts (MoE) models that adjust the number of experts activated per token during inference, aiming to preserve model…Self-Credit: Credit Assignment for Long-Horizon Agentic RL via Intra-group Rescaling
LLM agents are commonly trained with RLVR, but the sparse nature of typical RLVR rewards makes credit assignment challenging, especially for long-horizon tasks.Rewards for Budget-Aware Multi-Turn Agents That Adapt to Arbitrary Test-Time Constraints
Large language model agents are often given an inference budget and asked to maximise success within it.Brain2Vec: A Self-Supervised 3D Foundation Model for Structural Brain MRI
Deep learning models have become essential tools in medical imaging, yet training them end-to-end for diverse tasks remains challenging due to limited labeled data and computational constraints.DISTRACT: Degradation-Indexed Study of Textual Reference Across Competing Targets
Vision-language trackers (VLTs) accept descriptions intended to assist tracking, but accepting text does not establish that its content changes tracking decisions, particularly as visual evidence…Region-Specific EEG Classification of Hypnotic Susceptibility Using a Sliding-Window 2D CNN: A Subject-Independent Evaluation
We investigate whether a 2D Convolutional Neural Network (CNN) trained on minimally preprocessed EEG signals can classify hypnotic susceptibility across distinct brain regions.Do Neural Models Prefer Their Submodels?
A neural model is the set of functions that can be expressed by a given neural network architecture.CNN-based classifier for subject-independent MBTI Extraversion/Introversion preference classification from short-time EEG signals
This study examines whether short, minimally preprocessed EEG segments contain sufficient information to distinguish between individuals classified as extraverted or introverted according to the…A Bochner Weitzenböck Decomposition for Cellular Sheaf Laplacians, Curvature and Neural Sheaf Diffusion
We extend the Bochner-Weitzenböck decomposition to cellular sheaf Laplacians, splitting it into a positive semidefinite term, the Bochner sheaf Laplacian, and a block diagonal term, the Forman…LocaLeJEPA: Maximum Entropy Representations under Geometric Priors
Joint-Embedding Predictive Architectures (JEPA) offer an appealing approach to self-supervised learning, but are typically prone to representation collapse.