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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 183
Modeling Uncertainty Propagation in 3D Morphable Models
We study the problem of mapping parameter-space variance of a 3D Morphable Model (3DMM) to per-vertex variance.EdgeKernelBench: Unified LLM-Driven Kernel Generation for Edge Backends
Recent works show that LLMs can generate kernels with strong performance on server GPUs.Good Judgment, Bad Advice: Decision-Model Controllers Raise Agent Cost Without Improving Success
Agent scaffolds increasingly delegate small decisions (whether to stop, verify, or change approach) to cheaper models.Lightweight Machine Learning for Classifying Disaster-Related Social Media Posts
Classifying disaster-related social-media posts into humanitarian information labels is challenging because individual messages are typically short and context-limited, humanitarian categories can…Can AI agents make law? Evaluating AI legal relevance and creativity
Large language models, the artificial intelligence (AI) systems behind modern chatbots, are trained on vast amounts of legal text, but can they build on it to write law for situations that text…What RL Brings to Reasoning: A Few Decisions the Base Model Would Not Make
Reinforcement learning with verifiable rewards (RLVR) substantially improves mathematical reasoning, but what it adds to the base model remains unclear.Knowledge Diversion: A Novel Paradigm for Pre-training Decomposable Image Generation Models
Large-scale pre-trained models have become foundational to modern machine learning, yet conventional pre-training objectives primarily optimize source-domain performance without explicitly…MM-LeJEPA: Correlated Gaussian Regularization for Multimodal Joint-Embedding Predictive Architectures
LeJEPA derives an isotropic Gaussian embedding target by minimizing, under its task assumptions, a Fisher information criterion that controls the task-averaged squared prediction bias.Contrastive Adversarial Training for Robust Graph Neural Networks under Label Poisoning
Graph Neural Networks (GNNs) are effective for modeling relational data but are vulnerable to label poisoning, where a small number of corrupted training labels can propagate errors across the graph…Geometry-Aware Interpolation for Inverse Problems
Recovering missing visual information from incomplete or degraded images remains a challenging problem, particularly when spatial variations and complex image structures affect reconstruction…Native Probability Outputs for Clinical Triage: An Evaluation of Calibration and Decision Accuracy
Patient-facing triage requires an appropriate care recommendation and recognition of when information is insufficient.Spherical Physics-informed Neural Operator with Multi-scale Coupling for Meteorological Downscaling
Meteorological downscaling is crucial for high-resolution regional climate forecasting and disaster early warning.When Do Repetition Loops Lock? Truncation Sampling, Bounded Penalties, and a Blind Spot in DRY
Language models sometimes repeat verbatim until the generation limit.Tensor Network Datamodels for Training Data Attribution
Datamodels predict how a model's behavior changes when different parts of its training data are included or removed.fxBench: Evaluating and Understanding Formula Suggestions in Spreadsheets
Spreadsheets are among the most widely used tools for computations, and formulas are their core building blocks.How Does Muon Work in Private Deep Learning? Scalable Differentially Private Training via Orthogonalization
Training deep neural networks requires optimizers that balance computational efficiency with formal data privacy.Gaussian Process Bandits with Paired Auxiliary Observations
Bayesian optimization (BO) is widely used for expensive black-box optimization, yet observation noise makes each evaluation less informative and slows posterior learning.From Overparameterised Expressions to Parsimonious Models: Fisher-Geometric Pruning for Symbolic Regression
Genetic programming symbolic regression (GPSR) often returns expressions that are larger than necessary, with dormant branches and finely tuned algebraic configurations that fit sampling noise.Paired-Outcome Learning for Selective Spacecraft Attitude Capture under Torque Limits
Torque-limited spacecraft attitude capture requires adapting the maneuver speed to uncertain braking authority.Evidence Field: Adaptive Covering Programs for Medical Imaging
Medical image classifiers typically return predictions without a native contract for independently reconstructing their spatial decision computation.