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Machine Learning
The newest papers in Machine Learning that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 139
The Price of Choice in Selective Prediction
A selective classifier may be claimed to satisfy a risk-coverage contract if some rule from a predeclared menu (a threshold or a union of $K$ score intervals) attains coverage at least $κ$ and error…Value-Rectified Distillation for Flow-based Offline Reinforcement Learning
We aim to address the problem of Out-of-Distribution (OOD) Mode Averaging, a critical problem that emerges when distilling Flow Matching (FM) based behavior policies in offline Reinforcement…Internal Guidance as a Sampling Method for Tilted Diffusion Models
Reward guidance methods sample an exponential tilt of a base density using a diffusion model trained with only samples from the base distribution.Causal world models under physical parameter shifts
World models predict how embodied systems evolve under actions, but their accuracy can degrade when physical conditions move outside the training distribution.Dual-Channel Contrastive Encoding of Sperm Whale Coda
Sperm whales (Physeter macrocephalus) communicate through short click sequences called codas, whose structure includes the rhythmic pattern of inter-click intervals (ICI) and the spectral…DriftNeRF: Cross-Ray Density Drifting for Sparse-View Neural Radiance Fields
Neural Radiance Fields (NeRF) synthesize photorealistic novel views from dense image collections, but degrade sharply when few views are available.SU(2)-Equivariant Spinor Message Passing for Molecular Magnetism
Predicting molecular magnetic properties often requires costly electronic-structure calculations that account for spin-orbit coupling.MHeatmap: Two-Walk Laplacian Reordering and Mosaic Layout for Rectangular Matrix Visualization
Weighted rectangular matrices record relations between two types of objects, such as occupations and industries, documents and terms, origins and destinations, or annotated classes and computed…Recursive Self-Improvement on a Budget: Scoring Code Edits Before They Run
A coding agent that rewrites its own scaffold has to choose which proposed edit to expand before that edit has been evaluated, and the search then has to return one agent.Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering
Reward hacking is a major problem in current production machine learning systems, resulting in costly and dangerous failures.SAFTER: Safety-Aware Filtering Through Explicit Recoverability for Robust Reinforcement Learning under Distribution Shift
Learning safe policies that generalize to unseen environments is difficult under bounded actuation, where an unsafe decision may become unrecoverable.From Augmentation to Synthetic Evidence: Evaluating Generated Chest Radiographs at Dataset Intake
A generated chest radiograph can be useful as declared augmentation, but the same image becomes unsupported evidence when submitted as the record of a real examination.TeachBack: Learning to Teach via Bidirectional Preference Optimization for On-Policy Self-Distillation
On-policy self-distillation (OPSD) enables a language model to learn from a Teacher instantiated from the same model but given access to privileged information, such as a reference solution.Conditional Flow Models Convert Epistemic Uncertainty into Apparent Aleatoric Variance
Conditional Flow Models (CFMs) are increasingly being used in place of deterministic predictors across machine learning, including for tasks that were classically formulated as regression.ROCOCO: Robust Outline Completion of Occluded Curvilinear Objects
We propose a learning-based generative model for completing curvilinear features under occlusion by environmental obstacles.A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection of machine…Curvature-Scaled Sharpness-Aware Minimization: Convergence Beyond Smoothness
Sharpness-Aware Minimization (SAM) has established itself as a widely used method for training deep neural networks.Truncated Out-of-Bag Probability Gain: A Noise-Robust Criterion for Random-Forest Wrapper Feature Selection
Random-Forest wrapper feature selection can use out-of-bag (OOB) predictions as a built-in validation signal, but the criteria that decide each forward step accumulate OOB estimation noise.A Unified Evaluation Framework of Physical Adversarial Robustness in the Edge VLM Era
Recent advancements in Vision-Language Models (VLMs) enable end-to-end visual grounding via language prompts and generative decoding, demonstrating largely improved zero-shot and open-vocabulary…Reconstruction differences rival individual differences in the fly compass connectome and break trained circuit models
Neural network models are increasingly constructed from synapse-resolution connectomes, and the same circuit is now available in reconstructions from other animals and pipelines.