AI & Machine Learning · Computer Science & AI
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.
Get the few papers worth your time in Machine Learning every morning.
Follow Machine Learning and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 236
Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards
Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root learning-rate scaling.Exact Topological Compliance: Generating Persistence-Equivalent Graphs
Persistent homology summarizes the topological evolution of a graph as persistence diagrams (PDs).Hybrid Reasoning Agents
How can we build an agent that thinks beyond human language, yet still acts through it in the external environment?DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning
Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem.Data Free Training of Diffusion Models for Lattice Field Theories
Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative samplers that can produce independent…Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards
Long-horizon agents have drawn growing attention for their ability to work autonomously -- from hours to days -- on economically valuable tasks, a capability essential to automated discovery and…What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study
How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning?Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion
Sparse-to-dense motion completion recovers dense mesh motion from sparse vertex displacements.The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery
Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, analyze their results, and autonomously operate…An Initial Study of Physiological Markers for Deepfake Speech Detection
Conventional deepfake speech detectors primarily target synthesis artifacts and often degrade under unseen attacks or recording conditions.Delay Complexity Dimension: Learning with Delayed and Censored Labels
We study PAC learning of binary classifiers when labels arrive after random delays and are censored beyond an observation window $W$.Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench
Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific conjecture should be retained, revised, or rejected.Wong--Zakai Convergence and FNO Learning for Stochastic Volterra Equations
This paper studies Wong--Zakai convergence and Fourier neural operator learning for stochastic Volterra integral equations (SVIEs) describing random systems with memory.Semi-Amortized Bayesian Inference with Reusable Local Posteriors
Amortized Bayesian inference uses simulations to perform Bayesian estimation when likelihoods are expensive or analytically unavailable.Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model
Physical-layer spoofing attacks pose a critical threat to wireless localization systems by manipulating channel state information (CSI).Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries
Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward.Virtual-Alloy Physics-Aware Semantic System for extrapolative prediction of perovskite solar cells
Existing machine-learning models for perovskite solar cells struggle to predict devices made from new materials.Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models
Low-Rank Adaptation (LoRA) has made diffusion model personalization affordable and widely shareable, but it also creates moderation risks when harmful or problematic content is not faithfully…PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization
The Practical Molecular Optimization (PMO) benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles that do not capture structure-based drug…Data Density Scaling Laws for Image Self-Distillation
Current scaling law formulations suggest that increasing the number of unique samples in a dataset improves model performance, while repeated exposure to the same samples provides diminishing value.