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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 28
PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
Inverse kinematics (IK) converts end-effector pose commands into joint motion, and many vision-language-action (VLA) policies rely on it to act on a given arm and to transfer across embodiments.Aligning Language Models with General Preferences: An Empirical Study of Direct Nash Optimization
Direct Nash Optimization (DNO) aligns a language model to a general preference function by approximating the Nash equilibrium of a two-player preference game.Constraint-Preserving Flow Matching for Spectral Graph Generation
Spectral graph generation models graphs through the eigenvalues and eigenvectors of their Laplacians, providing a compact and continuous alternative to direct graph construction.HLA-Net: A Physics-Guided Skeleton-Based Framework for Automatic Gravitational Arc Detection in Astronomical Images
Gravitational arcs produced by strong gravitational lensing provide a unique approach for investigating dark matter distribution, galaxy cluster mass reconstruction and the structure of the Universe.Learning Implicit Solves for Dynamic Simulations
High-fidelity simulation of dynamical systems is central to scientific discovery and engineering design, yet remains computationally demanding.Matched but Not Better: An Equivalence-Tested Benchmark of Quantum Machine Learning on Six Clinical Datasets and the QML-Health-READY Reporting Framework
Quantum machine learning (QML) is promoted for clinical prediction, but comparisons with classical models are rarely paired or tested for equivalence.SurgTransfer: Multi-Step Latent Action Modeling for Action Transfer in Surgical World Model
Surgical world models have the potential to provide intra-operative guidance, accelerate risk-free training and facilitate transfer of expert surgical maneuvers.SO(3)-Equivariant Learning on CAD Boundary Representations
Learning on boundary representations (B-reps) requires robustness to the pose of a part, since CAD models are not stored in a canonical orientation.Classical training of conditional instantaneous quantum polynomial-time Born machines
Instantaneous quantum polynomial-time (IQP) Born machines are attractive quantum generative models, as certain IQP circuit families are conjectured to be hard to sample, yet they can be trained…Objective-Conditioned Active Preference Learning for Multi-Objective Alignment
Aligning large language models (LLMs) with several objectives, such as helpfulness, harmlessness, and honesty, typically relies on preference judgment for each objective, growing the annotation cost…Ctrl-F: Force as an Interface for Humanoid Loco-Manipulation
Humanoid motion tracking has scaled to general policies that reproduce diverse human motion, but the resulting behavior is in free-space: the tracker knows where the body should be, not how it…TSFM Meets LLM: Context as Covariates
Textual information is ubiquitous in real-world time series datasets, appearing as news, reports, and annotations that hold key predictive signals not deducible from numerical history alone.Tight Lower Bounds under Matched High-Order Hölder Smoothness and Uniform Convexity
In this paper, we provide tight lower bounds for minimizing high-order Hölder-smooth and uniformly convex functions when their orders match.The Confidence Score: a New Measure on Kernels
Across a wide range of applications, kernel measures are used to tune hyperparameters, select among candidate kernels, and assess the suitability of kernels for specific learning tasks.Recasting Visual Language Models as EEG Foundation Models for Clinical Neurodiagnostics
Historically automated electroencephalography (EEG) systems was done using task-specific supervised models trained on purposely annotated data.Generalized Hyperrectangular Prediction Sets in Multivariate Conformal Prediction
In multivariate conformal prediction, many nonconformity scores yield prediction sets with complex geometry (e.g., ellipsoids or density level sets) that are difficult to interpret and visualize.A Unified Framework for Training-Dataset Aggregation and Exact Machine Unlearning via Gibbs Probability Measures
In this paper, three operations on Gibbs probability measures are studied: renormalization, normalized log-linear combination, and nesting.FedSharp: Byzantine Robust Decentralized Federated Learning Under Noisy Channel
Decentralized federated learning (DFL) eliminates the central aggregation server but remains vulnerable to data heterogeneity, Byzantine attacks, and channel noise.Support Vector Machines Directly Minimise KL Divergence
Support vector machines (SVMs) are fundamentally grounded in geometric margin maximisation and currently lack a mainstream probabilistic interpretation.Near-Minimax Sample Complexity for Multi-Objective Reinforcement Learning under Generative Models
In multi-objective reinforcement learning, preferences may be specified only after data collection, requiring one dataset to support a continuum of reward trade-offs.