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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 55
Fewer Tokens, More Answers: Verifiable-Reward Tuning of an Ornith-Style VLM for Multi-View Driving QA
Reasoning-enabled model derivatives must turn intermediate deliberation into a usable answer within a deployment budget.Adaptive-Policy Distillation: Knowledge Injection under Information Asymmetry
On-policy distillation (OPD), which uses reverse-KL supervision on student rollouts to transfer a context-conditioned model's behavior into student model parameters for closed-book inference, has…HamFormer: A Hamiltonian Flow-based SE(3)-Transformer for Conformation Optimization
The Transformer architecture plays a central role in modern deep learning, and its SE(3)-equivariant variants have been widely applied to molecular conformation modeling and optimization.What Breaks and What Comes Back: Capability Damage and Recovery in Depth-Pruned Small Language Models
Depth pruning removes whole Transformer blocks to make a language model smaller and faster, but the damage is not spread evenly across what the model can do, and a short fine-tuning run, called…Foresight: Learning Twisted SMC for Rare Events from Reversed Relaxations
We consider the probability that a reversible Markov chain started in a set $A$ is in a rare set $B$ after a fixed horizon, a quantity often needed for many start laws, down to single states.Benchmarking Machine-Learning Classifiers for Neoantigen Immunogenicity Prediction and the Limits of SHAP Interpretability
Personalized cancer vaccines require prioritizing, among hundreds of candidate mutant peptides, the few that will induce a T-cell response.Benchmarking Classical Machine Learning Models for Shading-Fault Detection in Rooftop Photovoltaic Systems Using Real Sensor Data
Most published comparisons of machine-learning classifiers for photovoltaic (PV) fault detection are validated on simulated faults.TreePIN$^{2}$: Tree-Prior Initialized Neural Networks
Fitted decision trees provide predictive knowledge for neural network initialization.What matters in classical machine learning for non-intrusive load monitoring? A factorial benchmark on REFIT with validation on unseen houses
Published classical machine-learning results for appliance ON/OFF classification in nonintrusive load monitoring (NILM) range from F1-type scores of 0.53 to 0.98, and the sources of this spread have…Solvable dynamics model of in-context learning
Pretrained Transformer architectures display a surprising in-context learning ability without additional training.MarkovTS: Pretrained State-Transition Representations for Zero-Shot Time Series Generation
Across heterogeneous real-world domains, time-series generation supports simulation, model development, and data augmentation by generating sequences that preserve target-domain statistics and…HieraNTP: Structured Risk Representations for Time-Series Anomaly Detection via Hierarchical Next-Token Prediction
Time-series anomaly detection commonly converts deviations from learned temporal regularities into anomaly scores.Bootstrap Inference for Riemannian Optimization Problems
We develop an online multiplier bootstrap for manifold-valued parameters estimated by averaging the iterates of Riemannian stochastic approximation.Riemannian Newton Methods with Statistical Inference
We develop online statistical inference for Polyak-Ruppert averages of inexact Newton iterates on Riemannian manifolds.Optimal Learning in Fairness-Aware Contextual Bandits
Mitigating unfair disparities in sequential decision-making is a growing concern in machine learning.HALO: Hierarchical Evidence Selection and Reinforcement Learning for Allocating Budgeted Label Optimization from Longitudinal Clinical Records
Learning from extensive longitudinal clinical records--spanning repeated encounters, evolving conditions, and heterogeneous documentation--under a finite labeling budget requires solving two coupled…ISBC: Interpretable Sparse Binary Coding of Pretrained Text Embeddings
Pretrained text embeddings encode semantic structure in high-dimensional continuous spaces whose coordinates are difficult to interpret.Robust Pixel-wise Digital Image Correlation under Large Motions and Illumination Variations via a Global-to-Local Hybrid Neural Field
Digital image correlation (DIC) is a widely used non-contact technique for full-field displacement measurement.Jet-QSparse: Accelerating Agentic LLM Inference with 4-Bit Sparse Attention
Long-horizon reasoning and agentic workloads strain KV-cache capacity and decode attention throughput.Hierarchical Asynchronous Federated Learning over Space-Air-Ground Integrated Networks
With the emerging 6G paradigm where AI services operate over decentralized and resource-constrained edge infrastructure, performance of distributed learning algorithms remains a significant challenge.