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.
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Newest papers, page 7
Detecting Silent Faults at the Environment-Trainer Boundary in Reinforcement Learning
Robot reinforcement learning increasingly runs environments outside the trainer: in another process, on another machine, in another language.Detecting Weather Fronts from Satellite Observations: What Does Reanalysis Add?
Weather fronts are transition regions between air masses with contrasting thermodynamic properties and are associated with mid-latitude precipitation and severe weather.Interpretable Multimodal MRI-Clinical Fusion for Functional Outcome Prediction After Acute Ischemic Stroke
This study evaluates the added prognostic value of day-1 multimodal MRI for predicting three-month functional outcome after acute ischemic stroke (AIS).AI-Native Task-Oriented Semantic Communications over a Relay: Benefits and Vulnerabilities
Semantic communication (SemCom) using a relay node is considered, where the relay assists in conveying task-relevant information from a source to a destination, with meaning defined by…La-JEPA: Compact Robot Policies with Latent World Modeling
World-action models adapt pretrained video generators, and vision-language-action models build on pretrained vision-language backbones; both tie policy architecture and inference cost to large…Improving Context-Shift Robustness of Convolutional Networks via Context-Regularized Cross-Entropy
Image classification models often encounter objects in diverse and unpredictable contexts at test time, which may differ substantially from those seen during training.Patch Attention Wisely: Understanding Patch Length in Attention-Based Forecasting
Attention-based time series forecasters commonly group consecutive observations into patches.Regret Bounds for Profit Maximization in Bilateral Trade: Learning the Best Posted-Price Mechanism
Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they hold private valuations.BUMP: Brief Unexpected Mid-Interpolation Peak
We identify an intriguing phenomenon when linearly interpolating between permutation-aligned neural networks: test accuracy briefly improves near the interpolation endpoints before dropping toward…Do sEMG Foundation Models Improve Cross-Subject Generalisation?
sEMG Foundation Models aim to learn general purpose representations over diverse subjects and recording conditions that boost performance on a variety of downstream tasks.Beyond Quadratic Attention: Separable Gaussian Operators for ODE Vision Transformers
Self-attention carries a quadratic cost in token count, which limits how far Vision Transformers can scale with input resolution.FewStepRL: Fine-tuning Few-step Diffusion Models with Reinforcement Learning
Reinforcement learning (RL) has become a standard post-training method for diffusion models, but most RL objectives roll out the current policy at every iteration, and with a many-step sampler these…Geometric Relevance Is Not Sufficient: Evaluating Focused Post-Training Supervision in Diffusion Docking
Diffusion-based molecular docking requires learning a corrective score field across a broad corruption distribution, yet individual pose errors can differ substantially in their structural…Series-wise Cross-validation for Global Time Series Forecasting
We consider the problem of cross-validation (CV) in time series forecasting, with a focus on global forecasting models based on deep learning.Nowcasting of Tropical Cyclone Wind Fields and Intensity from Geostationary Imagery
High-resolution tropical cyclone surface wind speeds derived from synthetic aperture radar (SAR) observations can support storm structure and intensity modelling, but remain spatially and temporally…Scaling Neural Motor Decoding via Decoupled Behavioral Pretraining
Recent neural decoding approaches achieve strong performance by training on large collections of paired neural-behavioral recordings, but such datasets are expensive, invasive, and difficult to scale.DART: Domain-Agnostic Residual Transfer for Generalist Anomaly Detection
Generalist Anomaly Detection (GAD) aims to detect anomalies in unseen domains using only a few reference samples.Accelerating Safe Reinforcement Learning with Massive Parallelism
We investigate the problem of scaling safe reinforcement learning (safe RL) to massively parallel training regimes, where a large number of environments are exe- cuted with short rollout horizons.AutoCompress: Automated Hardware-Guided Model Compression
LLM pruning must preserve quality while meeting hardware- and workload-specific constraints on latency, throughput, and memory.Transformer-Based Trajectory Prescription for sEMG-Optimizing Robot-Mediated Rehabilitation
Robot-mediated rehabilitation has the potential to deliver high-intensity, individualized therapy, but existing systems (which have not consistently improved outcomes) typically prescribe kinematic…