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 128
Detecting Temporal Concepts in Video Transformers via a No-False-Positives Test
Video models have the architecture to support encoding temporal aspects of the input, i.e. concepts that require more than one frame to define, like object movement.The Clustering Occupation Kernel (CLOCK) Method for Time Series Arising from Dynamical Systems
We introduce the Clustering Occupation Kernel (CLOCK) method, a novel representation-based framework for clustering time series arising from dynamical systems.MOSHO: A Native Discrete Shark-Inspired Metaheuristic for Hardware-Aware Neural Architecture Search
Hardware-aware neural architecture search (NAS) requires jointly optimizing predictive accuracy and device-specific latency over discrete search spaces.Automated screening of Parkinson's Disease using Smooth Pursuit Eye Movements
Smooth pursuit eye movements are frequently altered in Parkinson's disease, but previous studies have relied mainly on conventional parameters that describe the relationship between gaze and target.LSTM-Style Looped Transformers with Shared KV Caches
Recurrent transformers reuse weights to perform deep computation with fewer parameters.Multiple Importance Sampling for Guided Speculative Inference
We study an extension of the Guided Speculative Inference (GSI) algorithm for test-time-scaling of large language models (LLMs) to a novel setting where an ensemble of smaller LLMs proposes draft…Lifetime-valid decisions by continually evolving AI analysts
AI agents increasingly act as analysts whose decision can often be cast as hypothesis tests.When News Gets Old: Portfolio Allocation with Reinforcement Learning and Large Language Models
Financial news can influence investment decisions long after publication.Bridging the Closed-World Gap: Interpretable Anomaly Detection via a Hybrid TSK-FCM Ensemble on the BETH Dataset
A fundamental paradox plagues the deployment of Machine Learning in cybersecurity: supervised models generalize poorly to novel, Out-of-Distribution (OoD) attacks, whereas unsupervised models often…Evaluating Type-1 Diabetes Digital Twins by Decision Quality
A digital twin (DT) for type 1 diabetes (T1D) can predict how an individual's blood glucose will respond to different insulin treatments without actually trying them.Future of Time Series Anomaly Detection: From Historical Review to Unified Outlook
Time series anomaly detection (TSAD) is a foundational problem in data mining, aiming to identify observations that deviate significantly from nominal temporal patterns.Interpreting Model Reasoning through Behavioral Counterfactuals
Faithfully understanding the internal process that occurs as models form their chain of thought is an important problem with the rise in model capabilities.Residual Calibration via Local Feature-Space Refinement
Deep neural networks often struggle to provide reliable confidence estimates, making post-hoc calibration essential for reliable decision-making.Fast Iterative Combinatorial Auctions via Deep Reinforcement Learning
Iterative combinatorial auctions are used in spectrum allocation, transportation, and industrial procurement, but rounds are costly.RiboBridge: protein-conditioned de novo RNA binder structure design
Designing RNA molecules that bind to target protein structures requires generating both an RNA conformation and its bound complex geometry, without assuming a predefined binding site.Scaling Laws for Safety Probes
Safety probes trained on a model's activations are a standard tool for monitoring language models in real time.Learning Domain Trajectories with Flow Matching for Gradual Domain Adaptation
Large distribution shifts remain a primary obstacle for domain generalization and unsupervised adaptation.An Agent Architecture with Tools and Memory for Selective Correction of Wrist PPG Heart Rate Estimates
Wrist photoplethysmography (PPG) heart rate estimates are vulnerable to motion artifacts, and corrections that reduce mean error can worsen individual windows.AIRO: A Streaming-Based Machine Learning Framework for Threat Detection and Security in Industrial Internet of Things
Modern Industrial Internet of Things (IIoT) im- plementations now connect edge gateways, cloud dashboards and business applications.Solvable models of learning to pursue a moving target
Agents tasked with intercepting a moving target must learn how to reach it, ideally as efficiently as possible.