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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 108
EVA-Flow: Learning Shared Chemistry for Unified Environment-Aware Molecular Conformation Generation
Molecular 3D structures underpin simulations of thermodynamics, kinetics, crystallization, and nucleation, making their accurate prediction central to drug discovery and materials design.Privately Clipping Heavy-Tailed Data
Summing vectors is a basic task in differentially private data analysis.BUSTA: Teaching Small Language Models to Reason About Binary Execution
Large Language Models (LLM) are increasingly applied to Reverse Engineering (RE) workflows.FASST: A Compact Stateless Atmosphere Model for Long-Term Ocean Coupling
Coupled climate simulations are computationally expensive, largely because of the atmosphere.Bridging Exploration and Loss of Plasticity in On-Policy Reinforcement Learning
Deep reinforcement learning (RL) algorithms have been observed to lose the ability to adaptively learn from new data, a phenomenon termed Loss of Plasticity (LoP).Confidence Is Not a Promise: A Reference Audit of Typed Decision Models
Typed decision models return a probability distribution over options supplied by the caller, and software tends to act on that number as if it were a guarantee.AHEAD: Adaptive Neural Acceleration for Large-Scale Linear Programming
Large-scale linear programs with millions of variables and constraints strain time and memory budgets.Balanced Active Subspaces for Optimal Control via Trajectory Optimization
The curse of dimensionality associated with solving the Hamilton-Jacobi-Bellman equation for the value function remains a key obstacle for real-time optimal feedback control of high-dimensional…Enhancing Clinical Classifier Robustness with Deep Representation Learning Based OOD Detection on DNA Methylation Data
DNA methylation (DNAM) array-based tumor classification is a powerful clinical diagnostic tool, but models trained on fixed reference cohorts can produce faulty predictions for tumor types not…HELICS: Biobank-scale Conditional Synthetic Genome Generation via Latent Flow Matching
Individual-level genotype data underpin genetic discovery, disease-risk modeling, and therapeutic development, yet many critical settings remain data-limited, including underrepresented ancestries…Reliability-Aware Short-Term Glycemic Risk Prediction Using an Anchor-Based Comparative Framework of Sequence, Multimodal, and State-Updating Digital-Twin Models
Anticipating short-term hypoglycemic and hyperglycemic risk is essential for proactive diabetes management.IOSage: Detecting HPC I/O Bottleneck Patterns to Guide LLM Recommendations
HPC applications often leave much of their parallel file system's bandwidth unused, and turning their Darshan logs into concrete I/O fixes still requires a scarce I/O expert.Evaluating Federated Vision-Language and Vision-Only Models for Privacy-Aware Breast Cancer Imaging
Artificial intelligence (AI) shows promise for breast imaging, but limited labeled data, institutional data fragmentation, and privacy concerns hinder centralized training.From Generation to Scoring: Scaling LLM-Based Semantic Labeling at LinkedIn
For efficiency, industrial large language model (LLM) labelers often pack many candidate labels into one prompt and generate a YES/NO answer for each, even when each decision depends only on the…SpecLST: Spectral-Enhanced Long-Short Term Spatio-Temporal Multimodal Modeling for Traffic Forecasting
Modeling multi-scale dynamics in urban road networks is essential for intelligent transportation systems but is often hindered by the computational cost of long-range historical context and the…When Can a Learned Design Surrogate Be Trusted in a Self-Driving Microfluidics Laboratory? An Offline Closed-Loop Framework Built and Tested on Six Public Tables
Self-driving laboratories couple a learned design surrogate to an instrument, and on every new target domain it must answer four questions: when it can be trusted, how many target measurements it…ORBITAL: Optimization of Robust Brain--Computer Inference Through Architecture-Aware Low-Precision Compression
Compact electroencephalography (EEG) decoders enable embedded brain--computer interfaces (BCIs), but compression can alter adversarial robustness without proportional hardware savings.Persistence, Exposure, and Credit Routing in Linear Temporal Predictive Coding
Temporal Predictive Coding (tPC) networks offer a layer-local alternative to backpropagation.Towards Myocardial Infarction Diagnosis: An Investigation into Machine Learning and Deep Learning Algorithms using ECG Data
Myocardial Infarction (MI) is a severe and potentially fatal cardiac condition caused by myocardial injury due to ischemia, and timely detection can substantially reduce its mortality.Continual Learning Exam: Benchmarking Long-Horizon RSI and Experiential Learning in LLM Agents
Continual learning is the ability of LLM agents to improve over time by acquiring, refining, and applying knowledge from experience while retaining useful prior knowledge.