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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 171
EGiME: Entropy-Guided Model Evaluation in Few-Shot Learning
The feature-extraction quality of pre-trained models sets the ceiling of downstream transfer learning, yet few-shot model selection remains difficult: supervised evaluation on scarce labels is…Hilbert-Scale Stability of Posterior Diffusion Priors for Ill-Posed Signal Recovery
Indirect and noisy observations are difficult to invert when the forward operator smooths out the unknown signal.ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems
As AI systems begin to resolve open research problems, reliable verification becomes increasingly important.Discrete Neural Interlingua for Ontology-Agnostic EHR Modeling
EHR foundation models promise scalable clinical prediction across health systems, yet their deployment is constrained by a tokenization bottleneck.Graph Neural Networks for Astrophysics and Cosmology
Physics seldom makes breakthroughs when we consider objects only in isolation.LEAF: LUT-aware Efficient Attention on FPGAs
We introduce Lookup-table (LUT)-aware Efficient Attention on FPGAs (LEAF).ClockMark: Adaptive and Host-Dependent Watermarking for Large Language Models
Large language model (LLM) watermarking operates on the next-token distribution that evolves during generation.LOB-Impact Benchmark: Emergent Market Impact in Generative Limit Order Book Models
Realistic market impact is essential for generative limit order book (LOB) models to serve as world models for trading agents.This is SParTA: A modular, efficient and scalable transformer architecture for jet-tagging
We present the Sparse Particle-interaction Transformer Architecture (SParTA), a modular, scalable, and efficient transformer for jet tagging at high-energy colliders.RGH-GNN : Risk-Gated Heterogeneous Graph Neural Network for Financial Fraud Detection
Credit card fraud is a significant problem for financial institutions, resulting in substantial monetary loss each year.Reconstruction-Optimized Expert Pruning for Mixture-of-Experts Language Models
Mixture-of-Experts (MoE) models reduce computation through sparse expert activation, but deployment still requires storing a large number of expert parameters.Glass-Box Deep Reinforcement Learning for High-Speed Flow Control
Deep reinforcement learning (DRL) is challenging long-held assumptions in active flow control.Ammonia-Hydrogen Phase Equilibria in Planetary Envelopes from First-Principles-Based Machine-Learning Molecular Dynamics
Ammonia and hydrogen are major constituents of planets within and beyond the solar system, yet how they interact in planetary envelopes is poorly known.Ontic state recovery through prediction
We encounter a system through its responses, yet its state concerns how the system is.Representing short-term movement to reveal animal identity and learning progress
How learning progresses over time and varies across individuals is a longstanding question in cognitive and behavioral science.Towards Interaction State Recognition in Sensorized Gecko-Inspired Adhesive Interfaces for Orbital Servicing
Reliable contact establishment remains a critical challenge for autonomous on-orbit servicing (OOS), particularly when interacting with non-cooperative targets.ICLHerd: Herding-Based Data Selection for LLMs with In-Context Learning as a Proxy
Data selection plays a central role in modern large language model training, as the quality of training data strongly influences downstream performance.Cross-disorder Learning of Imaging-derived Joint Mophometric Markers in Alzheimer's and Parkinson's Diseases
As two most prevalent neurodegenerative diseases (NDDs), Alzheimer's disease (AD) and Parkinson's disease (PD) shared commonalities both in symptomology and pathophysiology.A Hybrid TFT-NBEATS Framework for Robust Multi-Horizon Photovoltaic Power Forecasting
Accurate multi-horizon photovoltaic (PV) power forecasting is essential for grid stability, energy scheduling, and the efficient operation of PVbatterygrid systems.Understanding the Benefits of Scaffolds through Computational Separations
Inference-time harnesses, i.e., external procedures that call a model many times and keep state across calls, can substantially improve model capabilities.