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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 64
Shock Loading Accesses Reaction Pathways beyond Accelerated Thermolysis
Thermolysis and shock loading can produce similar major stable products in energetic materials, despite different chemical evolution and reaction pathways.Chronos-2: A foundation model for multivariate time series forecasting
Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications ranging from epidemiology and economics to energy systems and earth sciences.WASABI: Wasserstein Sampling via Bridge Interpolants
Sampling from Boltzmann distributions is a fundamental problem in physics, chemistry, and biology.Robust Learning of Multi-Index Models under Logconcave Distributions via Weak Partial Learners
We develop a new algorithmic framework for learning Boolean concept classes under logconcave distributions.When Is a Game a Markov Decision Process? Correspondences Between Game Theory and Reinforcement Learning
Reinforcement learning and game theory describe the same object from two sides.When Does Test-Time Search Help World Action Models? An Empirical Audit of Candidate Selection and Simulator Integrity
World-action models increasingly apply test-time scaling, spending extra inference computation on candidate selection and adaptive allocation to improve control.New Lower Bounds for Private PAC Learning
We study realizable PAC learning under approximate differential privacy, and give a framework for proving lower bounds on its sample complexity.Less Loss, Fixed Weights: Logit Corrections for Forgetting
When a language model is fine-tuned to follow instructions, or further pretrained on a new subject, it often gets worse at predicting standard web text.When Does Jacobian Fine-Tuning Stabilize Neural PDE Emulators?
Neural emulators of chaotic PDEs are trained on one-step prediction but deployed autoregressively, where their errors compound.Know When the Equation Is Wrong: Structural Uncertainty for LLM-Guided Symbolic Regression under Extrapolation
Large language models (LLMs) can help discover mathematical equations from data, but most LLM-guided symbolic regression methods focus on a single best equation without quantifying uncertainty over…GhostPrefix: Synchronization-Free Universal Prefix Attacks for ASR Manipulation
We present GhostPrefix, the first synchronization-free universal adversarial prefix attack against automatic speech recognition (ASR) systems.A Neural Network Approach towards Real-Time Knot Point Density Estimates for the Nonlinear Quadrotor
Spline representations provide a compact way to approximate continuous trajectories using a finite number of parameters.CausalXRL: Explainable Reinforcement Learning through Causal Graph Reasoning
Reinforcement learning is a powerful paradigm for training autonomous agents and has achieved impressive performance in complex environments.Empirical Evaluation of Quantum Kernel Methods for Classification: A Comparative Study with Classical Kernels
Quantum kernel methods are the most concrete near-term proposal for quantum machine learning on classical data, yet published benchmarks disagree on whether they outperform tuned classical kernels…There Is No Free Loop: When Does Looping a Trained Model Pay for Its Inference Compute?
Looping re-applies a block of layers so that each token passes through the block more than once.Learning Clean Generative Processes from Noisy Measurements
We study how to learn a generative model of clean images using only noisy observations.A richness-sensitive Acoustic Biodiversity Index from species-level detections: classifier benchmarking, false-positive control and validation against standard soundscape indices in an African savanna and an urban zoo
Acoustic indices are widely used as rapid proxies for biodiversity, but they summarise the distribution of sound energy rather than the species producing it and often track richness poorly.Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization
This work considers the design of stepsize schedules for stochastic gradient descent in smooth convex optimization.RAMA: Retrieval-Augmented Multi-Agent Routing with Episodic Memory for LEO Satellite Networks
Low-Earth orbit (LEO) satellite networks present a challenging routing environment due to rapidly changing topology, traffic, and link conditions.Differentially Private Synthetic Data from Sliced Chebyshev Moments
Research on practical differentially private (DP) synthetic data has largely focused on methods that approximately preserve low-dimensional marginals of a target distribution, and can thus only hope…