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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 224
Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning
Robust adaptive control is vital for keeping robots safe while actively reducing uncertainty in unfamiliar settings.Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes
Deep learning models, particularly Long Short-Term Memory networks, are increasingly used for operational streamflow forecasting, with ensemble spread providing a signal of predictive uncertainty.LEV: Thinking Fast and Deep with Looped Decision Models
Decision models answer typed questions about an input with one probability per option, computed in a single forward pass without generating text.Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora
A decoder of imagined speech that works for a new user without calibration would make a non-invasive speech interface practical.MIDI-REPA: Improving Text-to-Music Generation with Symbolic Representation Alignment
State-of-the-art text-to-music models increasingly rely on scaling model capacity, data, and compute, while still learning musical structure directly from dense audio.Two-Stage Score Estimation for Group-Specific Diffusion Models
Generative modeling across related distributions raises a question: how can shared structure support accurate generation for a target distribution with limited observations?How to Train Your Little Dragon: Teaching a Small Language Model to Construct and Refine GPU Optimization Scripts
Compiler transformations interact, making GPU kernel optimization a search for combinations that preserve the computation while improving performance.The Blume-Capel Model as a $φ^6$ Field Theory: A Monte Carlo and Flow Model Study
We report Monte Carlo simulations of a continuous spin variant of the Blume-Capel model.Do As I Do, Not As I Say: On-Policy Distillation Inherits the Teacher in Vision-Language Models
Improving the reasoning capabilities of vision-language models (VLMs) often relies on post-training with supervision from stronger models.One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation
Reordering multiple-choice options can change a model's answer, an effect studied as selection bias (a preference for certain option letters) and as order sensitivity (changes in answers across…Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence
We study fixed-budget adaptive experimentation aimed at maximizing the probability of obtaining sufficient posterior evidence that a recommended design's mean performance exceeds a target.EigDiff: Eigensample Diffusion - or how to significantly reduce training dataset size with marginal performance variation?
Diffusion models are notoriously expensive to train, requiring large-scale datasets to learn how to gradually reverse the noise from images over multiple small denoising steps.Imperfect Is Perfect: Learned Column Prefilling for Branch-Price-and-Cut
Column generation folklore warns that initial columns hurt: a warm start may bias the dual and slow the root of branch-price-and-cut.Can LLMs Anticipate Their Cruxes?
To use language models to help us make decisions we would like to understand the rationale behind the language model's outputs.Audio-Visual Dog Identification and Tracking in Real-World Videos
Identifying individual dogs from barks in real-world videos remains challenging and largely unexplored.CausalEvo: Causal Skill Self-Evolution for Health LLM Agents
Large language model (LLM) agents for health can accumulate reusable textual skills from task feedback, such as calculation criteria, measurement time points, and formula units.Acceleration and its Limits in Gaussian Variational Inference
We study whether the classical momentum-based acceleration that improves the condition-number dependence of gradient descent from $κ$ to $\sqrtκ$ for strongly convex, smooth objectives extends to…Learning to Read Out: Unembedding Dynamics in Language Model Pretraining
When does a language model acquire a capability?On the Last-Iterate Convergence of Clipped Gradient Methods
In stochastic (strongly) convex optimization, establishing last-iterate convergence guarantees for first-order methods has recently garnered increasing attention, as this not only deepens the…Deep Learning for Forest Plantation Mapping in Godavari Districts of Andhra Pradesh, India
Small-area forest plantations play a vital role in the socioeconomic well-being of farmers in Southeast Asia.