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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 191
Out of Support, Not Out of Reach: In-Context Learning with Unsupported Instructions and Composed Tasks
In-Context Learning (ICL) enables pretrained LLMs to adapt to downstream tasks from a small set of input-output demonstrations, without parameter updates.Causal Discovery beyond Conditional Independence: Identifiability, Algorithms, and Statistical Inference for Directed Acyclic and Cyclic Graphs
Learning causal structure from data requires assumptions about mechanisms, noises, observation schemes, and interventions.Beyond Latent Personalization: Integrating HRV-Conditioned Personalization via LLM for Interpretable Stress Detection
Stress detection based on heart rate variability (HRV) has received increasing attention.Beyond Expected Values: Risk-Sensitive Planning with Distributional Monte-Carlo Tree Search
Monte-Carlo Tree Search (MCTS) typically stores only scalar mean estimates, which makes it poorly suited for tail-risk objectives such as conditional value-at-risk (CVaR).A general recipe for data-adaptive anytime-valid envelopes for iterative algorithms
Most convergence guarantees for iterative stochastic algorithms are stated at a fixed iteration count and do not justify stopping at a data-dependent time.BMuon: Budgeting Muon's Spectral Geometry for Structured Compressibility
Muon has emerged as a strong optimizer for neural network training and admits an interpretation as steepest descent under a spectral-norm geometry.Boundary Detection for High-Dimensional Point Clouds
We introduce two new methods for boundary detection in high-dimensional point cloud data, intended to work in situations with non-uniform density, high ambient dimension, and substantial noise.When Magnitude Suffices: Reconstructing Time-Domain-Gated Microwave Signals Without Phase on a Thoracic Phantom
Time-domain gating separates the arrivals that form a microwave S-parameter measurement.How Many, but How Sure? Class Competition as a Confidence Proxy in Multi-Class Density-Based Counting
Multi-class density estimators can count objects in crowded scenes, but their class-wise outputs offer no indication of how reliably density mass has been allocated.Trajectory-Triggered Sparse Collaborative Decoding
Dense assistance methods such as speculative decoding repeatedly verify draft tokens with a target model, keeping target computation coupled to generation.The Sign Code: The Hidden Binary Nature of Deep Networks
Every neuron in a trained network outputs a number that can be separated into its sign (positive or negative) and its magnitude (absolute size).ThetreStageAI: Multi-View Retrieval-Augmented Generation for Structured Bengali Theatre Production Planning
Staging a theatre performance requires coordinating dialogue, actor movement, stage directions, lighting focus, cue timing, and equipment constraints within the same production plan.GRAFT-EEG: Gauge-Aware Physical Field Transport for Cross-Dataset Motor-Imagery EEG
Cross-dataset EEG decoding often treats montage, reference, and active-sensor changes as representation shift, although they also alter the observation operator.Deconstructing Multi-Task Active Learning: The Paradox of Gradient Conflict and Orthogonal Decomposition
Existing multi-task active learning (MTL-AL) methods predominantly rely on linear scalarization of heterogeneous objectives such as uncertainty, diversity, and gradient conflict.Deep LLM layers expose passage-level context in EEG during silent reading
In a previous report we showed that a decoder can recover open-vocabulary word-level information from 19-channel EEG recorded during silent reading, by aligning short EEG windows with hidden-state…No-Regret Language Generation
We introduce no-regret generation, which requires a generator to make a sublinear number of mistakes uniformly over all demonstration sequences and compatible target languages.Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling
Accurate atomistic simulations of materials under electric fields require long-range electrostatics and electrical-response modeling at length scales beyond the reach of conventional…JAXtari: High-Throughput and Easy-to-Modify Arcade Learning Environment
The Arcade Learning Environment (ALE) has been a cornerstone of Deep Reinforcement Learning research, providing a standardized benchmark for evaluating general agents.A Language-Informed Loss for Visual Generation
End-to-end visual generation with pixel-space loss has recently achieved competitive performance without the need for learned image tokenizers.An Efficient Approach to Beetle Damage Detection of Banana Using Deep Learning
Visible surface damage of banana fruits by Basilepta subcostata (the banana fruit scarring beetle) can reduce their market value.