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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 105
Quantitative Generalization Bounds for Multi-Layer Transformers: A Few Long Strings can Beat Many Short Ones
Transformer-based language models exhibit an astonishing capacity to generalize to longer and more difficult problem instances than those seen in training.Audit Where Hacks Pay: Budgeted Strong-Verifier Audits Against Reward Hacking in GRPO
Reinforcement learning with verifiable rewards trains on cheap checks, such as unit tests, that a policy can learn to game, while a stronger check is too expensive to run on every rollout.Quantifying the failure of symmetry learning in linear diffusion models due to initialization and finite-sample effects
Data distributions often possess one or more symmetries: for example, all orientations of any specific dog are included in the distribution of all possible dog pictures.Can JEV Support Clinical Trial Criteria Assessment?
Clinical trial prescreening requires distinguishing unmet conditions from insufficient evidence in patient records.TRACE: Memory-Efficient Time Series Class-Incremental Learning via Temporal-Spectral Reconstruction
Web-connected sensing applications continuously generate multivariate time series with evolving behavioral categories, making Time Series Class-Incremental Learning (TSCIL) essential for adapting to…Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution
Multi-objective reinforcement learning is a fundamental problem for modern AI, with applications spanning post-training, reasoning models, and robotic control.Preference-Guided Cooperation in Multi-Objective Multi-Agent Reinforcement Learning
Multi-objective multi-agent reinforcement learning (MOMARL) addresses agent coordination and objective trade-offs simultaneously while optimising overall system performance.LUCID: Local Understanding of Covariate Importance with Conditional Density Estimation
Computing local-level variable importance (VI) is critical for understanding machine learning models.Model Selection for Off-Policy Evaluation via Pairwise Bellman Validation
Model selection and hyperparameter tuning pose a central challenge in off-policy evaluation (OPE) because errors of fitted Q-functions and occupancy-ratio estimates cannot be directly evaluated from…Beyond Physical Illness: Can Clinical Narratives Reveal Emerging Mental Health Risks in Chronically Ill Patients?
Mental-health risks such as depression, dementia, and suicidal ideation can emerge during hospitalization among chronically ill patients and may worsen without timely recognition and intervention.Improving Signals, Declining Encoder: A Known-Answer Test of Label-Free Monitoring in Self-Supervised Learning
Self-supervised training is monitored without labels, through the loss and statistics of the learned features.A theory of noisy linear recurrent neural network learning dynamics
Biological neural networks are noisy: their dynamics involve difficult-to-predict fluctuations which are likely due not just to chaos, but also to a variety of low-level biophysical processes.Selective Tie Learning: Mitigating Spurious Correlations in Preference Optimization
Preference optimization can learn spurious correlations, and augmenting the preference data with ties (equally preferred responses) can reduce spurious reliance.FOCUS: Active Critic Learning for Long-Horizon Language Model Agents
Sparse outcome rewards make it difficult for long-horizon language-model agents to assign credit to earlier actions.Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models
Diffusion large language models (dLLMs) can generate multiple tokens in parallel, but often require many decoding rounds to complete a response.Does VGGT Know What It Does Not Know?
Feed-forward 3D reconstruction models such as VGGT regress cameras and geometry in a single forward pass, but they report no uncertainty for the camera poses they predict: an incorrect relative pose…Pretraining Knows What, Simulation Teaches How: Compositional Generalization in a Sim-Trained Robot Agent
A robot policy needs to understand what a task is asking for in a scene, and how to move the robot to accomplish that task.Information-Theoretic Secure Aggregation With Heterogeneous Client Quantization
In federated learning (FL), secure aggregation enables a server to compute the sum of local model updates of $N$ clients, without revealing any information on the individual model updates to the…Work Agents: Systems, Capabilities, and Optimization
As foundation models evolve from information generation to digital work execution, agents are increasingly capable of completing complex tasks across tools, applications, and workflows.On the stability of learning
Learning algorithms are usually analyzed under the assumption that informative gradients are continuously available.