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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 125
Regularised RL improves sim-to-real transfer
Regularised reinforcement learning (RL) has provable robustness guarantees that should ease the transfer of policies from simulation to robots, known as sim-to-real.Sustainability of TSFM Deployment and Pretraining: From Carbon Footprint Analysis to Mitigation via Pruning
Time Series Foundation Models (TSFMs) offer strong zero-shot forecasting capabilities, but their high pretraining cost raises a practical sustainability question: does this upfront investment pay…Interpretable sparse fine-tuning with TopKLoRA enables faithful circuit discovery
Fine-tuning on datasets of unknown origin can create a backdoor in the trained model when the data includes poisoned examples.Trusting the Tool: Asymmetric Deference of LLM Vulnerability Auditors to Static-Analysis Evidence
Pipelines that combine large language models (LLMs) with static analysis let the model consume the analyser's output as evidence, but whether the model distinguishes true from false evidence, and…Reinforcement Learning with Multi-Step Transition Predictions: A Bellman Theory
Multi-step predictions of future transition realizations can improve reinforcement learning, but their value remains difficult to quantify.When Human Corrections Travel: Safety in Federated VLA Adaptation
Vision-language-action (VLA) policies can adapt to local human intervention, while federated learning can combine distributed robot experience without pooling raw records.LoCA: Enabling Multi-Modality in Geospatial Location Encoders
Location encoders map geographic coordinates to high-dimensional descriptors through contrastive alignment with paired Earth Observation imagery.Recommender regret for improving regularised RL estimation with bandits and MCTS
Regularised reinforcement learning (RL) algorithms rely on estimators, like Monte Carlo tree search or one-step look-ahead, to provide policy, value, or Q-value estimates in training and inference.A Tutorial on Physics Constrained Machine Learning for Hybrid Modeling, Optimization, and Control
Physics-constrained machine learning (PCML) integrates domain knowledge, first-principles, and physical constraints into AI/ML models.SemantEq: Faithful Bidirectional Equivalence of Formal Statements
Statement autoformalization translates mathematics from natural language into a proof assistant such as Lean 4, and models now do it at scale.Adaptive Frequency Threshold Byte Pair Encoding for Mixed Natural Language and Structured Symbolic Corpora
Modern language models rely on subwording tokenization to define the discrete units of input processing, typically using methods like Byte Pair Encoding (BPE) that treat whitespace as a fixed…ZENO: Nonlinear filtering with Zakai Equation Neural Operators
State inference and parameter learning for nonlinear filters is a core computation for scientific machine learning with varied applications in engineering and neuroscience.Learning Positive Linear Combinations of ReLUs in Fully Polynomial Time
We study the fundamental problem of PAC learning positive linear combinations of $k$ arbitrary ReLU activations on $\mathbb{R}^d$ with respect to the Gaussian distribution, in $L_2$ loss.If at First You Don't Succeed, Backtrack: Lightweight Recovery for Frozen VLAs
Vision-Language-Action (VLA) models perform well on robotic manipulation, yet remain brittle once their own actions carry them into states their training data barely covers.Knowledge Distillation across Item and Code Spaces for Parallel Generative Recommendation
Generative recommendation with semantic IDs represents each item as a sequence of discrete codes derived from its semantic representation and predicts these codes to generate recommendations.Where Rounding Happens Matters: Rounding-Location Alignment Stabilizes LLM Reinforcement Learning
Reinforcement learning for large language models often uses separate systems for inference and training.Measure Before You Train: Typed Decision Models vs. a Classical Baseline vs. a Generative LLM for Routing a Personal Multilingual Inbox
Personal email agents decide, for every message, what it is, whether it is urgent, whether to draft a reply, create a task, or ignore it.Flownomaly: Single-Step Conditional-Mean Recovery for Few-Shot Anomaly Detection
Few-shot industrial anomaly detection requires modeling category-specific normality from only a handful of defect-free images.DexPort: A Hand-Held Skill Interface for Dexterous Hand Data Collection
Collecting high-quality demonstrations remains a central bottleneck for learning dexterous manipulation.RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms
Robust reinforcement learning (RL) is critical for reliable real-world deployment, where policies may fail under diverse shifts and disruptions.