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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 79
Meshless Latent Dynamics Models for Model Order Reduction of time-dependent Partial Differential Equations
Latent dynamics learning has emerged as a prominent approach for Model Order Reduction (MOR) of time-dependent, parameterized Partial Differential Equations (PDEs).Consensus Within, Competition Between: Decentralized LLM Teams for Perimeter Defense
The use of large language models (LLMs) to coordinate multi-agent teams raises a fundamental architectural question: whether task allocation should be centralized in a single LLM controller or…Localizing predictive concepts encoded in neural representations via hierarchical testing
Understanding which regulatory sequence motifs a genomic deep learning model has learned, with statistical error guarantees, is essential for trusting its use in downstream tasks such as variant…Communication-Efficient Private Federated Reinforcement Learning with Post-Quantum Authentication for Vehicular Networks
Federated reinforcement learning supports cooperative vehicular routing without centralizing trajectory data, but model updates may reveal private information and incur communication overhead over…Nonlinear Maps Between Irreducible Representations in Graph Neural Networks
Equivariant linear maps cannot mix non-isomorphic irreducible representations, whereas nonlinear activations can.Single-Annotation, Multi-Hypothesis Segmentation with Semantic-Spatial Residual Banks
Multi-rater segmentation datasets show that a single image can admit several valid masks, yet most training sets expose only one annotation per image.D2ML: Models for Direct-to-Biology Small Molecule Screening
Direct-to-biology (D2B) screening enables rapid testing of newly synthesized molecules without purification.bf16 Rounding Silently Discards Most RL Policy Updates: A Hidden Training-Inference Mismatch and an Unbiased One-Line Fix
Reinforcement learning (RL) post-training of language models keeps fp32 master weights in the optimizer and serves rollouts from a bf16 copy produced by round-to-nearest conversion.Where, When, and How Many Bits? Scaling Laws for SGD with Stochastic Rounding
Low-precision training reduces computation but introduces additional rounding error.Stochastic and Noisy Oracles in Smooth Continuous Optimization
We review models of inexact computational oracles in smooth continuous optimization and their implications for the convergence, computational complexity, and attainable accuracy of optimization…DTP-OPSD: Reinforcement Learning with Distractor-Aware Temporal-Privilege On-Policy Self-Distillation for Temporal Video Grounding
Temporal Video Grounding (TVG) aims to localize the start and end times of an event described by a language query in a video.FB-MOAC: A Reinforcement Learning Algorithm for Forward-Backward Markov Decision Processes
Reinforcement learning (RL) algorithms are effective in solving problems that can be modeled as Markov decision processes (MDPs).All Paths All at Once: A Quantum Monte Carlo and Neural Committor Approach to Reaction Pathway Search
Locating competing reaction pathways is difficult when potential-energy gradients are unavailable.Jailbreak Connectivity: Towards Diverse, Transferable, and Universal Multimodal Large Language Model Jailbreak
Multimodal large language models (MLLMs) are vulnerable to visual jailbreak attacks that bypass safety safeguards.Nemotron 3.5 Super and Lightning: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Models for Agentic AI
We introduce Nemotron 3.5 Super and Nemotron 3.5 Lightning, open and efficient Mixture-of-Experts Hybrid Mamba-Transformer models designed for reasoning, long-context understanding, and agentic…Evaluating Causal Reasoning in LLMs: A Case Study in the Challenges of Procedural Generation
Large language models (LLMs) appear to represent and reason about a large range of phenomena.Same Label, Different Interface: Beyond Action-Space Labels in Robot Learning
Robot-learning studies often compare position and velocity actions as though each label identified a single experimental treatment, yet the label alone does not determine what the robot executes.TGTV: An Interpretable Framework for UAV Fault Detection and Traceability via Spatiotemporal Graph Learning and Large Language Models
Fixed-wing unmanned aerial vehicles experience operating-condition transitions, asynchronous sensor sampling, and control-system compensation that can obscure fault signatures.Who Called? V33DA: A Physically Verified Multimodal Benchmark for Vocal Attribution in Zebra Finch Groups
Deciding which member of an animal group produced a vocalization and where that vocalization was produced are central to studying social communication but are rarely evaluated with physically…Post-Training Large Language Models with LLMON
Large Language Models (LLMs) treat all context tokens uniformly, lacking explicit separation between executable instructions and passive data.