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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 140
On the gradient conjecture at infinity: Finite spherical length for gradient trajectories of definable potentials
We prove that for every open $ U \subseteq \mathbb R^n $ the spherical projection of every gradient trajectory $θ(t)$, $t \geq 0$, of a definable $C^1$-potential $F \colon U \to \mathbb R$ has…Preference Is Not Permission: Personalizing the Auto-Purchase Line in Agentic Commerce
Personalized shopping benchmarks ask which item fits the user; we ask how far an agent may commit on that item, and pose learning the individual's auto-purchase line as a task.PerSPL: Personalized Semi-supervised Preference Learning
Personalized reward models require user-specific preference comparisons, but collecting such feedback is costly and provides only limited supervision for each user.Delay-Guided Attention for Quality Prediction in Multistage Manufacturing
Quality prediction in multistage manufacturing is challenging when inter-stage lags vary over time, as measurements taken at the same timestamp may reflect different process states.Schrödinger-Bass Bridge Barycenter Models for Measure-Valued Data
Barycenter models, based on stochastic and deterministic optimal transport bridges between probability measures, have proven to be useful for supervised and unsupervised learning for (probability)…Cache Locally, Recompute Selectively for Streaming Video Understanding
Streaming video understanding (SVU) requires video-LLMs to process incoming video continuously while answering user queries with low latency.MA-UITD: Offline Multi-Agent Reward Inference for Characterizing Continuous Social Behavior
Multi-agent inverse reinforcement learning offers a powerful framework not only for imitation learning but also for understanding the neural computations supporting social interactions and…Extending FAIR Principles with Observability for Trustworthy Federated Learning
Federated learning (FL) keeps data at its source, but locality alone does not make a federation trustworthy.Group Distributionally Robust Optimization over Decentralized Networks
We study group distributionally robust optimization (GDRO) over decentralized networks, where workers collaborate to learn robust models by minimizing the maximum local risk.Estimating Performance for Safe Deployment of Machine Learning Models
The main challenge in applying deep neural networks to real-world scenarios lies in distributional discrepancies between training and test datasets, often caused by variations in data acquisition…Design and Tuning of Multi-Agent Systems Using Bayesian Evolutionary Optimization
Designing effective multi-agent systems (MAS) remains a challenging and resource-intensive task.Flat-RLVR: Sharpness-Aware Reinforcement Learning with Verifiable Rewards for Reasoning Capacity in LLMs
Reinforcement learning with verifiable rewards (RLVR), particularly through group-relative policy optimization (GRPO), has emerged as a central paradigm for the post-training of large language…Approximate Solomonoff Induction: On the Robustness of Learned Universal Predictors
Why large language models trained via log-loss minimization over massive and diverse datasets exhibit broad capabilities and generalizable skills is not yet fully understood.Compressing Onboard Language Models for Space Robots under a Behavioral-Fidelity Requirement
Language models are being explored as the interface through which space robots interpret operator commands and select among verified skills.ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery
Evolutionary coding agents such as AlphaEvolve are one of the notable successes in using Large Language Models (LLMs) to build AI Scientists.Data-driven and physics-guided hindcasting of measured wave spectra
Short-range hindcasts of measured wave spectra are evaluated using a recurrent model initialized by one hourly buoy spectrum and supplied with local wind, fetch and depth.Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming
Cutting planes (cuts) remove fractional solutions from mixed-integer linear programs (MILPs) without excluding any feasible integer solutions, making them a key ingredient in modern MILP solvers.YingLan: A Multimodal Ocean Foundation Model for Unified Representation Learning from Heterogeneous Observations
Multimodal ocean modeling remains challenging due to substantial heterogeneity in data structures, physical semantics, and spatiotemporal characteristics across satellite imagery, gridded…DISRL: Dual-Scale Incremental Semi-Supervised Reinforcement Learning for Dynamic Community Tracking
Dynamic local community detection tracks the evolving community of designated seed nodes across graph snapshots.BrainCodes: A Lightweight EEG Foundation Model over Discrete Neural Codes
Recent electroencephalography (EEG) foundation models increasingly emphasize model scale, potentially limiting their deployment on resource-constrained devices and in everyday environments.