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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 170
Agent World Model in the Wild: Computer-Use Agent Environment Synthesis
Agent learning is shifting from learning from human data toward learning through interaction with the world.ProteinGeoBench: Frontier Structural-Biology Agents Are Fluent at Lookup but Lost in Geometry
Frontier agents retrieve structural metadata well and compute geometry from coordinates less reliably.Supervised Learning with Quantum Hamiltonian Embeddings
Machine learning is widely regarded as a promising application in which quantum computing could offer a computational advantage.Kronecker Delta Rule for Long Context Linear Attention
We propose KronLSTM, a linear attention architecture based on the Kronecker Delta Rule, a novel update rule for associative matrix memories.Newton-Based Methods for Bilevel Optimization Beyond Lower-Level Strong Convexity
Bilevel optimization provides a framework for machine learning tasks such as hyperparameter optimization, data reweighting, and meta-learning.WeAct: Dynamic Weight Switching as Agent Actions
Long-horizon agents must learn heterogeneous capabilities from interleaved trajectories.CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting
Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments.OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory
Multimodal large language models (MLLMs) have made rapid progress on visual reasoning, yet many tasks require evidence that is difficult to obtain through internal reasoning alone: our controlled…Text-to-Image Generative Modeling Without Language Pretraining
Modern text-to-image (T2I) systems use internet-scale language pretraining for text-conditioned image generation.Latent Projection for Latent Planning
Latent world models let an agent plan in latent space.Dynamic Dual-Feedback Conformal Inference for Time Series Forecasting
Quantifying uncertainty in time series forecasting is particularly demanding because sequential data exhibit temporal dependence and are prone to distributional changes.CoASFL: Coupled Consistency Optimization for Asynchronous Split Federated Learning
Split Federated Learning (SFL) has emerged as a promising paradigm for training large-scale deep models on resource-constrained edge devices.CorteXforge: Scenario-Based RF Dataset Generation on Real Testbeds
Deep learning for radio frequency (RF) signal analysis relies on large annotated datasets, yet synthetic datasets trade physical realism for controllability, while uncontrolled over-theair…ORBIT: Autoregressive Time Series Generation via Next Frequency-Band Prediction
Deep generative models provide an effective modern solution to alleviating the problem of data scarcity by synthesizing realistic time series.HierDTA: Hierarchical Structure-Aware Representation Learning for Drug--Target Affinity Prediction
Drug--target affinity (DTA) prediction plays an important role in computer-aided drug discovery, where reliable computational models can help prioritize compound--protein pairs before experimental…Robust Latent Variable Graphical Models: Exact Recovery and Fundamental Limits
We study robust estimation of a Gaussian precision matrix that decomposes as a sparse matrix minus a symmetric, possibly indefinite, low-rank matrix, a structure that arises when latent-variable…Exploration via Activation-Space Steering for Reinforcement Learning with Verifiable Rewards
Reinforcement learning with verifiable rewards (RLVR) trains a language model by sampling responses, scoring them with an automatic verifier, and updating the policy from the resulting rewards.MultiCard: Multiple Attribute Cardinality Estimation via Deep Learning
Cardinality estimation is essential for query optimization, but most existing learned estimators focus on a single predicate type, such as string LIKE clauses or numerical range filters.Reliable Estimation of Differential Privacy under Selection Bias
Statistical privacy audits are essential for verifying the theoretical privacy guarantees of differentially private (DP) mechanisms.Gaussian Approximation of Besov Functions for Logistic Classification on Manifolds
We study Gaussian kernel approximation and regularized logistic classification on a connected compact smooth embedded manifold $X\subset\mathbb R^d$ without boundary and of intrinsic dimension…