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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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Adaptive Memory Index Evolution for Agents
Long-horizon agents continually accumulate experience, yet retaining a memory does not guarantee access when it is needed.Reinforcement learning for constrained stochastic linear--quadratic control in continuous time
We study continuous-time stochastic linear--quadratic control with a scalar state and nonnegative control constraints.Attention under Bounded Key-Query Similarity: Space Complexity and Transformer Anisotropy
A growing body of work on KV-cache compression aims to reduce the memory requirements of autoregressive generation.BellowsKV: One Latent for Many Layers, Expanded on Demand
Large language models (LLMs) now serve as the backbone of agents that reason over many turns, call tools, and pursue long-horizon tasks.AutoPRL: Automated Path Representation Learning via a Learnable View Generator
Self-supervised learning has emerged as a powerful paradigm for Path Representation Learning (PRL) in road networks.Macro Mondrian Conformal Risk Control
Conformal Risk Control (CRC) extends conformal prediction beyond the coverage setting to the control of general monotone loss functions, in a distribution-free and modelagnostic fashion.Mapping the Solution-Set Structure of Grokking on Latin Squares
Grokking is a delayed transition from memorisation to generalisation.Risk-Aware Output Control Should be the Focus of Machine Unlearning
Well-documented failure modes of machine learning (ML) systems, including bias, toxicity, hallucination, and leakage of sensitive information, has driven the rise of machine unlearning (MU).Reinforcement Learning Done Right for Explicit Confidence Targets
Large language models (LLMs) can produce plausible but incorrect answers when they are uncertain, which can mislead users and undermine model reliability.Joint Dynamic Lane Reversal and Variable Speed Limit Control: A Graphormer-Based Approach
Connected and automated vehicles could allow reassigning lane directions and speed limits on every road frequently, but the benefit hinges on how traffic reroutes, and one-way roads can isolate…Blendshape Generation for Models with Disconnected Parts: Adding ARKit/MediaPipe Blendshapes to Anime-Style Character Models
Blendshape generation for 3D models has been extensively researched, but very few works address generating blendshapes for models with multiple disconnected parts.Improving VLMs on Scientific Chart Analysis via Autonomous Evolution
Understanding and operating with scientific charts is a standard approach in data analysis, and while humans are good at interpreting such charts, open vision-language models (VLMs) are known to…Toward Fast Human Reaction Modeling from High-Frequency Digital Interaction Data
We investigate whether limbic-like signatures of rapid, salience-driven behavior can be identified in high-resolution Counter-Strike 2 telemetry.A JiT-Based Denoising Defense Method Against Adversarial Examples for Radio Signal Automatic Modulation Classification
Automatic modulation classification (AMC) of radio signals is a core technology for spectrum monitoring and electromagnetic situational awareness.Uncertainty Evaluation to Support Safety of the Intended Functionality Analysis for Determining the Applicability of Performance Targets Across Triggering Conditions in ML-Based LiDAR Object Detection
For safety of the intended functionality (SOTIF) analysis according to ISO 21448, a machine learning object detector for light detection and ranging (LiDAR) point clouds can meet a performance…Spherical Brownian Diffusion for Language Modeling
Continuous diffusion language models involve two coupled design choices: how to corrupt continuous token representations and how to invert that corruption at generation time.FlowGenesis: Representation-Driven Self-Organization of Neural Architectures
Deep neural networks typically rely on architectures specified before training, despite the continuous evolution of their internal representations.Subspace Crystallization in Grokking: Rank Compression Tracks the Phase Transition
Grokking describes understanding something deeply and intuitively.UTA: Universal Training Adaptive Learning Rate Decay
Learning rate decay (LRD) is critical for training high-performance models, yet most designs remain empirically driven.Generative AI in Turbulence: Foundations, Applications, Validations and Benchmarks
Generative AI offers new approaches to turbulence problems in which incomplete observations or unresolved d.o.f. admit multiple physically plausible solutions.