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Learn the Dynamics, Enforce the Physics: Generative Latent Operators for High-Dimensional Beam Evolution

arXiv:2610.14537v1 Announce Type: cross Abstract: Particle-in-cell (PIC) simulation is indispensable for modeling beam dynamics in particle accelerators, but its computational cost limits many-query w

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Learnable Low-Rank Polynomial Sketch for Effective Linear Attention

DGX agent

arXiv:2610.14219v1 Announce Type: new Abstract: Softmax attention in Transformers suffers from quadratic complexity in sequence length, making it impractical for long-context applications. Linear atte

researcharxiv-cs-lg

14 Oct 2026

Learning a Group Element from Its Action: Galois Correspondences, Optimal ERM, and an Abel-Ruffini Threshold for the Natarajan Dimension

arXiv:2610.18154v1 Announce Type: new Abstract: We study learning an unknown element g of a group acting with finite orbits on a countable set, from examples (x,gx). After a sample on A, the consisten

Learning, Compression, and Meta-Complexity

arXiv:2610.16535v1 Announce Type: cross Abstract: The idea that learning is closely tied to compression has long served as both a powerful principle and, at times, an informal analogy across machine l

Applications

Learning Deployable Causal Action Geometry under Temporal Non-Stationarity

arXiv:2610.16644v1 Announce Type: new Abstract: Learning a response law for continuous decisions from observational time series is central to many real-world decision systems, yet temporal non-station

applicationsarxiv-cs-lg

Learning Distributions from Samples and Qualities

arXiv:2610.17956v1 Announce Type: cross Abstract: We study parameter estimation when each sample from a continuous parametric distribution is accompanied by a binary quality label governed by the same

Learning Dynamic Grasping

arXiv:2610.14848v1 Announce Type: new Abstract: Synthesizing believable 4D hand-object grasping sequences is important for character animation, extended reality, and embodied agents. Grasping requires

researcharxiv-cs-ro

Learning Families of Fixed-Wing Aircraft Control Policies with Hypernetwork-Conditioned Reinforcement Learning

arXiv:2604.03392v2 Announce Type: replace-cross Abstract: This paper investigates whether it is more effective to condition reinforcement learning policies across changing dynamics using standard inpu

Learning from fracture: Teaching language models to design through physical experience

arXiv:2610.17804v1 Announce Type: cross Abstract: How can language models learn to optimize physical designs through experience? We introduce FracLearn, an experience-abstraction-generalization framew

Agents

Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization

arXiv:2610.16537v1 Announce Type: new Abstract: In a variety of multi-agent and physical environments, an element's update depends strongly on a few other elements and weakly on many, and both the ide

agentsarxiv-cs-lg

Learning Incompressible 3D Flows from Sparse Samples: A Set Transformer Approach to Divergence-Free Kernel Fitting

arXiv:2610.16645v1 Announce Type: new Abstract: Recovering divergence-free velocity fields from sparse and corrupted observations requires reconciling physical consistency, expressive representations,

Learning Multimodal Sensor Fusion with JEPA Representations for Breathing Activity and Pose Recognition

model-releasesarxiv-cs-cv

arXiv:2610.15891v1 Announce Type: new Abstract: Predictive video representations do not by themselves specify how a multimodal system should handle missing sensors. We transfer frozen V-JEPA2 features

Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters

arXiv:2610.14246v1 Announce Type: new Abstract: Variance-based methods recover a Gaussian linear structural equation model (SEM) with equal or suitably constrained error variances, either by recursive

Learning omega-Powered Conditional Distributions with Importance-Weighted Flow Matching

arXiv:2610.16419v1 Announce Type: cross Abstract: Classifier-Free Guidance (CFG) with guidance scale gamma is commonly motivated by the omega-powered conditional distribution obtained by setting omega

Learning Operators for Symmetry Discovery: From Lie Groups to Local Symmetry Geometry

arXiv:2610.16884v1 Announce Type: new Abstract: Continuous symmetry discovery is typically formulated as a separate estimation problem for each function or dataset. We instead investigate whether the

Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction

arXiv:2610.18456v1 Announce Type: new Abstract: AI-based cellular perturbation prediction is essential to accelerate drug discovery and therapeutic identification. However, perturbation-specific signa

Learning Reachability-Based Safety Representations for Real-Time Robot Navigation in Unknown and Dynamic Environments

arXiv:2610.16522v1 Announce Type: cross Abstract: Safe robot navigation in unknown, dynamic environments requires more than geometric collision checking: a robot still far from an obstacle may be unab

Learning Robot Policies for Whole-Body Mobility Assistance via Dynamic Human Motion Generation

arXiv:2610.17750v1 Announce Type: new Abstract: Robotic assistance for human mobility could benefit care recipients and reduce caregiver burden during physically demanding tasks such as Sit-to-Stand (

Learning Robust Visuomotor Policies via Dynamics Alignment

arXiv:2510.27114v2 Announce Type: replace-cross Abstract: Generative BC policies model expert action distributions well, but they do not explicitly reason about what scene a candidate action would pro

Learning Safety-Constrained Whole-Body Loco-Manipulation for Lunar Surface Operations

arXiv:2610.15486v1 Announce Type: new Abstract: Robotic systems for lunar surface operations must act autonomously and preserve their own integrity in an environment where failures are unrecoverable a

Learning Shortest Paths with Generative Flow Networks

arXiv:2603.01786v2 Announce Type: replace Abstract: In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we

Learning Source Structure: Bayesian Prediction with Transformers

arXiv:2610.18725v1 Announce Type: new Abstract: A transformer trained on sequences whose source parameters are drawn afresh each time can learn only what the sequences share: the source family and its

Learning Temporal Evidence Selection for Video Anomaly Understanding

arXiv:2610.16299v1 Announce Type: new Abstract: Video anomaly understanding (VAU) aims to generate language-level interpretations of anomalous events in long videos, requiring visual evidence that sup

researcharxiv-cs-cv

Learning to Assist: Collaborative VLAs for Implicit Human-Robot Collaboration

arXiv:2606.12475v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collabo

Learning to Design Convex Relaxations for Nonconvex Optimization

arXiv:2610.16948v1 Announce Type: new Abstract: Convex relaxations make nonconvex optimization tractable by replacing hard nonconvex problems with convex surrogates that give valid bounds. Yet their d

Agents

Learning to Design SR Solvers: Harness Optimization for Symbolic Regression

arXiv:2610.16966v1 Announce Type: new Abstract: Symbolic regression (SR) seeks explicit equations that explain observed data, and recent methods increasingly employ large language models (LLMs) and ag

agentsarxiv-cs-lg

Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting

arXiv:2610.17399v1 Announce Type: new Abstract: Continuous monitoring of generative AI systems requires detecting distribution shifts in the unstructured data they ingest and generate. Existing monito

Learning to Extrapolate in Depth with Looped Transformers

arXiv:2610.14456v1 Announce Type: new Abstract: Looped transformers reuse a single block across iterations, raising a natural question: how far can a block trained at one depth be safely extrapolated

Learning to See through Action: Predictive Curiosity for Robots

arXiv:2610.14302v1 Announce Type: new Abstract: Humans and animals do not learn vision from a fixed set of data: they move, interact, and learn from the visual consequences of their actions. Motivated

Local Ai

Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning

arXiv:2610.14268v1 Announce Type: cross Abstract: The scalability and performance of edge federated learning are fundamentally constrained by synchronization bottlenecks. Clients operate under heterog

local-aiarxiv-cs-lg

Learning Transferable World-Action Models from Task-Paired Human Videos

arXiv:2610.14351v1 Announce Type: new Abstract: World-action models inherit broad visual and physical priors from video pre-training. However, standard within-video next-frame prediction allows them t

Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains

arXiv:2610.18730v1 Announce Type: new Abstract: Manufacturers often need to characterize a new material, process, or product before enough operating data are available to train reliable predictive mod

LeHami: Symplectic Latent Dynamics for World Models

arXiv:2610.15267v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) learn world models directly in latent space, but unconstrained autoregressive predictors may contract t

Applications

LensErase: A Foundation Model for Erase Anything

arXiv:2610.18072v1 Announce Type: new Abstract: We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. L

applicationsarxiv-cs-cv

LERR-Nav: Learning to Coordinate Exploration, Recovery, and Reminiscing for Object Navigation

arXiv:2610.15362v1 Announce Type: new Abstract: Object Navigation in unseen environments remains a fundamental challenge in embodied AI, as it requires agents to generalize visual and spatial reasonin

Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision

arXiv:2610.15612v1 Announce Type: new Abstract: Accurate lesion segmentation in mammography is important for computer-aided breast cancer diagnosis, but dense pixel-level annotations remain costly and

researcharxiv-cs-cv

Less is More: Label-Guided Efficient Summarization of Procedural and Instructional Videos

arXiv:2601.12243v3 Announce Type: replace Abstract: Video summarization transforms long videos into concise representations that are easier to document and analyze, especially in high-stakes domains s

Less Noise, Same Certificate: Retain Sensitivity for Unlearning

arXiv:2603.03172v2 Announce Type: replace Abstract: Certified machine unlearning aims to provably remove the influence of a deletion set U from a model trained on a dataset S, by producing an unlearne

Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.18352v1 Announce Type: new Abstract: A robot following a multi-step instruction needs to act on the right objects in the right order, even as the scene changes. It must also maintain awaren

LETS: Low-Overhead External-Torque Sensing for Force-Feedback Bilateral Teleoperation

arXiv:2610.14889v1 Announce Type: new Abstract: Bilateral teleoperation with force feedback is becoming increasingly important, and the quality of external-torque estimation is critical to this proces

researcharxiv-cs-ro

LexiCan: Lexicographic Canonicalization of Point Clouds for Machine Learning Applications

arXiv:2610.15171v1 Announce Type: new Abstract: Canonicalization resolves the rotational and permutation ambiguity of 3D point clouds by selecting a unique orbit representative, maintaining their geom

LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2610.18843v1 Announce Type: new Abstract: Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, ca

LiDAR-based Humanoid Navigation in Dynamic, Non-Convex Environments via Spatio-Temporal Safe Corridors

arXiv:2610.17232v1 Announce Type: new Abstract: Navigating humanoid robots through dynamic, non-convex environments requires tightly coupling onboard perception with footstep planning and dynamic loco

researcharxiv-cs-ro

LIFT: Likelihood-Based Intervention Faithfulness Training

arXiv:2509.13334v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning improves LLM accuracy on complex tasks, but CoTs are often unfaithful; that is, the model does not rely on th

Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence

arXiv:2610.18166v1 Announce Type: cross Abstract: Randomly initialised neural networks of width m converge to Gaussian processes as m o infty, with two types of quantitative bounds characterising this

Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements

arXiv:2610.18482v1 Announce Type: cross Abstract: Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependen

LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT

arXiv:2610.18243v1 Announce Type: new Abstract: Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportunist

LightVAE: Towards Compact and Efficient Video Autoencoders

arXiv:2610.14726v1 Announce Type: new Abstract: Latent diffusion models enable high-quality video generation, but video VAE encoding and decoding remain computationally expensive. Existing methods use