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LABIT: Long-Horizon Robotic Assembly Benchmark for Industrial Tasks

model-releasesarxiv-cs-ro

arXiv:2610.16398v1 Announce Type: new Abstract: Robot learning has undergone significant advancements in the domain of compliant control and robotic assembly. The development of novel methodologies ha

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LACRM: LLM-Guided Mining of Directed Advertiser Competition Relationships in E-Commerce Advertising

DGX agent

arXiv:2610.13959v1 Announce Type: new Abstract: In e-commerce advertising, ad platforms typically fail to model the externalities among advertisers when making optimization decisions: an advertiser's

model-releasesarxiv-cs-lg

14 Oct 2026

LADDER: A Long-Horizon Robot Manipulation Benchmark

arXiv:2610.18053v1 Announce Type: cross Abstract: General-purpose robot manipulation policies learn short-horizon skills effectively, but degrade rapidly on long-horizon tasks. Existing benchmarks can

LAG-IO: Multi-IMU State Estimation for Legged Robots Using Factor Graphs

arXiv:2610.17870v1 Announce Type: new Abstract: Robust proprioceptive state estimation is crucial for legged robots, particularly in challenging environments where exteroceptive sensors are unreliable

researcharxiv-cs-ro

Language2Drive: Do Vision-Language-Action Models Really Follow the Instructions in Autonomous Driving?

model-releasesarxiv-cs-cv

arXiv:2610.16634v1 Announce Type: cross Abstract: Recent Vision-Language-Action (VLA) models increasingly use natural-language instructions to condition autonomous driving behavior. Yet existing close

LapMerge: Differentiable Exact-Budget Token Merging in Linear Time

arXiv:2610.13858v2 Announce Type: replace Abstract: Modern vision and language models process ever longer token sequences, and reducing them is the main lever on downstream cost. Token compression, ho

Last-Iterate Convergence of Clipped SGD with Linear Decay

arXiv:2610.18555v1 Announce Type: new Abstract: In this paper, we study the last-iterate convergence of clipped stochastic gradient descent under heavy-tailed noise. We assume that the stochastic grad

Applications

Latent-Space Importance Weighting under Covariate Shift

arXiv:2610.15571v1 Announce Type: cross Abstract: In this work, we propose a transfer learning algorithm for covariate shift, integrating sample reweighting with representation learning. A key feature

applicationsarxiv-cs-lg

Latent Taxonomies: How Property Induction Develops in Language Model Pre-training

arXiv:2610.15836v1 Announce Type: cross Abstract: When told that robins have sesamoid bones, people are far more prone to extend the property to sparrows than to whales. This behavior, called category

Latent World Action Model for GUI Agents

arXiv:2610.14228v1 Announce Type: new Abstract: Pixel-space world models have recently emerged as a promising approach for GUI agents, predicting future screens prior to execution and subsequently re-

LatentRiskBench: Measuring State-Dependent Risks in Enterprise Agents

arXiv:2610.17685v1 Announce Type: cross Abstract: Enterprise agents face safety risks that cannot be determined from individual actions alone. Existing safety benchmarks largely evaluate action-local

Layer-wise Input Recoverability in Deep Neural Networks

arXiv:2610.14290v1 Announce Type: new Abstract: The inner workings of deep neural networks are still not well understood. The information bottleneck framework, which characterizes deep networks by the

LayerCanvas: Infographic Generation in AI Learning Assistants via Hybrid Layer-Set Diffusion

arXiv:2610.18763v1 Announce Type: new Abstract: Online learning platforms are increasingly deploying AI assistants to provide personalized, on-demand tutoring. These can leverage infographics to commu

LeanPRM: Lean-Grounded Process Supervision for Natural Language Proof Verification

arXiv:2610.17697v1 Announce Type: new Abstract: AI systems now write long and difficult mathematical proofs, and checking their step-by-step validity is becoming a bottleneck. A learned verifier could

Learn from Move: the Next Step for GUI Agents

arXiv:2610.16720v1 Announce Type: new Abstract: Existing benchmarks for GUI agents primarily focus on increasing task difficulty by extending task horizons. Besides, enabling agents to explore unfamil

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

Learnable Low-Rank Polynomial Sketch for Effective Linear Attention

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

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

safetyarxiv-cs-cv

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 (

safetyarxiv-cs-ro

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

safetyarxiv-cs-ro

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

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

safetyarxiv-cs-ro

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

safetyarxiv-cs-cv

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