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Laplacian Frequency Interaction Network for Rural Thematic Road Extraction

arXiv:2605.02866v1 Announce Type: new Abstract: Rural thematic road network construction aims to extract topological road structures from movement trajectory images of agricultural machinery. However,

applicationsarxiv-cs-cv

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Human DGX agent

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Large Language Models for Multi-Robot Systems: A Survey

DGX agent

arXiv:2502.03814v5 Announce Type: replace Abstract: The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, ta

agentsarxiv-cs-ro

5 May 2026

Large margin classifier with graph-based adaptive regularization

arXiv:2605.02027v1 Announce Type: new Abstract: This paper introduces the use of per-class regularization hyperparameters in Gabriel graph-based binary classifiers. We demonstrate how the quality inde

Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference

arXiv:2605.02739v1 Announce Type: new Abstract: Dual-system Vision-Language-Action (VLA) models achieve state-of-the-art robotic manipulation but are bottlenecked by the VLM backbone, which must execu

researcharxiv-cs-ro

Latent Space Probing for Adult Content Detection in Video Generative Models

arXiv:2605.00874v1 Announce Type: new Abstract: The rapid proliferation of AI-powered video generation systems has introduced significant challenges in content moderation, particularly with respect to

Latent Trajectory Dynamics in Large Language Models: A Manifold Evolution Framework with Empirical Validation

model-releasesarxiv-cs-cl

arXiv:2505.20340v3 Announce Type: replace Abstract: Understanding how latent representations evolve during generation is a central open problem in large language model interpretability. We introduce e

LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images

arXiv:2605.00899v1 Announce Type: new Abstract: We present LatentDiff, a scalable framework for semantic dataset comparison that operates directly in the latent space of pretrained vision encoders. By

Lateral String Stability for Vehicle Platoons: Formulation, Definition, and Analysis

arXiv:2605.01731v1 Announce Type: new Abstract: Platooning of connected and automated vehicles provides significant benefits in terms of energy efficiency, traffic throughput, and, most critically, sa

safetyarxiv-cs-ro

Hardware

LEAP: Layer-wise Exit-Aware Pretraining for Efficient Transformer Inference

arXiv:2605.01058v1 Announce Type: cross Abstract: Layer-aligned distillation and convergence-based early exit represent two predominant computational efficiency paradigms for transformer inference; ye

hardwarearxiv-cs-cl

Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM

arXiv:2605.01973v1 Announce Type: new Abstract: Conventional LLMs may suffer from corpus heterogeneity and subtle condition changes. While finetuning can create the catastrophe forgetting issue, appli

tutorialsarxiv-cs-cl

Learning a Stochastic Differential Equation Model of Tropical Cyclone Intensification from Reanalysis and Observational Data

arXiv:2601.08116v2 Announce Type: replace Abstract: Tropical cyclones are dangerous natural hazards, but their hazard is challenging to quantify directly from historical datasets due to limited datase

Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation

arXiv:2509.10468v2 Announce Type: replace-cross Abstract: Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenize

tutorialsarxiv-cs-cl

Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach

arXiv:2605.01089v1 Announce Type: new Abstract: The ensemble Gaussian mixture filter (EnGMF) is a powerful, convergent particle filter capable of medium-to-high dimensional non-linear filtering. The E

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions

arXiv:2605.02699v1 Announce Type: cross Abstract: Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is

tutorialsarxiv-cs-cv

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning

model-releasesarxiv-cs-lg

arXiv:2605.01046v1 Announce Type: new Abstract: LoRA adapts large language models (LLMs) by restricting updates to low-rank subspaces of pre-trained weights. While this substantially reduces training

Learning Koopman operators for coupled systems via information on governing equations of subsystems

arXiv:2605.01835v1 Announce Type: new Abstract: Nonlinear coupled systems are ubiquitous in science and engineering. The analysis and modeling of such systems is challenging due to their high dimensio

tutorialsarxiv-cs-lg

Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning

arXiv:2510.03599v2 Announce Type: replace Abstract: We present a unified framework for multi-task locomotion and manipulation policy learning grounded in a contact-explicit representation. Instead of

safetyarxiv-cs-ro

Learning to Place Objects with Programs and Iterative Self Training

arXiv:2503.04496v2 Announce Type: replace-cross Abstract: In this work we study indoor scene object placement. Given a 3D indoor scene and an object, the task is to predict placement locations within

Learning to Race in Minutes: Infoprop Dyna on the Mini Wheelbot

arXiv:2605.01096v1 Announce Type: new Abstract: Reinforcement Learning (RL) has the potential to enable robots with fast, nonlinear, and unstable dynamics to reach the limits of their performance. How

tutorialsarxiv-cs-lg

Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models

arXiv:2605.01591v1 Announce Type: cross Abstract: Neural Ranking Models (NRMs) are central to modern information retrieval but remain highly vulnerable to adversarial manipulation. Existing attacks of

applicationsarxiv-cs-cl

Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure

arXiv:2605.01735v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in real-world systems, they must support post-hoc removal of specific content to meet privacy

safetyarxiv-cs-cl

Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization's Impact on VLMs Beyond Accuracy

arXiv:2509.21173v5 Announce Type: replace Abstract: Vision-Language Models (VLMs) such as CLIP have revolutionized zero-shot classification and safety-critical tasks, including Out-of-Distribution (OO

safetyarxiv-cs-cv

Leveraging Argument Structure to Predict Content Hatefulness

arXiv:2605.02457v1 Announce Type: new Abstract: Information disorder is a challenging phenomenon that affects society at large. This phenomenon entails the diffusion of misleading, misinforming, and h

researcharxiv-cs-cl

Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise

arXiv:2605.01874v1 Announce Type: new Abstract: The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise.

applicationsarxiv-cs-lg

Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions

arXiv:2412.02408v3 Announce Type: replace-cross Abstract: The advent of smart contracts has enabled the rapid rise of Decentralized Finance (DeFi) on the Ethereum blockchain, offering substantial rewa

applicationsarxiv-cs-lg

Leveraging Imperfect Medical Data: A Manifold-Consistent Spatio-Temporal Network for Sensor-based Human Activity Recognition

arXiv:2605.00913v1 Announce Type: new Abstract: Sensor-based Human Activity Recognition (HAR) has attracted increasing attention in medical and healthcare monitoring, particularly with the growth of I

LGDWT-GS: Local and Global Discrete Wavelet-Regularized 3D Gaussian Splatting for Sparse-View Scene Reconstruction

arXiv:2601.17185v2 Announce Type: replace Abstract: We propose a new method for few-shot 3D reconstruction that integrates global and local frequency regularization to stabilize geometry and preserve

LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions

arXiv:2605.00879v1 Announce Type: new Abstract: Rehabilitation aims to help patients with limited mobility regain their physical abilities through targeted movements, exercises, stimulation, and other

researcharxiv-cs-ro

LiDAR Teach, Radar Repeat: Robust Cross-Modal Navigation in Degenerate and Varying Environments

arXiv:2605.02809v1 Announce Type: new Abstract: Long-term autonomy requires robust navigation in environments subject to dynamic and static changes, as well as adverse weather conditions. Teach-and-Re

researcharxiv-cs-ro

LIE: LiDAR-only HD Map Construction with Intensity Enhancement via Online Knowledge Distillation

arXiv:2605.01478v1 Announce Type: new Abstract: Online High-Definition (HD) map construction is a key component of autonomous driving. Recent methods rely on multi-view camera images for cost-effectiv

agentsarxiv-cs-cv

Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation

arXiv:2202.10873v4 Announce Type: replace-cross Abstract: Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or b

Limited-Angle Tomography Reconstruction via Projector Guided 3D Diffusion

arXiv:2510.06516v2 Announce Type: replace Abstract: Limited-angle electron tomography aims to reconstruct 3D shapes from 2D projections of Transmission Electron Microscopy (TEM) within a restricted ra

tutorialsarxiv-cs-cv

Linear-Readout Floors and Threshold Recovery in Computation in Superposition

arXiv:2605.01192v1 Announce Type: new Abstract: Two recent approaches to computation in superposition reach different recursive capacity regimes: Hanni et al. certify ilde{O}(d^{3/2}) computable featu

Linear-Time Global Visual Modeling without Explicit Attention

arXiv:2605.01711v1 Announce Type: new Abstract: Existing research largely attributes the global sequence modeling capability of Transformers to the explicit computation of attention weights, a process

Local Ai

Linearizing Vision Transformer with Test-Time Training

arXiv:2605.02772v1 Announce Type: new Abstract: While linear-complexity attention mechanisms offer a promising alternative to Softmax attention for overcoming the quadratic bottleneck, training such m

local-aiarxiv-cs-cv

LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery

arXiv:2508.12232v3 Announce Type: replace-cross Abstract: Issue-to-commit link recovery in software repositories is fundamental to software traceability and project management, yet it remains a challe

agentsarxiv-cs-ai

Linking spatial biology and clinical histology via Haiku

arXiv:2605.00925v1 Announce Type: cross Abstract: Integrating molecular, morphological, and clinical data is essential for basic and translational biomedical research, yet systematic frameworks for jo

safetyarxiv-cs-cv

LinMU: Multimodal Understanding Made Linear

arXiv:2601.01322v2 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-attention, which prevents th

LITcoder: A General-Purpose Library for Building and Comparing Encoding Models

arXiv:2509.09152v2 Announce Type: replace Abstract: We introduce LITcoder, an open-source library for building and benchmarking neural encoding models. Designed as a flexible backend, LITcoder provide

researcharxiv-cs-cl

LiteVLA-H: Dual-Rate Vision-Language-Action Inference for Onboard Aerial Guidance and Semantic Perception

arXiv:2605.00884v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong semantic grounding and task generalization in manipulation, but aerial deployment remains difficul

LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment

model-releasesarxiv-cs-lg

arXiv:2603.00042v2 Announce Type: replace Abstract: We identify the Spectral Energy Gain in extreme model compression, where low-rank binary approximations outperform tiny-rank floating-point baseline

LLM-Augmented Semantic Steering of Text Embedding Projection Spaces

arXiv:2605.01957v1 Announce Type: cross Abstract: Low-dimensional projections of text embeddings support visual analysis of document collections, but their spatial organization may not reflect the rel

safetyarxiv-cs-cl

LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk

arXiv:2511.19175v2 Announce Type: replace-cross Abstract: A critical barrier to the trustworthiness of sixth-generation (6G) agentic autonomous networks is the uncertainty neglect bias; a cognitive te

safetyarxiv-cs-ai

LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging

model-releasesarxiv-cs-ro

arXiv:2605.01461v1 Announce Type: new Abstract: Swarm foraging algorithms, such as the central-place foraging algorithm (CPFA), typically rely on offline parameter optimization using genetic algorithm

LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning

arXiv:2605.01047v1 Announce Type: cross Abstract: Hallucinations, outputs that sound plausible but are factually incorrect, remain an open challenge for deployed LLMs. In code generation, models frequ

agentsarxiv-cs-cl

Hardware

LLM Output Detectability and Task Performance Can be Jointly Optimized

arXiv:2605.01350v1 Announce Type: new Abstract: Detecting machine-generated text is essential for transparency and accountability when deploying large language models (LLMs). Among detection approache

hardwarearxiv-cs-cl

LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment

arXiv:2601.19487v2 Announce Type: replace Abstract: Safety-aligned LLMs suffer from two failure modes: jailbreak (answering harmful inputs) and over-refusal (declining benign queries). Existing vector

safetyarxiv-cs-lg

Local Hessian Spectral Filtering for Robust Intrinsic Dimension Estimation

arXiv:2605.01221v1 Announce Type: new Abstract: While diffusion models enable new approaches for estimating Local Intrinsic Dimension (LID), existing methods fail in high-dimensional spaces where nois