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LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases

arXiv:2512.12643v2 Announce Type: replace Abstract: Legal relations serve as an important analytical framework for dispute resolution in civil cases. However, legal relations in Chinese civil cases re

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LiDAR-based Crowd Navigation with Visible Edge Group Representation

DGX agent

arXiv:2604.16741v1 Announce Type: new Abstract: Robot navigation in crowded pedestrian environments is a well-known challenge and we explore the practical deployment of group-based representations in

safetyarxiv-cs-ro

21 Apr 2026

LiFT: Does Instruction Fine-Tuning Improve In-Context Learning for Longitudinal Modelling by Large Language Models?

arXiv:2604.16382v1 Announce Type: new Abstract: Longitudinal NLP tasks require reasoning over temporally ordered text to detect persistence and change in human behavior and opinions. However, in-conte

LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning

arXiv:2506.00772v2 Announce Type: replace-cross Abstract: Recent studies have shown that supervised fine-tuning of LLMs on a small number of high-quality datasets can yield strong reasoning capabiliti

Light-Adapted Electroretinogram and Oscillatory Potentials (LEOPs) Dataset for Autism Spectrum Disorder and Typically Developing Individuals

arXiv:2604.16981v1 Announce Type: cross Abstract: The LEOPs (Light-ERG-Oscillatory Potentials) dataset provides light-adapted (LA) electroretinogram (ERG) and Oscillatory Potentials (OPs) waveforms fo

Applications

Lightweight Cybersickness Detection based on User-Specific Eye and Head Tracking Data in Virtual Reality

arXiv:2604.17158v1 Announce Type: cross Abstract: The occurrence of cybersickness in virtual reality (VR) significantly impairs users' perception and sense of immersion. Therefore, timely detection of

applicationsarxiv-cs-lg

Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage

arXiv:2601.03043v3 Announce Type: replace Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing si

Linear-Time and Constant-Memory Text Embeddings Based on Recurrent Language Models

arXiv:2604.18199v1 Announce Type: new Abstract: Transformer-based embedding models suffer from quadratic computational and linear memory complexity, limiting their utility for long sequences. We propo

Linking Exteroception and Proprioception through Improved Contact Modeling for Soft Growing Robots

arXiv:2507.10694v2 Announce Type: replace Abstract: Passive deformation due to compliance is a commonly used benefit of soft robots, providing opportunities to achieve robust actuation with few active

researcharxiv-cs-ro

LiquidTAD: An Efficient Method for Temporal Action Detection via Liquid Neural Dynamics

arXiv:2604.18274v1 Announce Type: new Abstract: Temporal Action Detection (TAD) in untrimmed videos is currently dominated by Transformer-based architectures. While high-performing, their quadratic co

Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length

arXiv:2512.04677v5 Announce Type: replace Abstract: Audio-driven avatar interaction demands real-time, streaming, and infinite-length generation -- capabilities fundamentally at odds with the sequenti

LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing

arXiv:2604.17021v1 Announce Type: new Abstract: Video editing aims to modify input videos according to user intent. Recently, end-to-end training methods have garnered widespread attention, constructi

Agents

Live LTL Progress Tracking: Towards Task-Based Exploration

arXiv:2604.17106v1 Announce Type: new Abstract: Motivated by the challenge presented by non-Markovian objectives in reinforcement learning (RL), we present a novel framework to track and represent the

agentsarxiv-cs-lg

LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection

arXiv:2604.04815v2 Announce Type: replace Abstract: The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to comp

Applications

LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation

arXiv:2602.17036v2 Announce Type: replace-cross Abstract: The continuous expansion of digital learning environments has catalyzed the demand for intelligent systems capable of providing personalized e

applicationsarxiv-cs-lg

Lizard: An Efficient Linearization Framework for Large Language Models

arXiv:2507.09025v4 Announce Type: replace Abstract: We propose Lizard, a linearization framework that transforms pretrained Transformer-based Large Language Models (LLMs) into subquadratic architectur

LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning

arXiv:2506.03178v2 Announce Type: replace-cross Abstract: Automated radiology report generation holds significant potential to reduce radiologists' workload and enhance diagnostic accuracy. However, g

LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding

arXiv:2501.05067v3 Announce Type: replace Abstract: In this paper, we introduce LLaVA-Octopus, a novel video multimodal large language model. LLaVA-Octopus adaptively weights features from different v

researcharxiv-cs-cv

Agents

LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search

arXiv:2604.16555v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) aims to automatically discover high-performing deep neural network (DNN) architectures. However, conventional algorit

agentsarxiv-cs-cv

LLM as Graph Kernel: Rethinking Message Passing on Text-Rich Graphs

arXiv:2603.14937v2 Announce Type: replace-cross Abstract: Text-rich graphs, which integrate complex structural dependencies with abundant textual information, are ubiquitous yet remain challenging for

LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models

arXiv:2604.17770v1 Announce Type: new Abstract: Data scarcity remains a fundamental bottleneck in applying deep learning to wireless communication problems, particularly in scenarios where collecting

Safety

LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies

arXiv:2604.16763v1 Announce Type: new Abstract: Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goal

safetyarxiv-cs-lg

LLM Hypnosis: Exploiting User Feedback for Unauthorized Knowledge Injection to All Users

arXiv:2507.02850v3 Announce Type: replace Abstract: We describe a vulnerability in language models (LMs) trained with user feedback, whereby a single user can persistently alter LM knowledge and behav

LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains

arXiv:2604.16379v1 Announce Type: cross Abstract: Industrial B2B applications (e.g., construction site risk prediction, material procurement) face extreme data sparsity yet feature rich textual intera

LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies

arXiv:2604.16935v1 Announce Type: cross Abstract: Scarce longitudinal evidence examines LLMs' persuasiveness and humanness along time-evolving psychological frameworks. We introduce Talk2AI, a longitu

Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models

arXiv:2604.17140v1 Announce Type: cross Abstract: We present a generic algorithm for learning and approximate inference with an intuitive epistemic interpretation: iteratively focus on a subset of the

Applications

Local learning for stable backpropagation-free neural network training towards physical learning

arXiv:2603.24790v2 Announce Type: replace Abstract: While backpropagation and automatic differentiation have driven deep learning's success, the physical limits of chip manufacturing and rising enviro

applicationsarxiv-cs-lg

Locomotion of an Elastic Snake Robot via Natural Dynamics

arXiv:2604.17895v1 Announce Type: new Abstract: Nature suggests that exploiting the elasticities and natural dynamics of robotic systems could increase their locomotion efficiency. Prior work on elast

researcharxiv-cs-ro

Local Ai

LOD-Net: Locality-Aware 3D Object Detection Using Multi-Scale Transformer Network

arXiv:2604.16696v1 Announce Type: new Abstract: 3D object detection in point cloud data remains a challenging task due to the sparsity and lack of global structure inherent in the input. In this work,

local-aiarxiv-cs-cv

LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning

arXiv:2601.16504v3 Announce Type: replace Abstract: Commonsense reasoning often involves evaluating multiple plausible interpretations rather than selecting a single atomic answer, yet most benchmarks

Logical Computational Linguistics

arXiv:2604.17346v1 Announce Type: new Abstract: In this book we promote logical computational linguistics as opposed to statistical computational linguistics. In particular, we provide a logical seman

LogicDiff: Logic-Guided Denoising Improves Zero-Shot Reasoning in Masked Diffusion Language Models

arXiv:2603.26771v2 Announce Type: replace Abstract: Masked diffusion language models (MDLMs) generate text by iteratively unmasking tokens from a fully masked sequence. Their standard confidence-based

Logit Arithmetic Elicits Long Reasoning Capabilities Without Training

arXiv:2510.09354v2 Announce Type: replace Abstract: Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification. Yet, these capabi

Long-CODE: Isolating Pure Long-Context as an Orthogonal Dimension in Video Evaluation

arXiv:2604.17428v1 Announce Type: new Abstract: As video generation models achieve unprecedented capabilities, the demand for robust video evaluation metrics becomes increasingly critical. Traditional

Long-Text-to-Image Generation via Compositional Prompt Decomposition

arXiv:2604.18258v1 Announce Type: new Abstract: While modern text-to-image (T2I) models excel at generating images from intricate prompts, they struggle to capture the key details when the inputs are

LongBench: Evaluating Robotic Manipulation Policies on Real-World Long-Horizon Tasks

model-releasesarxiv-cs-ro

arXiv:2604.16788v1 Announce Type: new Abstract: Robotic manipulation policies often degrade over extended horizons, yet existing benchmarks provide limited insight into why such failures occur. Most p

Agents

LookasideVLN: Direction-Aware Aerial Vision-and-Language Navigation

arXiv:2604.17190v1 Announce Type: new Abstract: Aerial Vision-and-Language Navigation (Aerial VLN) enables unmanned aerial vehicles (UAVs) to follow natural language instructions and navigate complex

agentsarxiv-cs-cv

LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging

arXiv:2511.07129v3 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient approach for fine-tuning large language models. However, conventional LoRA adapters

LoRaQ: Optimized Low Rank Approximation for 4-bit Quantization

arXiv:2604.18117v1 Announce Type: new Abstract: Post-training quantization (PTQ) is essential for deploying large diffusion transformers on resource-constrained hardware, but aggressive 4-bit quantiza

LoReC: Rethinking Large Language Models for Graph Data Analysis

arXiv:2604.17897v1 Announce Type: new Abstract: The advent of Large Language Models (LLMs) has fundamentally reshaped the way we interact with graphs, giving rise to a new paradigm called GraphLLM. As

Tutorials

Lorentz Framework for Semantic Segmentation

arXiv:2604.16836v1 Announce Type: new Abstract: Semantic segmentation in hyperbolic space enables compact modeling of hierarchical structure while providing inherent uncertainty quantification. Prior

tutorialsarxiv-cs-cv

Low Light Image Enhancement Challenge at NTIRE 2026

arXiv:2604.17669v1 Announce Type: new Abstract: This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final result

researcharxiv-cs-cv

Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training

model-releasesarxiv-cs-lg

arXiv:2509.11983v2 Announce Type: replace Abstract: Neural network (NN) training is inherently a large-scale matrix optimization problem, yet the matrix structure of NN parameters has long been overlo

Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance

arXiv:2604.16620v1 Announce Type: new Abstract: Analysis of Stochastic Gradient Descent (SGD) and its variants typically relies on the assumption of uniformly bounded variance, a condition that freque

LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation

arXiv:2604.18490v1 Announce Type: new Abstract: Existing MT evaluation frameworks, including automatic metrics and human evaluation schemes such as Multidimensional Quality Metrics (MQM), are largely

LTRR: Learning To Rank Retrievers for LLMs

arXiv:2506.13743v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs

ltzGLUE: Luxembourgish General Language Understanding Evaluation

arXiv:2604.17976v1 Announce Type: new Abstract: This paper presents ltzGLUE, the first Natural Language Understanding (NLU) benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for En

Lumos3D: A Single-Forward Framework for Low-Light 3D Scene Restoration

arXiv:2511.09818v2 Announce Type: replace Abstract: Restoring 3D scenes with low-light conditions is challenging, and most existing methods depend on precomputed camera poses and scene-specific optimi