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KG-ViP: Bridging Knowledge Grounding and Visual Perception in Multi-modal LLMs for Visual Question Answering

arXiv:2601.11632v2 Announce Type: replace Abstract: Multi-modal Large Language Models (MLLMs) for Visual Question Answering (VQA) often suffer from dual limitations: knowledge hallucination and insuff

researcharxiv-cs-cv

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Human DGX agent

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Safety

Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics

DGX agent

arXiv:2601.17647v2 Announce Type: replace-cross Abstract: Quantifying the causal relationship between sea ice thickness and sea surface height (SSH) is essential for understanding the mechanisms drivi

safetyarxiv-cs-ai

22 Apr 2026

Large Language Models Exhibit Normative Conformity

arXiv:2604.19301v1 Announce Type: new Abstract: The conformity bias exhibited by large language models (LLMs) can pose a significant challenge to decision-making in LLM-based multi-agent systems (LLM-

LASER: Learning Active Sensing for Continuum Field Reconstruction

arXiv:2604.19355v1 Announce Type: cross Abstract: High-fidelity measurements of continuum physical fields are essential for scientific discovery and engineering design but remain challenging under spa

Tutorials

Latent Linear Quadratic Regulator for Robotic Control Tasks

arXiv:2407.11107v3 Announce Type: replace-cross Abstract: Model predictive control (MPC) has played a more crucial role in various robotic control tasks, but its high computational requirements are co

tutorialsarxiv-cs-lg

LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data

model-releasesarxiv-cs-lg

arXiv:2505.09803v3 Announce Type: replace-cross Abstract: In many applications, we wish to fit a parametric statistical model to a small ensemble of spatially distributed random variables ('fields').

Hardware

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation

arXiv:2604.19167v1 Announce Type: cross Abstract: Deploying large language models (LLMs) in resource-constrained environments is hindered by heavy computational and memory requirements. We present LBL

hardwarearxiv-cs-ai

Learning Evolution via Optimization Knowledge Adaptation

arXiv:2501.02200v2 Announce Type: replace-cross Abstract: The iterative search process of evolutionary algorithms (EAs) encapsulates optimization knowledge within historical populations and fitness ev

researcharxiv-cs-ai

Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty

arXiv:2604.19677v1 Announce Type: cross Abstract: Reinforcement learning-based control policies have been frequently demonstrated to be more effective than analytical techniques for many manipulation

Tutorials

Learning Lifted Action Models from Unsupervised Visual Traces

arXiv:2604.19043v1 Announce Type: new Abstract: Efficient construction of models capturing the preconditions and effects of actions is essential for applying AI planning in real-world domains. Extensi

tutorialsarxiv-cs-ai

Applications

Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors

arXiv:2604.19028v1 Announce Type: new Abstract: One of the most challenging problems in graph machine learning is generalizing across graphs with diverse properties. Graph neural networks (GNNs) face

applicationsarxiv-cs-lg

Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation

arXiv:2604.19234v1 Announce Type: new Abstract: Reinforcement learning, particularly Group Relative Policy Optimization (GRPO), has emerged as an effective framework for post-training visual generativ

safetyarxiv-cs-cv

LegalBench-BR: A Benchmark for Evaluating Large Language Models on Brazilian Legal Decision Classification

arXiv:2604.18878v1 Announce Type: new Abstract: We introduce LegalBench-BR, the first public benchmark for evaluating language models on Brazilian legal text classification. The dataset comprises 3,10

LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues

model-releasesarxiv-cs-ai

arXiv:2604.19464v1 Announce Type: cross Abstract: More than half of the global population struggles to meet their civil justice needs due to limited legal resources. While Large Language Models (LLMs)

Less Is More: Cognitive Load and the Single-Prompt Ceiling in LLM Mathematical Reasoning

arXiv:2604.18897v1 Announce Type: new Abstract: We present a systematic empirical study of prompt engineering for formal mathematical reasoning in the context of the SAIR Equational Theories Stage 1 c

Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2601.22737v2 Announce Type: replace Abstract: The robust safety of Vision-Language Large Models (VLLMs) against joint multilingual and multimodal threats remains severely underexplored. Current

LiveVLN: Breaking the Stop-and-Go Loop in Vision-Language Navigation

model-releasesarxiv-cs-ro

arXiv:2604.19536v1 Announce Type: new Abstract: Recent navigation systems achieve strong benchmark results, yet real-world deployment often remains visibly stop-and-go. This bottleneck arises because

LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models

model-releasesarxiv-cs-ai

arXiv:2604.18803v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly deployed in settings where reliable visual grounding carries operational consequences, yet their behavi

LLMs Know They're Wrong and Agree Anyway: The Shared Sycophancy-Lying Circuit

arXiv:2604.19117v1 Announce Type: new Abstract: When a language model agrees with a user's false belief, is it failing to detect the error, or noticing and agreeing anyway? We show the latter. Across

safetyarxiv-cs-lg

Local Ai

Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control

arXiv:2604.19018v1 Announce Type: cross Abstract: Inference-time LLM alignment methods, particularly activation steering, offer an alternative to fine-tuning by directly modifying activations during g

local-aiarxiv-cs-ai

Applications

Local Updates in Distributed Optimization: Provable Acceleration and Topology Effects

arXiv:2601.03442v4 Announce Type: replace-cross Abstract: Inspired by the success of performing multiple local optimization steps between communication rounds in federated learning, incorporating such

applicationsarxiv-cs-lg

Local Ai

Localization-Guided Foreground Augmentation in Autonomous Driving

arXiv:2604.18940v1 Announce Type: new Abstract: Autonomous driving systems often degrade under adverse visibility conditions-such as rain, nighttime, or snow-where online scene geometry (e.g., lane di

local-aiarxiv-cs-cv

Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs

arXiv:2604.19292v1 Announce Type: cross Abstract: Multilingual large language models (LLMs) have minimized the fluency gap between languages. This advancement, however, exposes models to the risk of b

LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval

arXiv:2604.18913v1 Announce Type: new Abstract: Knowledge graphs (KGs) are increasingly integrated with large language models (LLMs) to provide structured, verifiable reasoning. A core operation in th

safetyarxiv-cs-cl

Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models

arXiv:2601.14152v2 Announce Type: replace-cross Abstract: Large language models exhibit surprising sensitivity to the structure of the prompt, but the mechanisms underlying this sensitivity remain poo

researcharxiv-cs-ai

Lost in Translation: Do LVLM Judges Generalize Across Languages?

arXiv:2604.19405v1 Announce Type: new Abstract: Automatic evaluators such as reward models play a central role in the alignment and evaluation of large vision-language models (LVLMs). Despite their gr

LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results

model-releasesarxiv-cs-cv

arXiv:2604.19445v1 Announce Type: new Abstract: This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world a

Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning

arXiv:2604.18978v1 Announce Type: cross Abstract: Scaling critic capacity is a promising direction for enhancing off-policy reinforcement learning (RL). However, larger critics are prone to overfittin

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization

arXiv:2506.09373v3 Announce Type: replace-cross Abstract: The advent of autonomous agents is transforming interactions with Graphical User Interfaces (GUIs) by employing natural language as a powerful

LSTM-MAS: A Long Short-Term Memory Inspired Multi-Agent System for Long-Context Understanding

model-releasesarxiv-cs-ai

arXiv:2601.11913v2 Announce Type: replace-cross Abstract: Effectively processing long contexts remains a fundamental yet unsolved challenge for large language models (LLMs). Existing single-LLM-based

Lyapunov-Certified Direct Switching Theory for Q-Learning

arXiv:2604.19569v1 Announce Type: cross Abstract: Q-learning is one of the most fundamental algorithms in reinforcement learning. We analyze constant-stepsize Q-learning through a direct stochastic sw

M^{2}GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuit

arXiv:2604.19404v1 Announce Type: cross Abstract: Traditional policy learning methods in cooperative pursuit face fundamental challenges in biomimetic underwater robots, where long-horizon decision ma

Machine individuality: Separating genuine idiosyncrasy from response bias in large language models

arXiv:2604.16755v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly integrated into daily life, in roles ranging from high-stakes decision support to companionship, un

MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments

arXiv:2511.04320v2 Announce Type: replace Abstract: Autonomous navigation in unknown environments requires multi-scale spatial understanding that captures geometric details, topological connectivity,

safetyarxiv-cs-ro

MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping

arXiv:2603.22650v2 Announce Type: replace Abstract: Active mapping aims to determine how an agent should move to efficiently reconstruct unknown environments. Most existing approaches rely on greedy n

agentsarxiv-cs-cv

Mango: Multi-Agent Web Navigation via Global-View Optimization

arXiv:2604.18779v1 Announce Type: new Abstract: Existing web agents typically initiate exploration from the root URL, which is inefficient for complex websites with deep hierarchical structures. Witho

Tutorials

MapPFN: Learning Causal Perturbation Maps in Context

arXiv:2601.21092v2 Announce Type: replace Abstract: Planning effective interventions in biological systems requires treatment-effect models that adapt to unseen biological contexts by identifying thei

tutorialsarxiv-cs-lg

Mask World Model: Predicting What Matters for Robust Robot Policy Learning

arXiv:2604.19683v1 Announce Type: new Abstract: World models derived from large-scale video generative pre-training have emerged as a promising paradigm for generalist robot policy learning. However,

safetyarxiv-cs-ro

MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering

arXiv:2602.09642v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have significantly improved table understanding tasks such as Table Question Answering (TableQ

Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras

arXiv:2604.18744v1 Announce Type: new Abstract: Event cameras have recently shown promising capabilities in instantaneous motion estimation due to their robustness to low light and fast motions. Howev

researcharxiv-cs-cv

Mechanistic Anomaly Detection via Functional Attribution

model-releasesarxiv-cs-lg

arXiv:2604.18970v1 Announce Type: new Abstract: We can often verify the correctness of neural network outputs using ground truth labels, but we cannot reliably determine whether the output was produce

MedFlowSeg: Flow Matching for Medical Image Segmentation with Frequency-Aware Attention

arXiv:2604.19675v1 Announce Type: new Abstract: Flow matching has recently emerged as a principled framework for learning continuous-time transport maps, enabling efficient deterministic generation wi

researcharxiv-cs-cv

Memory Assignment for Finite-Memory Strategies in Adversarial Patrolling Games

arXiv:2505.14137v2 Announce Type: replace Abstract: Adversarial Patrolling games form a subclass of Security games where a Defender moves between locations, guarding vulnerable targets. The main algor

researcharxiv-cs-ai

Local Ai

Memory Over Maps: 3D Object Localization Without Reconstruction

arXiv:2603.20530v2 Announce Type: replace-cross Abstract: Target localization is a prerequisite for embodied tasks such as navigation and manipulation. Conventional approaches rely on constructing exp

local-aiarxiv-cs-cv

Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems

arXiv:2604.19540v1 Announce Type: cross Abstract: Teams of LLM agents increasingly collaborate on tasks spanning days or weeks: multi-day data-generation sprints where generator, reviewer, and auditor

Micro Language Models Enable Instant Responses

arXiv:2604.19642v1 Announce Type: new Abstract: Edge devices such as smartwatches and smart glasses cannot continuously run even the smallest 100M-1B parameter language models due to power and compute

Mind the (DH) Gap! A Contrast in Risky Choices Between Reasoning and Conversational LLMs

arXiv:2602.15173v2 Announce Type: replace Abstract: The use of large language models either as decision support systems, or in agentic workflows, is rapidly transforming the digital ecosystem. However

Mind the Unseen Mass: Unmasking LLM Hallucinations via Soft-Hybrid Alphabet Estimation

arXiv:2604.19162v1 Announce Type: new Abstract: This paper studies uncertainty quantification for large language models (LLMs) under black-box access, where only a small number of responses can be sam

researcharxiv-cs-cl