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LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments

arXiv:2604.10834v1 Announce Type: cross Abstract: [Background:] Thematic analysis of free-text justifications in human experiments provides significant qualitative insights. Yet, it is costly because

researcharxiv-cs-ai

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LLMs for Text-Based Exploration and Navigation Under Partial Observability

DGX agent

arXiv:2604.09604v1 Announce Type: new Abstract: Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language m

model-releasesarxiv-cs-ai

14 Apr 2026

LLMs Should Incorporate Explicit Mechanisms for Human Empathy

arXiv:2604.10557v1 Announce Type: cross Abstract: This paper argues that Large Language Models (LLMs) should incorporate explicit mechanisms for human empathy. As LLMs become increasingly deployed in

LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation

arXiv:2604.11789v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks req

researcharxiv-cs-cv

Locket: Robust Feature-Locking Technique for Language Models

arXiv:2510.12117v3 Announce Type: replace-cross Abstract: Chatbot service providers (e.g., OpenAI) rely on tiered subscription plans to generate revenue, offering black-box access to basic models for

researcharxiv-cs-lg

LoDAdaC: a unified local training-based decentralized framework with adaptive gradients and compressed communication

arXiv:2604.09970v1 Announce Type: new Abstract: In the decentralized distributed learning, achieving fast convergence and low communication cost is essential for scalability and high efficiency. Adapt

local-aiarxiv-cs-lg

LogitDynamics: Reliable ViT Error Detection from Layerwise Logit Trajectories

arXiv:2604.10643v1 Announce Type: new Abstract: Reliable confidence estimation is critical when deploying vision models. We study error prediction: determining whether an image classifier's output is

researcharxiv-cs-cv

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling

arXiv:2604.11348v1 Announce Type: new Abstract: Efficient and explainable breast cancer (BC) risk prediction is critical for large-scale population-based screening. Breast MRI provides functional info

LOLGORITHM: Funny Comment Generation Agent For Short Videos

arXiv:2604.09729v1 Announce Type: cross Abstract: Short-form video platforms have become central to multimedia information dissemination, where comments play a critical role in driving engagement, pro

agentsarxiv-cs-ai

Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation

arXiv:2604.10103v1 Announce Type: new Abstract: Streaming video generation (SVG) distills a pretrained bidirectional video diffusion model into an autoregressive model equipped with sliding window att

local-aiarxiv-cs-cv

Look Twice before You Leap: A Rational Framework for Localized Adversarial Anonymization

arXiv:2512.06713v3 Announce Type: replace-cross Abstract: Current LLM-based frameworks for text anonymization usually rely on remote API services from powerful LLMs, which creates an inherent privacy

local-aiarxiv-cs-cl

LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval

arXiv:2601.14706v3 Announce Type: replace Abstract: In this paper, we present LookBench (We use the term 'look' to reflect retrieval that mirrors how people shop -- finding the exact item, a close sub

LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformers

arXiv:2504.14386v2 Announce Type: replace-cross Abstract: Positional embeddings (PE) play a crucial role in Vision Transformers (ViTs) by providing spatial information otherwise lost due to the permut

researcharxiv-cs-ai

LoopGuard: Breaking Self-Reinforcing Attention Loops via Dynamic KV Cache Intervention

arXiv:2604.10044v1 Announce Type: new Abstract: Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition

Lost in Diffusion: Uncovering Hallucination Patterns and Failure Modes in Diffusion Large Language Models

arXiv:2604.10556v1 Announce Type: new Abstract: While Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive paradigm comparable to autoregressive (AR) models, their fa

researcharxiv-cs-cl

LottieGPT: Tokenizing Vector Animation for Autoregressive Generation

arXiv:2604.11792v1 Announce Type: new Abstract: Despite rapid progress in video generation, existing models are incapable of producing vector animation, a dominant and highly expressive form of multim

LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment: Methods and Results

arXiv:2604.11207v1 Announce Type: new Abstract: This paper reviews the LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment. This challenge aims to raise a new direction, i.e., how

Applications

LoViF 2026 The First Challenge on Weather Removal in Videos

arXiv:2604.10655v1 Announce Type: cross Abstract: This paper presents a review of the LoViF 2026 Challenge on Weather Removal in Videos. The challenge encourages the development of methods for restori

applicationsarxiv-cs-ai

Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration

arXiv:2604.11446v1 Announce Type: cross Abstract: Recently, scaling reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs) has emerged as an effective training paradigm

LPNSR: Optimal Noise-Guided Diffusion Image Super-Resolution Via Learnable Noise Prediction

arXiv:2603.21045v4 Announce Type: replace-cross Abstract: Diffusion-based image super-resolution (SR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) observations, yet faces a

LRD-Net: A Lightweight Real-Centered Detection Network for Cross-Domain Face Forgery Detection

arXiv:2604.10862v1 Announce Type: new Abstract: The rapid advancement of diffusion-based generative models has made face forgery detection a critical challenge in digital forensics. Current detection

LumiMotion: Improving Gaussian Relighting with Scene Dynamics

arXiv:2604.10994v1 Announce Type: new Abstract: In 3D reconstruction, the problem of inverse rendering, namely recovering the illumination of the scene and the material properties, is fundamental. Exi

Lung Cancer Detection Using Deep Learning

arXiv:2604.10765v1 Announce Type: cross Abstract: Lung cancer, the second leading cause of cancer-related deaths, is primarily linked to long-term tobacco smoking (85% of cases). Surprisingly, 10-15%

researcharxiv-cs-ai

LVSum: A Benchmark for Timestamp-Aware Long Video Summarization

arXiv:2604.10024v1 Announce Type: cross Abstract: Long video summarization presents significant challenges for current multimodal large language models (MLLMs), particularly in maintaining temporal fi

M2-Verify: A Large-Scale Multidomain Benchmark for Checking Multimodal Claim Consistency

model-releasesarxiv-cs-cl

arXiv:2604.01306v2 Announce Type: replace Abstract: Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence. However, existing

M^{2}SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

arXiv:2303.10894v3 Announce Type: replace Abstract: Accurate medical image segmentation is critical for early medical diagnosis. Most existing methods are based on U-shape structure and use element-wi

researcharxiv-cs-cv

M^3KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

arXiv:2512.20136v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has recently been extended to multimodal settings, connecting multimodal large language models (MLLMs) wi

agentsarxiv-cs-ai

Machine Learning-Based Detection of MCP Attacks

arXiv:2604.10534v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) is a new and emerging technology that extends the functionality of large language models, improving workflows but als

agentsarxiv-cs-ai

Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems

arXiv:2604.11513v1 Announce Type: cross Abstract: We review recent advances in machine-learning (ML) force-field methods for large-scale Landau-Lifshitz-Gilbert (LLG) simulations of metallic spin syst

local-aiarxiv-cs-lg

MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments

arXiv:2604.11131v1 Announce Type: new Abstract: Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans mak

MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization

arXiv:2601.07208v2 Announce Type: replace-cross Abstract: Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is

safetyarxiv-cs-cl

MAFIG: Multi-agent Driven Formal Instruction Generation Framework

arXiv:2604.10989v1 Announce Type: new Abstract: Emergency situations in scheduling systems often trigger local functional failures that undermine system stability and even cause system collapse. Exist

local-aiarxiv-cs-ai

MapATM: Enhancing HD Map Construction through Actor Trajectory Modeling

arXiv:2604.11081v1 Announce Type: new Abstract: High-definition (HD) mapping tasks, which perform lane detections and predictions, are extremely challenging due to non-ideal conditions such as view oc

agentsarxiv-cs-cv

MARLIN: Multi-Agent Reinforcement Learning Guided by Language-Based Inter-Robot Negotiation

arXiv:2410.14383v4 Announce Type: replace Abstract: Multi-agent reinforcement learning is a key method for training multi-robot systems. Through rewarding or punishing robots over a series of episodes

safetyarxiv-cs-ro

MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification

arXiv:2601.15498v2 Announce Type: replace Abstract: Speculative Decoding (SD) accelerates autoregressive large language model (LLM) inference by decoupling generation and verification. While recent me

local-aiarxiv-cs-lg

MASH: Modeling Abstention via Selective Help-Seeking

arXiv:2510.01152v2 Announce Type: replace Abstract: LLMs cannot reliably recognize their parametric knowledge boundaries and often hallucinate answers to outside-of-boundary questions. In this paper,

agentsarxiv-cs-cl

Masked Contrastive Pre-Training Improves Music Audio Key Detection

arXiv:2604.10021v1 Announce Type: cross Abstract: Self-supervised music foundation models underperform on key detection, which requires pitch-sensitive representations. In this work, we present the fi

researcharxiv-cs-lg

Applications

Masked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images

arXiv:2508.09165v3 Announce Type: replace-cross Abstract: Background: Electrocardiograms are indispensable for diagnosing cardiovascular diseases, yet in many settings they exist only as paper printou

applicationsarxiv-cs-cv

MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models

arXiv:2511.18373v2 Announce Type: replace Abstract: Vision Language Models (VLMs) perform well on standard video tasks but struggle with physics-related reasoning involving motion dynamics and spatial

MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis

arXiv:2604.11188v1 Announce Type: cross Abstract: Synthesizing high-quality mathematical reasoning data without human priors remains a significant challenge. Current approaches typically rely on seed

MatRes: Zero-Shot Test-Time Model Adaptation for Simultaneous Matching and Restoration

arXiv:2604.10081v1 Announce Type: cross Abstract: Real-world image pairs often exhibit both severe degradations and large viewpoint changes, making image restoration and geometric matching mutually in

MAVEN-T: Multi-Agent enVironment-aware Enhanced Neural Trajectory predictor with Reinforcement Learning

arXiv:2604.10169v1 Announce Type: new Abstract: Trajectory prediction remains a critical yet challenging component in autonomous driving systems, requiring sophisticated reasoning capabilities while m

Maximum Entropy Relaxation of Multi-Way Cardinality Constraints for Synthetic Population Generation

arXiv:2603.22558v2 Announce Type: replace Abstract: Generating synthetic populations from aggregate statistics is a core component of microsimulation, agent-based modeling, policy analysis, and privac

MCAT: Scaling Many-to-Many Speech-to-Text Translation with MLLMs to 70 Languages

model-releasesarxiv-cs-cl

arXiv:2512.01512v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved great success in Speech-to-Text Translation (S2TT) tasks. However, current research is constr

MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval

arXiv:2604.09552v1 Announce Type: cross Abstract: Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retri

MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus

arXiv:2601.09270v3 Announce Type: replace Abstract: With the rapid advancement of Multimodal Large Language Models (MLLMs), their potential has gained significant attention in Chinese Classical Studie

researcharxiv-cs-cl

MDP Planning as Policy Inference

arXiv:2602.17375v2 Announce Type: replace Abstract: We cast episodic Markov decision process (MDP) planning as Bayesian inference over policies. A policy is treated as the latent variable and is assig

safetyarxiv-cs-lg

Measuring and curing reasoning rigidity: from decorative chain-of-thought to genuine faithfulness

arXiv:2603.22816v3 Announce Type: replace-cross Abstract: Language models increasingly show their work by writing step-by-step reasoning before answering. But are these steps genuinely used, or is the