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arXiv:2604.18460v1 Announce Type: new Abstract: Multimodal affective computing aims to predict humans' sentiment, emotion, intention, and opinion using language, acoustic, and visual modalities. Howev

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Learning Stable Predictors from Weak Supervision under Distribution Shift

DGX agent

arXiv:2604.05002v2 Announce Type: replace Abstract: Learning from weak, proxy, or relative supervision is common when ground-truth labels are unavailable, but robustness under distribution shift remai

model-releasesarxiv-cs-lg

21 Apr 2026

Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection

arXiv:2506.00955v2 Announce Type: replace Abstract: Sarcasm fundamentally alters meaning through tone and context, yet detecting it in speech remains a challenge due to data scarcity. In addition, exi

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

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: 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

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

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 are still not consistent judges of qualitative work, and small changes to how that work is presented affect outcomes. Better harnessing…

model-releasesethan-mollick--x

LLMs are still not consistent judges of qualitative work, and small changes to how that work is presented affect outcomes. Better harnessing and methods (multiple judging runs with randomized orders,

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-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

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

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

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

Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting

arXiv:2512.00198v2 Announce Type: replace Abstract: Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammog

safetyarxiv-cs-cv

MARA: A Multimodal Adaptive Retrieval-Augmented Framework for Document Question Answering

arXiv:2604.16313v1 Announce Type: cross Abstract: Retrieval-based multimodal document QA aims to identify and integrate relevant information from visually rich documents with complex multimodal struct

MARCO: Navigating the Unseen Space of Semantic Correspondence

arXiv:2604.18267v1 Announce Type: new Abstract: Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion backbones. While accurate, these billion-

Agents

MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation

arXiv:2604.18509v1 Announce Type: new Abstract: Large language models (LLMs) are widely used in retrieval-augmented generation (RAG) to incorporate external knowledge at inference time. However, when

agentsarxiv-cs-cl

MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems

arXiv:2503.16549v2 Announce Type: replace Abstract: Despite strong results on many tasks, multimodal large language models (MLLMs) still underperform on visual mathematical problem solving, especially

Measuring Representation Robustness in Large Language Models for Geometry

arXiv:2604.16421v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical reasoning, yet their robustness to equivalent problem representations remains po

Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos

arXiv:2601.06931v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed in socially consequential settings, raising concerns about social bias driven by demog

MEDN: Motion-Emotion Feature Decoupling Network for Micro-Expression Recognition

arXiv:2604.17899v1 Announce Type: new Abstract: Unlike macro-expression, micro-expression does not follow a strictly consistent mapping rule between emotions and Action Units (AUs). As a result, some

MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards

arXiv:2601.05488v3 Announce Type: replace Abstract: Maintaining consistency in long-term dialogues remains a fundamental challenge for LLMs, as standard retrieval mechanisms often fail to capture the

Agents

Memory Centric Power Allocation for Multi-Agent Embodied Question Answering

arXiv:2604.17810v1 Announce Type: new Abstract: This paper considers multi-agent embodied question answering (MA-EQA), which aims to query robot teams on what they have seen over a long horizon. In co

agentsarxiv-cs-ro

mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval

arXiv:2604.17054v1 Announce Type: new Abstract: Scalable Vector Graphics (SVGs) function both as visual images and as structured code that encode rich geometric and layout information, yet most method

MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models

arXiv:2604.17730v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored as scalable tools for mental health counseling, yet evaluating their safety remains challenging d

safetyarxiv-cs-cl

Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis

arXiv:2602.16144v3 Announce Type: replace Abstract: As multimodal systems increasingly process sensitive personal data, the ability to selectively revoke specific data modalities has become a critical

Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clustering

arXiv:2512.21510v2 Announce Type: replace-cross Abstract: Real-world multi-view data often exhibit highly inconsistent missing patterns, posing significant challenges for incomplete multi-view cluster

Mitigating Multimodal Hallucination via Phase-wise Self-reward

arXiv:2604.17982v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) still struggle with vision hallucination, where generated responses are inconsistent with the visual input. Exist

MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detection

arXiv:2603.23868v2 Announce Type: replace Abstract: In this paper, we address the challenging problem of single-scene, fully unsupervised video anomaly detection (VAD), where raw videos containing bot

researcharxiv-cs-cv

MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation

arXiv:2604.16943v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown impressive capabilities, yet they often struggle to effectively capture the fine-grained textual inf

MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment

arXiv:2604.17007v1 Announce Type: new Abstract: Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present

MoCo: A One-Stop Shop for Model Collaboration Research

arXiv:2601.21257v2 Announce Type: replace Abstract: Advancing beyond single monolithic language models (LMs), recent research increasingly recognizes the importance of model collaboration, where multi

safetyarxiv-cs-cl

Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosis

model-releasesarxiv-cs-lg

arXiv:2604.17713v1 Announce Type: new Abstract: Resting-state functional magnetic resonance imaging (fMRI) has emerged as a cornerstone for psychiatric diagnosis, yet most approaches rely on pairwise

Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding

arXiv:2604.17174v1 Announce Type: new Abstract: Modeling human cognitive states is essential for advanced artificial intelligence. Existing Large Language Models (LLMs) mainly address isolated tasks s

Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion Sampling

model-releasesarxiv-cs-lg

arXiv:2604.16461v1 Announce Type: cross Abstract: Physics-guided sampling with diffusion priors has recently shown strong performance in solving complex systems of partial differential equations (PDEs

MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression

arXiv:2604.17695v1 Announce Type: cross Abstract: KV cache memory is the dominant bottleneck for long-context LLM inference. Existing compression methods each act on a single axis of the four-dimensio

More Than Meets the Eye: Measuring the Semiotic Gap in Vision-Language Models via Semantic Anchorage

arXiv:2604.17354v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at photorealistic generation, yet often struggle to represent abstract meaning such as idiomatic interpretations of

Motion-Guided Semantic Alignment with Negative Prompts for Zero-Shot Video Action Recognition

arXiv:2604.17062v1 Announce Type: new Abstract: Zero-shot action recognition is challenging due to the semantic gap between seen and unseen classes. We present a novel framework that enhances CLIP wit

safetyarxiv-cs-cv

Multi-Camera Self-Calibration in Sports Motion Capture: Leveraging Human and Stick Poses

arXiv:2604.17567v1 Announce Type: new Abstract: Multi-camera systems are widely employed in sports to capture the 3D motion of athletes and equipment, yet calibrating their extrinsic parameters remain

Local Ai

Multi-View Hierarchical Graph Neural Network for Sketch-Based 3D Shape Retrieval

arXiv:2604.18019v1 Announce Type: new Abstract: Sketch-based 3D shape retrieval (SBSR) aims to retrieve 3D shapes that are consistent with the category of the input hand-drawn sketch. The core challen

local-aiarxiv-cs-cv

Multilevel neural networks with dual-stage feature fusion for human activity recognition

arXiv:2604.16577v1 Announce Type: new Abstract: Human activity recognition (HAR) refers to the process of identifying human actions and activities using data collected from sensors. Neural networks, s

Multimodal Claim Extraction for Fact-Checking

arXiv:2604.16311v1 Announce Type: new Abstract: Automated Fact-Checking (AFC) relies on claim extraction as a first step, yet existing methods largely overlook the multimodal nature of today's misinfo

Local Ai

NCO4CVRP: Neural Combinatorial Optimization for the Capacitated Vehicle Routing Problem

arXiv:2604.16581v1 Announce Type: new Abstract: Neural Combinatorial Optimization (NCO) has emerged as a powerful framework for solving combinatorial optimization problems by integrating deep learning

local-aiarxiv-cs-lg

Negative Advantage Is a Double-Edged Sword: Calibrating Advantage in GRPO for Deep Search

arXiv:2604.18235v1 Announce Type: new Abstract: Deep search agents can autonomously initiate multi-turn interactions with search engines, thereby exhibiting strong question-answering capabilities. Suc

safetyarxiv-cs-cl

Hardware

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

arXiv:2510.08726v2 Announce Type: replace-cross Abstract: Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and red

hardwarearxiv-cs-lg

Neuro-Symbolic Resolution of Recommendation Conflicts in Multimorbidity Clinical Guidelines

arXiv:2604.17340v1 Announce Type: new Abstract: Clinical guidelines, typically developed by independent specialty societies, inherently exhibit substantial fragmentation, redundancy, and logical contr