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MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining

arXiv:2604.24374v1 Announce Type: new Abstract: Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Repr

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MIRAGE: A Micro-Interaction Relational Architecture for Grounded Exploration in Multi-Figure Artworks

DGX agent

arXiv:2604.23788v1 Announce Type: new Abstract: Appreciating multi-figure paintings requires understanding how characters relate through subtle cues like gaze alignment, gesture, and spatial arrangeme

safetyarxiv-cs-cv

28 Apr 2026

MirrorMark: A Distortion-Free Multi-Bit Watermark for Large Language Models

arXiv:2601.22246v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become integral to applications such as question answering and content creation, reliable content attribution

Tutorials

Mitigating Error Amplification in Fast Adversarial Training

arXiv:2604.24332v1 Announce Type: new Abstract: Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant representatio

tutorialsarxiv-cs-lg

Mixture of Heterogeneous Grouped Experts for Language Modeling

arXiv:2604.23108v1 Announce Type: cross Abstract: Large Language Models (LLMs) based on Mixture-of-Experts (MoE) are pivotal in industrial applications for their ability to scale performance efficient

mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA

arXiv:2508.05318v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Mo

ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs

arXiv:2604.23053v1 Announce Type: new Abstract: Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial

applicationsarxiv-cs-lg

MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation

model-releasesarxiv-cs-lg

arXiv:2506.01897v5 Announce Type: replace Abstract: With increasing size of large language models (LLMs), full-parameter fine-tuning imposes substantial memory demands. To alleviate this, we propose a

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training

arXiv:2506.20332v4 Announce Type: replace Abstract: Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinfor

MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback

arXiv:2604.23107v1 Announce Type: cross Abstract: Causal effect estimation from observational data requires careful adjustment for confounding. Classical estimators such as inverse probability weighti

applicationsarxiv-cs-lg

Model-Free Inference of Investor Preferences: A Relative Entropy IRL Approach

arXiv:2604.24280v1 Announce Type: new Abstract: We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment acti

safetyarxiv-cs-lg

Modeling Behavioral Intensity and Transitions for Generative Recommendation

arXiv:2604.24472v1 Announce Type: cross Abstract: Multi-behavior recommendation aims to predict user conversions by modeling various interaction types that carry distinct intent signals. Recently, gen

Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion

arXiv:2604.23753v1 Announce Type: new Abstract: Multimodal affective computing analyzes user-generated social media content to predict emotional states. However, a critical gap remains in understandin

Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models

arXiv:2604.23272v1 Announce Type: new Abstract: Humans understand and interact with the real world by relying on diverse physical feedback beyond visual perception. Motivated by this, recent approache

applicationsarxiv-cs-ro

Monocular Depth Estimation via Neural Network with Learnable Algebraic Group and Ring Structures

arXiv:2604.24328v1 Announce Type: new Abstract: Monocular depth estimation (MDE) has witnessed remarkable progress driven by Convolutional Neural Networks and transformer-based architectures. However,

Tutorials

Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation

arXiv:2412.07160v3 Announce Type: replace Abstract: To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts

tutorialsarxiv-cs-cv

MotionHiFlow: Text-to-motion via hierarchical flow matching

arXiv:2604.23264v1 Announce Type: new Abstract: Text-to-motion generation aims to generate 3D human motions that are tightly aligned with the input text while remaining physically plausible and rich i

safetyarxiv-cs-cv

Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2604.23620v1 Announce Type: new Abstract: We present Move-Then-Operate, a Vision language action framework that explicitly decouples robotic manipulation into two distinct behavioral phases: coa

MTRouter: Cost-Aware Multi-Turn LLM Routing with History-Model Joint Embeddings

arXiv:2604.23530v1 Announce Type: cross Abstract: Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model inv

MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches

arXiv:2604.22881v1 Announce Type: cross Abstract: Generative recommendation (GR) offers superior modeling capabilities but suffers from prohibitive inference costs due to the repeated encoding of long

safetyarxiv-cs-ai

Multi-Dimensional Evaluation of Sustainable City Trips with LLM-as-a-Judge and Human-in-the-Loop

arXiv:2604.24158v1 Announce Type: new Abstract: Evaluating nuanced conversational travel recommendations is challenging when human annotations are costly and standard metrics ignore stakeholder-centri

Multi-Plane HyperX: A Low-Latency and Cost-Effective Network for Large-Scale AI and HPC Systems

arXiv:2604.23519v1 Announce Type: cross Abstract: Multi-plane architectures have become increasingly prevalent in the Fat-Tree networks of AI data centers. By leveraging multiple ports on a single net

Multi-Robot Motions in Milliseconds: Vector-Accelerated Primitives for Sampling-Based Planning

arXiv:2604.23960v1 Announce Type: new Abstract: In this paper, we extend the recent Vector-Accelerated Motion Planning (VAMP) framework to multi-robot motion planning (MRMP). We develop two vector-acc

researcharxiv-cs-ro

Multi-Scale Contrastive Learning for Video Temporal Grounding

arXiv:2412.07157v3 Announce Type: replace Abstract: Temporal grounding, which localizes video moments related to a natural language query, is a core problem of vision-language learning and video under

Multi-scale Dynamic Wake Modeling of Floating Offshore Wind Turbines via Fourier Neural Operators and Physics-Informed Neural Networks

arXiv:2604.23937v1 Announce Type: cross Abstract: Multi-scale dynamic wake prediction is essential for the real-time control and performance optimization of floating offshore wind turbines (FOWTs). In

Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion

arXiv:2603.26729v2 Announce Type: replace-cross Abstract: The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across th

Multi-View Synergistic Learning with Vision-Language Adaption for Low-Resource Biomedical Image Classification

model-releasesarxiv-cs-cv

arXiv:2604.23977v1 Announce Type: new Abstract: Accurate biomedical image classification under low-resource conditions remains challenging due to limited annotations, subtle inter-class visual differe

MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning

arXiv:2604.24186v1 Announce Type: cross Abstract: Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While Large Language Models (LLMs) have shown strong capab

safetyarxiv-cs-ai

Multimodal QUD: Inquisitive Questions from Scientific Figures

arXiv:2604.23733v1 Announce Type: new Abstract: Asking inquisitive questions while reading, and looking for their answers, is an important part in human discourse comprehension, curiosity, and creativ

researcharxiv-cs-cl

Multimodal Remote Inference

arXiv:2508.07555v3 Announce Type: replace Abstract: We consider a remote inference system with multiple modalities, where a multimodal machine learning (ML) model performs real-time inference using fe

safetyarxiv-cs-lg

Multispectral airborne laser scanning dataset for tree species classification: MS-ALS-SPECIES

arXiv:2604.24370v1 Announce Type: new Abstract: The shift from stand-level to individual-tree-level forest assessments supports improved biodiversity mapping, particularly in boreal ecosystems where t

Multivariate Gaussian NeRF for Wide Field-of-View Ultrasound Reconstruction

arXiv:2604.24187v1 Announce Type: new Abstract: Wide Field-of-View (WFoV) reconstruction enhances 3D ultrasound imaging by providing valuable anatomical context for segmentation models and visualizati

MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples

arXiv:2511.10047v2 Announce Type: replace Abstract: Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this p

Muscle Coactivation in the Sky: Geometry and Pareto Optimality of Energy vs. Aerodynamic Promptness and Multirotors as Variable Stiffness Actuators

arXiv:2602.14222v2 Announce Type: replace Abstract: In robotics and biomechanics, trading metabolic cost for kinematic readiness is a well-established principle. This paper formalizes this concept for

researcharxiv-cs-ro

MUSIC: Learning Muscle-Driven Dexterous Hand Control

arXiv:2604.23886v1 Announce Type: cross Abstract: We present a data-driven approach for physics-based, muscle-driven dexterous control that enables musculoskeletal hands to perform precise piano playi

MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation

model-releasesarxiv-cs-cv

arXiv:2604.23789v1 Announce Type: new Abstract: While video foundation models excel at single-shot generation, real-world cinematic storytelling inherently relies on complex multi-shot sequencing. Fur

MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender

arXiv:2408.08686v4 Announce Type: replace-cross Abstract: Language Models (LMs) have been widely used in recommender systems to incorporate textual information of items into item IDs, leveraging their

applicationsarxiv-cs-ai

Nearly Optimal Subdata Selection

model-releasesarxiv-cs-lg

arXiv:2604.23930v1 Announce Type: cross Abstract: When, in terms of the number of data points, the size of a dataset exceeds available computing resources, or when labeling is expensive, an attractive

Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks

arXiv:2604.23765v1 Announce Type: new Abstract: We analyze the universal approximation property of Kolmogorov-Arnold Networks (KANs) in terms of their edge functions. If these functions are all affine

Agents

NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning

arXiv:2601.03790v3 Announce Type: replace-cross Abstract: Neologism-aware machine translation aims to translate source sentences containing neologisms into target languages. This field remains underex

agentsarxiv-cs-ai

NeSyCat: A Monad-Based Categorical Semantics of the Neurosymbolic ULLER Framework

arXiv:2604.24612v1 Announce Type: new Abstract: ULLER (Unified Language for LEarning and Reasoning) offers a unified first-order logic (FOL) syntax, enabling its knowledge bases to be used directly ac

Neural Bridge Processes

arXiv:2508.07220v2 Announce Type: replace-cross Abstract: Learning stochastic functions from partially observed context-target pairs requires models that are expressive, uncertainty-aware, and strongl

safetyarxiv-cs-ai

Neural Grammatical Error Correction for Romanian

arXiv:2604.23627v1 Announce Type: new Abstract: Resources for Grammatical Error Correction (GEC) in non-English languages are scarce, while available spellcheckers in these languages are mostly limite

researcharxiv-cs-cl

Neural Network Optimization Reimagined: Decoupled Techniques for Scratch and Fine-Tuning

arXiv:2604.22838v1 Announce Type: cross Abstract: With the accumulation of resources in the era of big data and the rise of pre-trained models in deep learning, optimizing neural networks for various

Hardware

NeuroAPS-Net: Neuro-Anatomically Aware Point Cloud Representation for Efficient Alzheimer's Disease Classification

arXiv:2604.22883v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a major cause of dementia. Structural MRI is widely used to analyze AD-relate

hardwarearxiv-cs-ai

NeuroClaw Technical Report

model-releasesarxiv-cs-cv

arXiv:2604.24696v1 Announce Type: new Abstract: Agentic artificial intelligence systems promise to accelerate scientific workflows, but neuroimaging poses unique challenges: heterogeneous modalities (

New non-Euclidean neural quantum states from additional types of hyperbolic recurrent neural networks

arXiv:2604.24337v1 Announce Type: cross Abstract: In this work, we extend the class of previously introduced non-Euclidean neural quantum states (NQS) which consists only of Poincare hyperbolic GRU, t

No Test Cases, No Problem: Distillation-Driven Code Generation for Scientific Workflows

arXiv:2604.23106v1 Announce Type: cross Abstract: Existing multi-agent Large Language Model (LLM) frameworks for code generation typically use execution feedback and improve iteratively using Input/Ou