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MacArena: Benchmarking Computer Use Agents on an Online macOS Environment

arXiv:2606.06560v1 Announce Type: cross Abstract: Computer-use agents (CUAs) operate graphical user interfaces (GUIs) through vision and control primitives, and their capabilities have advanced rapidl

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MACD: Model-Aware Contrastive Decoding via Counterfactual Data

DGX agent

arXiv:2602.01740v3 Announce Type: replace Abstract: Video language models (Video-LLMs) are prone to hallucinations, generating plausible but ungrounded content when visual evidence is weak, ambiguous,

model-releasesarxiv-cs-ai

8 Jun 2026

Machine Learning for Electron-Scale Turbulence Modeling in W7-X

arXiv:2511.04567v2 Announce Type: replace-cross Abstract: Constructing reduced models for turbulent transport is essential for accelerating profile predictions and enabling many-query tasks such as pa

MADE: Beyond Scoring via a Multilingual Agentic Diagnosing Engine for Fine-Grained Evaluation Insights

model-releasesarxiv-cs-cl

arXiv:2606.07020v1 Announce Type: new Abstract: Multilingual and multicultural benchmarks now cover dozens of languages and model families, but the resulting score landscapes remain metric-rich and in

MADRAG: Multi-Agent Debate with Retrieval-Augmented Generation for Training-Free Analytic Essay Scoring

arXiv:2606.06754v1 Announce Type: cross Abstract: We present MADRAG, a training-free framework for analytic essay scoring that combines multi-agent reasoning with retrieval-augmented grounding. Unlike

safetyarxiv-cs-cl

MAGE: All-[MASK] Block Already Knows Where to Look in Block Diffusion LLM

arXiv:2602.14209v2 Announce Type: replace-cross Abstract: Block diffusion LLMs are an emerging paradigm for parallel language generation, but their KV caching makes memory access the dominant bottlene

Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation

arXiv:2606.07387v1 Announce Type: new Abstract: State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle

MalTree: Tracing Malware Evolution from Embeddings at Scale

arXiv:2606.06570v1 Announce Type: cross Abstract: Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary rel

applicationsarxiv-cs-ai

MatterDoor: Sampling Zero-shot Spatio-semantic Priors using Generative Models

arXiv:2510.11014v2 Announce Type: replace-cross Abstract: Autonomous robots often view rooms only partially, through a doorway, where the walls and scene structure hide the geometry and task-relevant

Meaning in Order, Order in Meaning: Semantic R-precision for Keyphrase Evaluation

arXiv:2606.07057v1 Announce Type: cross Abstract: Evaluating the quality of automatically generated keyphrases remains a complex challenge. Traditional metrics either rely on exact lexical matching or

Agents

Measuring Agents in Production

arXiv:2512.04123v4 Announce Type: replace-cross Abstract: LLM-based agents already operate in production across many industries, yet we lack an understanding of what technical methods make deployments

agentsarxiv-cs-ai

Tutorials

Mechanistic Evidence for Faithfulness Decay in Chain-of-Thought Reasoning

arXiv:2602.11201v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) explanations are widely used to interpret how language models solve complex problems, yet it remains unclear whether these st

tutorialsarxiv-cs-cl

MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models

arXiv:2606.06760v1 Announce Type: new Abstract: Medical large vision-language models (Med-LVLMs) have recently achieved remarkable progress in vision-language comprehension and medical image segmentat

researcharxiv-cs-cv

MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism

arXiv:2606.07512v1 Announce Type: cross Abstract: Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and

MetaConfigurator: AI-Assisted RDF Authoring from JSON Data

arXiv:2606.07094v1 Announce Type: cross Abstract: Scientific workflows increasingly generate structured JSON data that is easy to exchange but difficult to interpret consistently across systems due to

MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts

arXiv:2510.05363v2 Announce Type: replace Abstract: Adapting Foundation Models to new domains with limited training data is challenging and computationally expensive. While prior work has demonstrated

Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations

arXiv:2606.06779v1 Announce Type: cross Abstract: In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity

applicationsarxiv-cs-ai

Mind the Gap: Disentangling Performance Bottlenecks in Video Instance Segmentation

arXiv:2606.07394v1 Announce Type: new Abstract: In Video Instance Segmentation (VIS), classification, segmentation, and tracking objectives are jointly evaluated, but their individual contributions to

researcharxiv-cs-cv

Mining Useful General Data for Low-Resource Domain Adaptation

arXiv:2511.07380v2 Announce Type: replace Abstract: Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarcity of domain-specific data. While in-domain data

safetyarxiv-cs-cl

Mission-Level Runtime Assurance Framework for Autonomous Driving

arXiv:2606.06996v1 Announce Type: new Abstract: This paper studies runtime safety for autonomous driving when high-level driving commands become faulty or unreliable. Unlike conventional runtime-safet

safetyarxiv-cs-ro

MIST: Mutual Information Estimation Via Supervised Training

arXiv:2511.18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators. Since any MI estimator is a function of the observed sample

researcharxiv-cs-lg

Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge

arXiv:2606.07368v1 Announce Type: cross Abstract: Automated mitosis detection is a well-established task in computational pathology. While previous benchmarks focused on scanner-induced domain shift,

applicationsarxiv-cs-ai

MMAE: A Massive Multitask Audio Editing Benchmark

model-releasesarxiv-cs-cl

arXiv:2606.07229v1 Announce Type: cross Abstract: We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation testbed designed for general-purpose ins

MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models

arXiv:2606.06696v1 Announce Type: cross Abstract: Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling

mmPISA-bench: Do LLMs Reason Equally Well Across 43 Languages?

model-releasesarxiv-cs-cl

arXiv:2606.07069v1 Announce Type: new Abstract: We introduce mmPISA-bench, a compact high-quality multilingual reasoning benchmark derived from the OECD Programme for International Student Assessment

MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs

arXiv:2506.01850v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in instruction-following tasks by integrating pretrained visual enco

Agents

Model Context Protocols in Adaptive Transport Systems: A Survey

arXiv:2508.19239v2 Announce Type: replace Abstract: The rapid expansion of interconnected devices, autonomous systems, and AI applications has created severe fragmentation in adaptive transport system

agentsarxiv-cs-ai

Model Recycling Framework for Multi-Source Data-Free Supervised Transfer Learning

arXiv:2508.02039v2 Announce Type: replace Abstract: Increasing concerns for data privacy and other difficulties associated with retrieving source data for model training have created the need for sour

Modeling AdaGrad, RMSProp, and Adam with Integro-Differential Equations

arXiv:2411.09734v3 Announce Type: replace Abstract: In this paper, we propose a continuous-time formulation for the AdaGrad, RMSProp, and Adam optimization algorithms by modeling them as first-order i

researcharxiv-cs-lg

Modeling Nonlinear Feature Interactions with Product-Unit Residual Networks

arXiv:2606.06861v1 Announce Type: cross Abstract: Understanding nonlinear feature interactions is crucial in science and engineering, yet standard multilayer perceptrons (MLPs) often capture such inte

Modeling semantic association in self-paced reading with language model embeddings

arXiv:2606.07066v1 Announce Type: new Abstract: Semantic association between a word and its context has been identified as an important component of reading comprehension, even when word predictabilit

Modular Monolingual Adaptation using Pretrained Language Models

arXiv:2606.06738v1 Announce Type: new Abstract: Building monolingual language models (LMs) for low-resource languages typically relies on adapting pretrained language models (PLMs) by finetuning the w

MotionEnhancer: Leveraging Video Diffusion for Motion-Enhanced Vision-Language Models

arXiv:2606.06853v1 Announce Type: cross Abstract: The new era has witnessed a remarkable capability to extend Vision-Language Models (VLMs) for tackling tasks of video understanding. While current VLM

Local Ai

MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection

arXiv:2606.06718v1 Announce Type: cross Abstract: Myocardial substrate abnormalities, such as myocardial scar and myocardial infarction (MI), are associated with adverse cardiovascular outcomes. Elect

local-aiarxiv-cs-ai

Agents

Multi-Agent Reasoning with Consistency Verification Improves Uncertainty Calibration in Medical MCQA

arXiv:2603.24481v2 Announce Type: replace Abstract: Miscalibrated confidence scores are a practical obstacle to deploying AI in clinical settings. A model that is always overconfident offers no useful

agentsarxiv-cs-ai

Multi-FRuGaL: Multimodal Flexible Redundancy-aware Decomposed Gated Learning for Cancer Diagnosis and Prognosis

arXiv:2606.06867v1 Announce Type: new Abstract: Modern medicine relies on heterogeneous data sources spanning radiology, pathology, text reports, and structured clinical information. However, real-wor

applicationsarxiv-cs-cv

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials

arXiv:2602.16908v2 Announce Type: replace-cross Abstract: Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural net

researcharxiv-cs-lg

Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models

arXiv:2505.10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.g., DPO, IPO) has greatly improved alignment, yet these approaches assume a sing

Multi-Robot Planning and Control from CCTV Camera Networks in a Real Warehouse

arXiv:2606.06762v1 Announce Type: new Abstract: Off-board control of mobile robots from cameras embedded in the environment offers a practical path to scalable autonomy, moving sensing and compute off

researcharxiv-cs-ro

Multi-Scale Feature Attention Network for Polymer Classification using THz Dual-Comb Spectroscopy

arXiv:2606.06554v1 Announce Type: cross Abstract: Reliable polymer identification is essential for ensuring the quality and safety of recycled plastics, yet conventional sorting and spectroscopic tech

Multilingual Multi-Speaker Unit Vocoders: A Systematic Analysis of Discrete Speech Representations

arXiv:2606.06740v1 Announce Type: cross Abstract: Discrete speech units obtained via k-means clustering of self supervised embeddings entangle phonetic, speaker, and language information, causing spea

Multiscale POD of Transformer Attention Fields: Scale-Selective Analysis via Morlet Scalogram

arXiv:2606.06573v1 Announce Type: cross Abstract: We introduce scale-selective Proper Orthogonal Decomposition (POD) for transformer attention fields, inspired by the use of POD for extracting energet

MVCL-DAF++: Enhancing Multimodal Intent Recognition via Prototype-Aware Contrastive Alignment and Coarse-to-Fine Dynamic Attention Fusion

arXiv:2509.17446v3 Announce Type: replace-cross Abstract: Multimodal intent recognition (MMIR) suffers from weak semantic grounding and poor robustness under noisy or rare-class conditions. We propose

Hardware

MVSegNet: A Lightweight Boundary-Aware Network for Fetal Lateral Ventricle Segmentation and Atrial Width Estimation in Prenatal Ultrasound

arXiv:2606.06958v1 Announce Type: new Abstract: Fetal ventriculomegaly is assessed by measuring the atrial width of the lateral ventricle in prenatal ultrasound. Accurate segmentation is essential for

hardwarearxiv-cs-cv

Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment

arXiv:2606.07117v1 Announce Type: cross Abstract: This paper presents Native3D, the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations. Traditional

Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach

arXiv:2606.07483v1 Announce Type: new Abstract: Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion. A central

safetyarxiv-cs-lg

Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints

arXiv:2606.06877v1 Announce Type: cross Abstract: Task planning often suffers from severe efficiency bottlenecks when robots must reason over long-horizon action sequences under complex logical constr

Never Seen Before: Benchmarking Genuine Zero-Shot Composed Image Retrieval with Consistent Video-Sourced Datasets

arXiv:2606.07032v1 Announce Type: cross Abstract: Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption with