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Metric-Guided Feature Fusion of Visual Foundation Models for Segmentation Tasks

arXiv:2605.16864v1 Announce Type: cross Abstract: Although large-scale visual foundation models (VFMs) achieve remarkable performance in semantic understanding, they still underperform in instance-awa

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MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation

DGX agent

arXiv:2605.16464v1 Announce Type: cross Abstract: Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience

researcharxiv-cs-ai

19 May 2026

Tutorials

Mind the Gap: Learning Modality-Agnostic Representations with a Cross-Modality UNet

arXiv:2605.16887v1 Announce Type: new Abstract: Cross-modality recognition has many important applications in science, law enforcement and entertainment. Popular methods to bridge the modality gap inc

tutorialsarxiv-cs-cv

MiniGPT: Rebuilding GPT from First Principles

model-releasesarxiv-cs-cl

arXiv:2605.17398v1 Announce Type: new Abstract: This paper presents MiniGPT, a compact from-scratch implementation of GPT-style autoregressive language modeling in PyTorch. The aim is to rebuild the c

Local Ai

Mining Forgery Traces from Reconstruction Error: A Weakly Supervised Framework for Multimodal Deepfake Temporal Localization

arXiv:2601.21458v2 Announce Type: replace Abstract: Modern deepfakes have evolved into localized and intermittent manipulations that require fine-grained temporal localization to mitigate severe digit

local-aiarxiv-cs-cv

Minor First, Major Last: A Depth-Induced Implicit Bias of Sharpness-Aware Minimization

arXiv:2603.08290v2 Announce Type: replace-cross Abstract: We study the implicit bias of Sharpness-Aware Minimization (SAM) when training L-layer linear diagonal networks on linearly separable binary c

safetyarxiv-cs-ai

MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery

model-releasesarxiv-cs-cv

arXiv:2605.17198v1 Announce Type: cross Abstract: To be useful for downstream applications, vision decoding models that are trained to reconstruct seen images from human brain activity must be able to

Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints

arXiv:2605.16262v1 Announce Type: new Abstract: Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial trai

Mirror Mean-Field Langevin Dynamics

arXiv:2505.02621v2 Announce Type: replace Abstract: The mean-field Langevin dynamics (MFLD) minimizes an entropy-regularized nonlinear convex functional on the Wasserstein space over R^d, and has gain

MirrorBench: A Benchmark to Evaluate Conversational User-Proxy Agents for Human-Likeness

arXiv:2601.08118v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as human simulators, both for evaluating conversational systems and for generating fine-tuning da

Missing-Modality-Aware Graph Neural Network for Cancer Classification

arXiv:2506.22901v2 Announce Type: replace-cross Abstract: A key challenge in learning from multimodal biological data is missing modalities, where data from one or more modalities are absent for some

Mitigating 3D Prostate Biparametric MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection

arXiv:2507.06384v2 Announce Type: replace-cross Abstract: Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image int

Mitigating Conversational Inertia in Multi-Turn Agents

arXiv:2602.03664v3 Announce Type: replace Abstract: Large language models excel as few-shot learners when provided with appropriate demonstrations, yet this strength becomes problematic in multiturn a

safetyarxiv-cs-ai

Mixing Times of Glauber Dynamics on Masked Language Models

arXiv:2605.16378v1 Announce Type: cross Abstract: Masked language models (MLMs) define local conditional distributions over tokens but do not, in general, correspond to any consistent joint distributi

MixSD: Mixed Contextual Self-Distillation for Knowledge Injection

model-releasesarxiv-cs-cl

arXiv:2605.16865v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is widely used to inject new knowledge into language models, but it often degrades pretrained capabilities such as reasonin

Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource

arXiv:2506.12119v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token co

Mixture of Experts for Low-Resource LLMs

model-releasesarxiv-cs-cl

arXiv:2605.17598v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable efficient model scaling, yet expert routing behavior across underrepresented languages remains poorly unde

Mixup Barcodes: Quantifying Geometric-Topological Interactions between Point Clouds

arXiv:2402.15058v3 Announce Type: replace-cross Abstract: We combine standard persistent homology with image persistent homology to define a novel way of characterizing shapes and interactions between

ML-based Fast Simulation of FARICH Responses

arXiv:2605.17635v1 Announce Type: cross Abstract: A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern p

MLReplicate: Benchmarking Autonomous Research Systems for Machine Learning Reproducibility

model-releasesarxiv-cs-lg

arXiv:2605.16616v1 Announce Type: new Abstract: Autonomous research systems capable of generating complete scientific manuscripts have advanced rapidly, yet robust and realistic evaluation frameworks

MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation

arXiv:2605.17743v1 Announce Type: new Abstract: Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams while retaining past competence, yet texture

safetyarxiv-cs-cv

MoCA3D: Monocular 3D Bounding Box Prediction in the Image Plane

arXiv:2603.19538v2 Announce Type: replace Abstract: Monocular 3D object understanding has largely been cast as a 2D RoI-to-3D box lifting problem. However, emerging downstream applications require ima

Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals

arXiv:2605.18483v1 Announce Type: cross Abstract: Time series classification (TSC) of biological signals has progressed from handcrafted, modality-specific approaches to deep architectures capable of

Agents

Modelling Customer Trajectories with Reinforcement Learning for Practical Retail Insights

arXiv:2605.18449v1 Announce Type: cross Abstract: Understanding customer movement within retail spaces is essential for optimizing store layouts. Real-world trajectory data can provide highly accurate

agentsarxiv-cs-ai

MoleCode unlocks structural intelligence in large language models

arXiv:2605.16480v1 Announce Type: cross Abstract: Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular repre

Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics

arXiv:2605.18549v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through their Chain of Thought (CoT) reasoning. However, CoT is not alwa

safetyarxiv-cs-cl

Local Ai

Mono-Hydra++: Real-Time Monocular Scene Graph Construction with Multi-Task Learning for 3D Indoor Mapping

arXiv:2605.17661v1 Announce Type: cross Abstract: Autonomous agile robots need more than metric geometry: they must understand objects, rooms, places, and spatial relations for search, inspection, exp

local-aiarxiv-cs-cv

Monocular Depth Perception Enhancement Based on Joint Shading/Contrast Model and Motion Parallax (JSM)

arXiv:2605.17252v1 Announce Type: new Abstract: Stereoscopic 3D displays adopt a binocular depth cue to provide depth perception. However, users should be equipped with expensive special devices to ap

Monocular Open Vocabulary Occupancy Prediction for Indoor Scenes

model-releasesarxiv-cs-cv

arXiv:2602.22667v2 Announce Type: replace Abstract: Open-vocabulary 3D occupancy is vital for embodied agents, which need to understand complex indoor environments where semantic categories are abunda

MORN: Metacognitive Object-Goal Regulation for Resource-Rational Long-Horizon Navigation

arXiv:2605.16932v1 Announce Type: new Abstract: Robots deployed in unstructured human environments must frequently execute long-horizon missions, such as find the mug, then the chair, then the printer

researcharxiv-cs-ro

MorphSeek: Fine-grained Latent Representation-Level Policy Optimization for Deformable Image Registration

model-releasesarxiv-cs-cv

arXiv:2511.17392v3 Announce Type: replace Abstract: Deformable image registration (DIR) remains a fundamental yet challenging problem in medical image analysis, largely due to the prohibitively high-d

Agents

Motion Cues from Image-based Point Tracking for LiDAR Scene Flow Estimation

arXiv:2605.16922v1 Announce Type: new Abstract: LiDAR scene flow estimation is essential for autonomous driving, as it provides 3D motion for each point. Self-supervised approaches use static-dynamic

agentsarxiv-cs-cv

Motion Planning of Cooperative Nonholonomic Mobile Manipulators

arXiv:2502.05462v2 Announce Type: replace Abstract: We propose a real-time implementable motion planning framework for cooperative object transportation by nonholonomic mobile manipulator robots (MMRs

researcharxiv-cs-ro

Motion-Uncertainty-Aware Next-Best-View Planning for Moving Object Reconstruction

arXiv:2605.17593v1 Announce Type: new Abstract: Active 3D reconstruction of moving objects requires selecting informative viewpoints while accounting for object motion uncertainty during the decision-

applicationsarxiv-cs-ro

MR-SLAM: Immersive Spatial Supervision for Multi-Robot Mapping via Mixed Reality

arXiv:2605.16432v1 Announce Type: cross Abstract: Operating a multi-robot fleet for simultaneous localization and mapping (SLAM) in applications such as building inspection or warehouse-aisle monitori

MSIQ: Moment-based Scale-Invariant Quality Measure for Single Image Super-Resolution

arXiv:2605.17588v1 Announce Type: new Abstract: Assessing the quality of single image super-resolution (SISR) results remains an open methodological problem. Common full-reference metrics (PSNR, SSIM,

safetyarxiv-cs-cv

Local Ai

Multi-agent AI systems outperform human teams in creativity

arXiv:2605.17885v1 Announce Type: cross Abstract: Although artificial intelligence (AI) now matches or exceeds human performance across numerous cognitive tasks, creativity remains a highly contested

local-aiarxiv-cs-ai

Multi-hop Relational Contrastive Learning: Extending Spatial Contrastive Pre-training Beyond Pairwise Relations

arXiv:2605.16456v1 Announce Type: new Abstract: Understanding how objects relate to each other in space is fundamental to scene understanding, yet most contrastive pre-training approaches only model p

Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors

arXiv:2604.00919v2 Announce Type: replace-cross Abstract: Energy-based models provide a natural bridge between statistical physics and machine learning by representing data through structured energy l

Multi-Object Tracking Consistently Improves Wildlife Inference

arXiv:2605.16672v1 Announce Type: cross Abstract: Camera traps have become a common tool for wildlife monitoring efforts in ecological research and biodiversity conservation. Wildlife classification m

Multi-Order Matching Network for Alignment-Free Depth Super-Resolution

arXiv:2511.16361v3 Announce Type: replace Abstract: Recent guided depth super-resolution methods are premised on the assumption of strict spatial alignment between depth and RGB, achieving high-qualit

safetyarxiv-cs-cv

Agents

Multi-Paradigm Agent Interaction in Practice:A Systematic Analysis of Generator-Evaluator, ReAct Loop,and Adversarial Evaluation in the buddyMe Framework

arXiv:2605.16821v1 Announce Type: new Abstract: The rapid evolution of Large Language Model (LLM) agents has produced diverse interaction paradigms, yet few production systems integrate multiple parad

agentsarxiv-cs-ai

Multi-Party Multi-Objective Optimization as Consensus Search: Runtime Analysis of Cross-Party Recombination

arXiv:2605.17454v1 Announce Type: new Abstract: Multi-party multi-objective optimization problems (MPMOPs) require consensus among autonomous decision makers and therefore differ from flattened many-o

Multi-site PPG: An In-the-Wild Physiological Dataset from Emerging Multi-site Wearables

model-releasesarxiv-cs-lg

arXiv:2605.17859v1 Announce Type: cross Abstract: Wearables are widely used for mobile health monitoring, and photoplethysmography (PPG) is a key sensing modality for heart rate and related physiologi

Multi-task learning on partially labeled datasets via invariant/equivariant semi-supervised learning

arXiv:2605.17624v1 Announce Type: cross Abstract: We investigate the potential of invariant and equivariant semi-supervised learning for addressing the challenges of training multi-task models on part

Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness and Safety

arXiv:2605.17126v1 Announce Type: cross Abstract: We study the multi-task linear regression problem in the presence of contaminated tasks. We address the setting where the unknown parameters of a majo

safetyarxiv-cs-lg

Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages

arXiv:2605.17152v1 Announce Type: new Abstract: Multimodal LLMs are evolving from vision-language to tri-modality that see, hear, and read, yet pipelines and benchmarks remain English-centric and comp

safetyarxiv-cs-cl

Multilingual jailbreaking of LLMs using low-resource languages

arXiv:2605.18239v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attempts that circumvent safety guardrails. We investigate whether multi-turn conversation