All entries

Model Merging in the Essential Subspace

arXiv:2602.20208v2 Announce Type: replace Abstract: Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model

Modular Diffusion Models for Structured Visual Recognition

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Clear filters

arXiv:2606.22702v1 Announce Type: new Abstract: Traditional supervised methods for structured visual recognition tasks -- such as object detection, segmentation, and scene graph generation -- often pr

local-aiarxiv-cs-cv

23 Jun 2026

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning

arXiv:2606.21943v1 Announce Type: new Abstract: Reinforcement learning (RL) has become central to LLM post-training, yet the methods that dominate current pipelines, PPO and GRPO, represent only a nar

safetyarxiv-cs-lg

MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts

arXiv:2606.21033v1 Announce Type: cross Abstract: Image compression for machines calls for a unified codec that serves multiple downstream vision tasks. Existing approaches either adopt task-specific

MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

arXiv:2212.04495v3 Announce Type: replace Abstract: Conventional methods for human motion synthesis are either deterministic or struggle with the trade-off between motion diversity and motion quality.

MOOZY: A Patient-First Foundation Model for Computational Pathology

arXiv:2603.27048v3 Announce Type: replace Abstract: Computational pathology needs whole-slide image (WSI) foundation models that transfer across diverse clinical tasks, yet current approaches remain l

MORL-A2C: Multi-Objective Reinforcement Learning Reranker for Optimizing Healthiness in MOPI-HFRS

arXiv:2606.23603v1 Announce Type: new Abstract: Unhealthy dietary behavior continues to be a persistent public health issue in the United States, exacerbated by recommendation systems that prioritize

Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction

arXiv:2606.22077v1 Announce Type: new Abstract: Morphological traits provide important evidence for phylogenetic reconstruction and evolutionary relationship analysis. Recent image-based approaches ha

Motion-Aware Reinforcement Learning For Object Localization

arXiv:2606.21764v1 Announce Type: new Abstract: We present MARLNet (Motion-Aware Reinforcement Learning Network), a PPO-based bounding-box refinement agent that incorporates a constant-velocity motion

MotionHalluc: Diagnosing Kinematic Hallucinations in Fine-Grained Motion Reasoning

arXiv:2606.23061v1 Announce Type: new Abstract: Motion instruction generation in cross-video comparison aims to produce corrective feedback that describes the differences between a query and a referen

MotionMAR: Multi-scale Auto-Regressive Human Motion Reconstruction from Sparse Observations

arXiv:2606.23000v1 Announce Type: new Abstract: Human motion follows a temporal hierarchical structure, transitioning from low-frequency global trajectories to high-frequency details. Inspired by the

MotionPyramid: Hierarchical Motion Representation and Residual Interfaces

arXiv:2606.20705v1 Announce Type: new Abstract: We ask whether the representational hierarchy seen in perception, from local primitives such as edges to higher level structures such as parts and objec

Applications

MS-rPPG: Multi-spectral State Space Model for Remote Photoplethysmography in Driver Monitoring Systems

arXiv:2606.21115v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) is a camera-based technique for measuring physiological signals, particularly cardiac activity. From the remotely mea

applicationsarxiv-cs-cv

Multi-AUV Marine Life Tracking with Single Hydrophone Payloads via a Hidden Markov Model Equipped Particle Filter

arXiv:2606.22335v1 Announce Type: new Abstract: Researchers tag and track marine animals to study migration patterns, human impacts on behavior, and behavioral shifts due to climate change. Accurate d

local-aiarxiv-cs-ro

Hardware

Multi-cancer detection using a computationally efficient CNN with transfer learning

arXiv:2606.22400v1 Announce Type: new Abstract: This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detec

hardwarearxiv-cs-cv

Multi-Depth Concept Extraction for Post-Hoc Vision Encoder Explanation

arXiv:2411.19700v5 Announce Type: replace Abstract: Explainable AI methods for vision models aim to identify the parts of the input that are important for the final prediction and subsequently relate

Multi-Target Maneuver Coordinations: Unlocking Coordination Opportunities in Connected Automated Driving

arXiv:2606.22055v1 Announce Type: cross Abstract: Maneuver coordination is a key enabler of connected and automated driving, allowing vehicles to negotiate and execute maneuvers that would otherwise b

researcharxiv-cs-ro

Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework

arXiv:2502.17583v2 Announce Type: replace-cross Abstract: Multi-year-to-decadal climate predictions are a key tool in understanding the range of potential regional climate futures. Here, we present a

safetyarxiv-cs-lg

Multi4D: High-Fidelity Dynamic Gaussian Splatting via Multi-Level Competitive Allocation

arXiv:2606.22197v1 Announce Type: new Abstract: Dynamic 3D Gaussian splatting faces a fundamental tension between motion consistency and visual fidelity. Deformation-based approaches preserve temporal

Multigrid Training for Molecular Generation using Graph Neural Networks

arXiv:2606.22377v1 Announce Type: new Abstract: Deep learning has demonstrated significant success for modeling biochemical molecular systems, where inputs are commonly represented as graphs or 3D gri

MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learninga

arXiv:2606.22220v1 Announce Type: new Abstract: Memorization in machine learning models enables high performance on rare in-distribution samples by capturing their atypical patterns. However, it also

Multimodal Image Colorization: Quantifying the Impact of Text-Conditioned Guidance on Grayscale-to-Color Translation

arXiv:2606.20722v1 Announce Type: cross Abstract: Grayscale images are commonly found in historical photography restoration, medical imaging, and artistic media. However, automatically applying color

muMatch: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM

arXiv:2606.21605v1 Announce Type: new Abstract: Vision foundation models have substantially advanced computer vision, enabling state-of-the-art performance in zero- and few-shot settings. They have be

Muown Implicitly Performs Angular Step-size Decay

arXiv:2606.23637v1 Announce Type: new Abstract: Matrix-aware optimizers such as Muon and Muown have recently shown strong empirical performance for pre-training Transformers. In particular, Muown sepa

Music Playlist Captioning at Scale with Large Language Models

arXiv:2606.22460v1 Announce Type: cross Abstract: Music streaming services such as Deezer often recommend personalized playlists to users. Playlist captioning, which involves describing these playlist

MV-WAM: Manifold-Aware World Action Model with Value Augmentation

arXiv:2606.21088v1 Announce Type: new Abstract: Achieving robust and generalizable manipulation across diverse environments remains a fundamental challenge in embodied robotics. Recent world action mo

safetyarxiv-cs-ro

MythraGen: Two-Stage Retrieval Augmented Art Generation Framework

arXiv:2606.22924v1 Announce Type: new Abstract: Text-to-image generation has seen rapid advancements, especially with the development of generative models. However, challenges remain in achieving high

N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout

arXiv:2509.18671v2 Announce Type: replace Abstract: Determining where to execute the manipulation policy is a fundamental challenge in mobile manipulation. Most approaches have formulated this as a ge

local-aiarxiv-cs-ro

Applications

NAC: Neural Action Codec for Vision-Language-Action Models

arXiv:2606.21372v1 Announce Type: cross Abstract: Vision-language-action (VLA) models rely on discrete action tokenizers to bridge continuous robot control and autoregressive sequence modeling, yet ex

applicationsarxiv-cs-lg

Agents

NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning

arXiv:2606.21297v1 Announce Type: new Abstract: Augmenting model-free reinforcement learning (RL) with representations learned through observation dynamics prediction (observation-predictive RL) can i

agentsarxiv-cs-lg

Native space based pipelines outperform template space based pipeline in subcortical segmentation

arXiv:2606.21463v1 Announce Type: new Abstract: Accurate segmentation of subcortical regions is critical for neurosurgical planning and functional research. Most automated methods rely on template spa

tutorialsarxiv-cs-cv

NegAS: Negative Label Guided Attention and Scoring for Out-of-Distribution Object Detection with Vision-Language Models

arXiv:2606.22537v1 Announce Type: new Abstract: Out-of-Distribution (OOD) detection is essential for ensuring the robustness and reliability of object detection systems deployed in safety-critical app

Agents

Negative Knowledge as Failure-aware Shared Memory for AutoResearch

arXiv:2606.21024v1 Announce Type: cross Abstract: AI-assisted research systems generate many failed attempts, but those failures rarely become a durable, shared knowledge asset. We propose a negative

agentsarxiv-cs-lg

NeoJaundice-AI: Smartphone-Based Neonatal Jaundice Detection Using Dual-Input Deep Learning and Synthetic Augmentation

arXiv:2606.20689v1 Announce Type: new Abstract: Neonatal jaundice (hyperbilirubinemia) is one of the most common conditions affecting newborns worldwide, with India alone recording roughly 15 million

NeoLoc-68: End-to-end 68-point neonatal facial landmark localisation in neonatal clinical environments

arXiv:2606.20823v1 Announce Type: new Abstract: Facial landmark localisation is a prerequisite for developing automated, non-contact neonatal pain assessment methods. Clinicians use pain scales to jud

local-aiarxiv-cs-cv

Agents

NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning

arXiv:2403.06828v4 Announce Type: replace Abstract: Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision

agentsarxiv-cs-ro

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation

arXiv:2606.21061v1 Announce Type: new Abstract: Stochastic segmentation seeks to represent multiple plausible masks for a single image, which is essential in safety- and quality-critical applications

Neural Architecture Search of Sample Reweighting Networks for Complex Distribution Shift

arXiv:2606.22991v1 Announce Type: new Abstract: Sample reweighting is a major approach to addressing distribution shifts, such as label noise and class imbalance. Meta-Weight-Net (MW-Net) is a promisi

Applications

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings

arXiv:2507.07532v4 Announce Type: replace Abstract: While Prover-Verifier Games (PVGs) offer a promising path toward verifiability in nonlinear classification models, they have not yet been applied to

applicationsarxiv-cs-lg

Neural Conjugate Aggregation: Identifiable Unsupervised Multi-Sensor Regression under Heterogeneous Sensor Bias

arXiv:2606.22200v1 Announce Type: new Abstract: We study regression-based data fusion under uncertainty, where multiple noisy and biased measurement sources are available but ground-truth labels are a

safetyarxiv-cs-lg

Neural Networks as Linear Regression: An Introduction for Statisticians

arXiv:2606.23601v1 Announce Type: cross Abstract: Neural networks are a commonly used prediction tool in computer science and statistics. However, the barrier to entry of this interesting field remain

Neural Operator Processes for Probabilistic Operator Learning under Partial Observations

arXiv:2606.22946v1 Announce Type: new Abstract: Neural operators learn mappings between function spaces, but are typically developed with dense input-output training fields and fully observed inputs a

tutorialsarxiv-cs-lg

Neural Parameter Calibration for Finite-State Mean Field Games

arXiv:2606.23155v1 Announce Type: cross Abstract: Mean field games efficiently approximate a very large population of strategic agents. While these games can aid the understanding of complex systems,

NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication

arXiv:2606.20673v1 Announce Type: cross Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained. In particular, vari

New Smooth Loss functions for Robust Regression that Closely Approximate Absolute Error and Provide Improved Performance on Datasets With Significant Outliers

arXiv:2606.22068v1 Announce Type: new Abstract: The performance of supervised machine learning models is directly related to the quality of the training dataset. In particular, the presence of signifi

NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations

arXiv:2601.17207v2 Announce Type: replace Abstract: We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving different

tutorialsarxiv-cs-lg

Next-Gen CAPTCHAs: Leveraging the Cognitive Gap for Scalable and Diverse GUI-Agent Defense

arXiv:2602.09012v2 Announce Type: replace Abstract: The rapid evolution of GUI-enabled agents has rendered traditional CAPTCHAs obsolete. While previous benchmarks like OpenCaptchaWorld established a

NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling

arXiv:2606.23200v1 Announce Type: cross Abstract: Neighboring-slice self-supervised denoising is attractive for volumetric medical imaging, yet inter-slice misalignment breaks anatomical correspondenc

tutorialsarxiv-cs-cv