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LOTUSim: Multi-Domain Simulator for Marine Robotics

arXiv:2607.03072v1 Announce Type: cross Abstract: Simulation is essential for maritime robotics, supporting operator training, mission rehearsal, and human-vehicle interaction in environments where re

applicationsarxiv-cs-ro

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LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure

DGX agent

arXiv:2607.04733v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is the standard approach for adapting pretrained language models to downstream domains, yet it often improves target-domain

local-aiarxiv-cs-cl

7 Jul 2026

LRX-PINN: A Layer-Resolving XNet Physics-Informed Neural Network with Integrated Cauchy Activations for Convection-Dominated Problems

arXiv:2607.03682v1 Announce Type: cross Abstract: Convection-dominated convection-diffusion problems often develop thin layers, where the solution has sharp transition profiles and its derivatives are

LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering

model-releasesarxiv-cs-cl

arXiv:2607.02763v1 Announce Type: new Abstract: Spoken Question Answering (SQA) remains largely focused on high-resource languages and carefully recorded speech, limiting the reach of speech-LLM metho

Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic

arXiv:2607.04531v1 Announce Type: cross Abstract: Low-precision neural networks are attractive for resource-constrained hardware, but fixed-point arithmetic introduces failure modes that are often hid

MABLE: Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning

arXiv:2607.02990v1 Announce Type: new Abstract: We propose MABLE (Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning), a self-supervised framework for learning nod

safetyarxiv-cs-lg

Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology

arXiv:2607.04648v1 Announce Type: cross Abstract: Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of inte

applicationsarxiv-cs-ai

Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk

arXiv:2510.25147v3 Announce Type: replace Abstract: To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas. The Optimal Power Shutoff (OPS) problem optimizes l

Machine Unlearning via Information Theoretic Regularization

arXiv:2502.05684v5 Announce Type: replace-cross Abstract: How can we effectively remove or ``unlearn'' undesirable information, such as specific features or the influence of individual data points, fr

MACRO: Training-free Multi-plane Attention for Closeup Render Optimization

arXiv:2607.03875v1 Announce Type: new Abstract: Close-up rendering, zooming into a scene well beyond any training camera, is important for virtual production and interactive 3D content, yet remains an

applicationsarxiv-cs-cv

MACS: Measurement-Aware Consistency Sampling for Inverse Problems

arXiv:2510.02208v3 Announce Type: replace-cross Abstract: Diffusion models have emerged as powerful generative priors for solving inverse imaging problems. However, their practical deployment is hinde

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control

arXiv:2509.23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-ag

safetyarxiv-cs-ai

MAGE: View-guided Point Cloud Completion with Efficient Modality Alignment and Adaptive Geometry Enhancement

arXiv:2607.02568v1 Announce Type: new Abstract: View-based point cloud completion aims to recover a complete 3D shape from a partial point cloud, guided by a single-view image. However, existing appro

safetyarxiv-cs-cv

MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network

arXiv:2407.20893v2 Announce Type: replace-cross Abstract: Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments. With the

MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model

arXiv:2607.03013v1 Announce Type: cross Abstract: Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limit

local-aiarxiv-cs-ai

MambaRefine-CD: MambaVision with Region-Boundary Temporal Refinement

arXiv:2607.04403v1 Announce Type: cross Abstract: Binary change detection in remote sensing requires both complete changed-region localization and accurate boundary delineation. We present MambaRefine

MANCE: Manifold Aware Concept Erasure

arXiv:2607.03973v1 Announce Type: new Abstract: Concept erasure aims to remove a target concept from a representation while preserving the other information encoded in it. This is difficult because re

ManifoldFlow: SPD-Relaxed Stiefel Layers with Learnable Singular Spectrum

arXiv:2607.04535v1 Announce Type: new Abstract: Orthogonal and Stiefel layers give neural weights exact spectral control, but they also impose a strong modeling constraint: all represented singular va

MARLIN: De Novo Molecular Structure Elucidation from Tandem Mass Spectra without a Ground-Truth Formula

model-releasesarxiv-cs-lg

arXiv:2607.04774v1 Announce Type: new Abstract: Untargeted tandem mass spectrometry (MS/MS) detects thousands of small molecules per biological sample, yet most go unidentified because they are absent

Agents

Mask-based Predictive Representations for Reinforcement Learning

arXiv:2607.04153v1 Announce Type: cross Abstract: Vision-based deep reinforcement learning involves dealing with high-dimensional inputs of image information. It is crucial to abstract effective state

agentsarxiv-cs-ai

Mask2Real-WM: Segmentation Masks as a Sim-to-Real Bridge for Controllable Dexterous World Models

arXiv:2607.04546v1 Announce Type: cross Abstract: Action-conditioned world models allow robots to predict the future consequences of candidate actions without additional physical interaction, supporti

Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition

arXiv:2607.04139v1 Announce Type: new Abstract: Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization. However, its ap

local-aiarxiv-cs-lg

MatPhaseBench: A Semantics-Guided Benchmark for Materials Phase Diagrams Understanding

arXiv:2607.02934v1 Announce Type: cross Abstract: Materials phase diagrams are a core knowledge representation in materials science, encoding temperature,composition, phase stability, and phase transf

MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation

arXiv:2603.25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling a

safetyarxiv-cs-ai

MDL Meets Latent Confounders: LNML-based Causal Discovery

arXiv:2607.04133v1 Announce Type: new Abstract: Causal discovery with nonlinear mechanisms and latent confounders remains challenging. Existing methods often rely on either linear assumptions or causa

applicationsarxiv-cs-lg

Measuring 3D Spatial Geometric Consistency in Dynamic Video Generation

arXiv:2603.19048v2 Announce Type: replace Abstract: Recent generative models can produce high-fidelity videos, yet they often exhibit 3D spatial geometric inconsistencies. Existing evaluation methods

Measuring and Mitigating Post-hoc Rationalization in Reverse Chain-of-Thought Generation

arXiv:2602.14469v3 Announce Type: replace Abstract: Reverse Chain-of-Thought Generation (RCG) synthesizes reasoning traces from query-answer pairs, but it risks producing post-hoc rationalizations: wh

researcharxiv-cs-cl

Measuring Harness-Induced Belief Divergence in Multi-Step LLM Agents

arXiv:2607.04528v1 Announce Type: new Abstract: Software-agent benchmarks usually report whether an agent solves a task, but the agent reaches that outcome through a harness that controls what it sees

Measuring the Robustness of Audio Deepfake Detection under Real-World Corruption

arXiv:2503.17577v2 Announce Type: replace-cross Abstract: Deepfakes have emerged as a widespread and rapidly escalating concern in generative AI, spanning images, audio, and videos. Among these, audio

applicationsarxiv-cs-ai

Measuring What Matters: A Unified Evaluation Framework for GNN Explainability

arXiv:2607.04600v1 Announce Type: new Abstract: Graph eXplainable AI (G-XAI) is increasingly important for making Graph Neural Networks interpretable and accountable. While a growing number of explain

Mechanism-level routing failure in LLMs over Lean-verified algebraic structures

model-releasesarxiv-cs-cl

arXiv:2607.04534v1 Announce Type: new Abstract: We present an empirical study of structural routing failure in large language models (LLMs) over a formally verified algebraic corpus. The task requires

Agents

MechMath Agent Team: LLM Driven Agents for Mathematical Research

arXiv:2607.04394v1 Announce Type: new Abstract: AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models. However, mathem

agentsarxiv-cs-ai

MedCalc-Pro: Solving Complex Medical Calculations with LLM Agents

arXiv:2607.02879v1 Announce Type: new Abstract: Current benchmarks for evaluating large language models (LLMs) in medical calculation are largely based on simplified settings, where each patient case

Medi-Gemma: A Hybrid Clinical Decision Support System Integrating Deterministic EMR Analytics and Retrieval-Augmented Generation

arXiv:2607.04907v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) in high-stakes clinical settings remains limited by structural hallucinations, weak deterministic reasoning over

Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules

arXiv:2606.16337v3 Announce Type: replace Abstract: Predictive modeling for clinical decision support requires not only strong predictive performance but also transparent decision logic. Although deep

MedMambaLite: Hardware-Aware Mamba for Medical Image Classification

arXiv:2508.05049v1 Announce Type: cross Abstract: AI-powered medical devices have driven the need for real-time, on-device inference such as biomedical image classification. Deployment of deep learnin

local-aiarxiv-cs-cv

MeGA-MP: Metric Graph Advection Message Passing -- A Physics-Informed Message Passing Operator for Advection-Dominated Metric Graphs

arXiv:2607.05167v1 Announce Type: new Abstract: Many real-world systems are organized as networks where spatio-temporal dynamics unfold along connections and not discretely between nodes. Examples inc

safetyarxiv-cs-lg

Memory-Efficient FastText: A Comprehensive Approach Using Double-Array Trie Structures and Mark-Compact Memory Management

model-releasesarxiv-cs-cl

arXiv:2506.01254v2 Announce Type: replace Abstract: FastText remains a practical choice for industrial word representation because it can synthesize vectors for out-of-vocabulary words from character

Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture

arXiv:2607.04391v1 Announce Type: new Abstract: Long-term memory remains a structural weakness of AI agents. The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based simi

local-aiarxiv-cs-cl

MemPose: Category-level Object Pose Estimation with Memory

arXiv:2607.04930v1 Announce Type: cross Abstract: In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effec

Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets

arXiv:2607.03540v1 Announce Type: new Abstract: Detecting mental health disorders in a timely manner is an important societal challenge. NLP and machine learning (ML) methods used to assist with detec

researcharxiv-cs-cl

MentalThink: Shaping Thoughts in Mental SVG World

arXiv:2607.03530v1 Announce Type: new Abstract: We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for 'mental' visualizati

safetyarxiv-cs-ai

MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images

arXiv:2607.04747v1 Announce Type: new Abstract: Survival analysis on Whole Slide Images (WSIs) is important in computational pathology for prognosis estimation and treatment planning. However, existin

MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions

arXiv:2510.22298v2 Announce Type: replace-cross Abstract: Uncovering the causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collec

applicationsarxiv-cs-lg

Metallic Ultrasound Waveguides as a Distributed Tactile Sensing Platform for Contact Localization, Force Estimation, and Material Class Discrimination

model-releasesarxiv-cs-ro

arXiv:2607.02827v1 Announce Type: new Abstract: Tactile sensing is central to how robotic systems interact with the real world, yet current solutions face a tradeoff between sensing area and system co

MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration

arXiv:2211.10872v2 Announce Type: replace Abstract: Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability t

MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution

arXiv:2607.05297v1 Announce Type: new Abstract: Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further

local-aiarxiv-cs-ai

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

arXiv:2601.22054v2 Announce Type: replace-cross Abstract: Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging du

applicationsarxiv-cs-ai