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LP-NavOA: Integrated Local Navigation and Obstacle Avoidance for Humanoid Robots under Limited Perception

arXiv:2606.23249v1 Announce Type: new Abstract: Humanoid local navigation in cluttered environments must jointly resolve obstacle avoidance, sparse-goal recovery, and stable whole-body locomotion unde

safetyarxiv-cs-ro

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LSTM Variants for Chaotic Dynamical Systems: An Empirical Study on the Lorenz Attractor

DGX agent

arXiv:2606.22662v1 Announce Type: new Abstract: Forecasting chaotic dynamical systems such as the Lorenz attractor is notoriously difficult: small numerical errors are amplified exponentially over lon

researcharxiv-cs-lg

23 Jun 2026

LUMINA-26: Low-Light Understanding for Modeling and Interpreting Night-time Actions

arXiv:2606.23118v1 Announce Type: new Abstract: Low-light human action recognition remains a challenging problem due to poor illumination, amplified noise, motion ambiguity, and diverse real-world sce

LUQ: Layerwise Ultra-Low Bit Quantization for Multimodal Large Language Models

arXiv:2509.23729v3 Announce Type: replace Abstract: Large Language Models (LLMs) with multimodal capabilities have revolutionized vision-language tasks, but their deployment often requires huge memory

LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression

arXiv:2304.12319v2 Announce Type: replace-cross Abstract: Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distort

Machine Learning Classification of Cryopathy Syndromes: A Comprehensive Comparative Study

arXiv:2606.20874v1 Announce Type: new Abstract: Cryopathy syndromes are difficult to classify because laboratory patterns often overlap across diagnostic categories, while some diagnoses are rare. Thi

MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning

arXiv:2606.20641v1 Announce Type: cross Abstract: Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in semantic understanding and common sense reasoning, making them

Maintain Plasticity in Long-timescale Continual Test-time Adaptation

arXiv:2412.20034v2 Announce Type: replace Abstract: Continual test-time domain adaptation (CTTA) aims to adjust pre-trained source models to perform well over time across non-stationary target environ

safetyarxiv-cs-cv

MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection

arXiv:2606.23126v1 Announce Type: new Abstract: While recent advancements in anomaly detection have demonstrated the efficacy of CNN- and Transformer-based approaches, these architectures face inheren

local-aiarxiv-cs-cv

MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis

arXiv:2606.21119v1 Announce Type: new Abstract: Mammography is an essential tool for breast cancer detection, with millions of examinations conducted annually. However, publicly available high-quality

Manipulider: A Multi-Engine Buoyancy-Controlled Robot for Thrusterless Underwater Gliding and Manipulation

arXiv:2606.21461v1 Announce Type: new Abstract: The Manipulider is a buoyancy-actuated underwater robot that enables thrusterless, glide-like locomotion and attitude-based manipulation, while providin

researcharxiv-cs-ro

MapReason-OSM: Can Vision-Language Models Make Graph-Verifiable Mobility Decisions from Street Maps ?

arXiv:2606.22597v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used to read maps for logistics, delivery, and accessible navigation, where the output is an actionable d

MAPS: Multi-Anchor Projection Similarity for Joint Vision-Language Geo-Localization

arXiv:2606.22543v1 Announce Type: new Abstract: Humans localize places by integrating perceptual cues from vision with semantic reasoning from language, forming a scene understanding that is both intu

safetyarxiv-cs-cv

MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoring

arXiv:2606.22649v1 Announce Type: new Abstract: Foundation models provide highly descriptive representations for medical images, yet their reliability degrades under distribution shifts arising from c

Agents

MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?

arXiv:2606.23664v1 Announce Type: new Abstract: Multi-agent systems (MAS) offer a scalable path forward for agentic AI, comprising multiple LLM-based agents, each assigned a system prompt and a positi

agentsarxiv-cs-lg

Massive Activations Are Architecturally Robust: A Controlled Scratch/Commitment Residual Stream Test

arXiv:2606.20743v1 Announce Type: new Abstract: Trained transformers reliably develop massive activations, a small number of hidden dimensions whose magnitude is far above the median and which concent

Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials

model-releasesarxiv-cs-lg

arXiv:2606.21830v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has driven rapid progress in mathematical and code reasoning, but when extended to science, existi

Tutorials

MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference

arXiv:2602.15206v2 Announce Type: replace Abstract: Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it rem

tutorialsarxiv-cs-lg

Measuring Intent Comprehension in LLMs

model-releasesarxiv-cs-lg

arXiv:2506.16584v3 Announce Type: replace-cross Abstract: People judge interactions with large language models (LLMs) as successful when outputs match what they want, not what they type. Yet LLMs are

Measuring Model-Induced Discrimination via Efficient Fairness Approximation

arXiv:2405.09251v2 Announce Type: replace Abstract: Providing various machine learning (ML) applications in the real world, concerns about discrimination hidden in ML models are growing, particularly

MedFedPure: A Medical Federated Framework with MAE-based Detection and Diffusion Purification for Inference-Time Attacks

arXiv:2511.11625v2 Announce Type: replace Abstract: Artificial intelligence (AI) has shown great potential in medical imaging, particularly for brain tumor detection using Magnetic Resonance Imaging (

local-aiarxiv-cs-lg

MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models

arXiv:2606.21194v1 Announce Type: new Abstract: Medical Vision-Language Models (Med-VLMs) achieve strong expert-level performance, yet their ability to generate patient-accessible descriptions remains

MedTS-TTT: Test-Time Training for Medical Time Series Classification

model-releasesarxiv-cs-lg

arXiv:2606.21329v1 Announce Type: new Abstract: Medical time series (MedTS) signals such as electroencephalography (EEG) and electrocardiography (ECG) support many clinical applications. However, subs

MeGAS: Thermomechanical Dynamic Gaussian Splatting for Thermophysical Scene Editing

arXiv:2606.23455v1 Announce Type: new Abstract: Recent advances integrate physically grounded Newtonian dynamics with neural rendering frameworks, narrowing the gap between photorealistic scene recons

Membrane-based Acoustic Microrobots

arXiv:2606.21047v1 Announce Type: new Abstract: Acoustic microrobots have emerged as a promising frontier for targeted drug delivery and minimally invasive medicine due to their high-power density and

researcharxiv-cs-ro

Memory Contagion: Cross-Temporal Propagation of Evaluator Bias via Agent Memory

arXiv:2606.23195v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly rely on memory systems to maintain long-term coherence. Recent work shows that agent memories degrade dur

Memory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering

arXiv:2606.21540v1 Announce Type: new Abstract: Graph convolutional networks (GCNs) have demonstrated significant success in capturing complex user-item relationships for collaborative filtering (CF).

MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation

arXiv:2606.20679v1 Announce Type: cross Abstract: Video-world-model policies learn action-relevant representations by predicting future observations. However, they condition on only a short observatio

safetyarxiv-cs-cv

Mesh2GS: White-Box 3DGS Construction via Plenoptic Sampling

arXiv:2606.21898v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising method for high-quality, real-time 3D reconstruction. To associate 3DGS with mesh representati

MeshFlow: Mesh Generation with Equivariant Flow Matching

arXiv:2606.23489v1 Announce Type: cross Abstract: Meshes are among the most common 3D scene representations, but directly generating meshes is challenging because the representation contains important

Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation

arXiv:2606.22146v1 Announce Type: new Abstract: Meta-reinforcement learning is a promising approach to multi-objective optimisation because it enables rapid policy adaptation across changing environme

MILE: A Mechanically Isomorphic Exoskeleton Data Collection System with Fingertip Visuotactile Sensing for Dexterous Manipulation

arXiv:2512.00324v3 Announce Type: replace-cross Abstract: Imitation learning provides a promising approach to dexterous hand manipulation, but its effectiveness is limited by the lack of large-scale,

Mimic Human Cognition, Master Multi-Image Reasoning: A Meta-Action Framework for Enhanced Visual Understanding

arXiv:2601.07298v2 Announce Type: replace Abstract: While Multimodal Large Language Models (MLLMs) excel at single-image understanding, they exhibit significantly degraded performance in multi-image r

Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Data

arXiv:2606.21344v1 Announce Type: cross Abstract: Trajectory prediction allows autonomous vehicles to anticipate the future behavior of surrounding objects (or agents) and, accordingly, maximize the s

local-aiarxiv-cs-lg

Mind the Privileged-to-Camera Gap: Actor-Centric Sidecar Supervision for Camera-First Open-Loop Waypoint Prediction

arXiv:2606.20772v1 Announce Type: new Abstract: Camera-first autonomous-driving models predict future ego waypoints from images, ego-state features, and route commands, but waypoint supervision alone

safetyarxiv-cs-ro

Minimax Quantile Lower Bounds for Interactive Statistical Decision Making with Privacy

arXiv:2606.23096v1 Announce Type: new Abstract: Minimax risk and regret are expectation-based criteria and do not capture rare but consequential failures. To address this concern, we develop a elta-ex

Mirage: a Clean-Label Backdoor against LiDAR 3D Object Detection

arXiv:2606.20752v1 Announce Type: new Abstract: Deep neural network-based LiDAR 3D object detection serves as a critical perception component in safety-critical autonomous systems. However, recent stu

MIRAGE: Stealthy Visual Prompt Injection for Vulnerability Detection in Web Agents

arXiv:2606.20717v1 Announce Type: new Abstract: Multimodal Large Language Model (MLLM)-based web agents provide practical, high-precision solutions for visual browser automation; however, they inheren

local-aiarxiv-cs-cv

Tutorials

MIRCaps: A Large-Scale Mixed-Domain Dataset with Image-Level and Region-Level Captions for Fine-Grained Vision-Language Learning

arXiv:2606.21419v1 Announce Type: new Abstract: Despite recent progress in Vision-Language Models (VLMs), mixed-domain image-caption datasets for both general-purpose and CCTV-based video surveillance

tutorialsarxiv-cs-cv

Mitigating Cross-Image Information Leakage in Multi-Image Understanding with Large Vision-Language Models

arXiv:2508.13744v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) exhibit strong performance on single-image tasks. However, their performance degrades significantly when handli

Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification

arXiv:2606.22551v1 Announce Type: cross Abstract: Hybrid Quantum Neural Network (QNN) classifiers produce logits as expectation values of quantum measurement operators. For standard Pauli measurements

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection

arXiv:2501.03432v3 Announce Type: replace Abstract: The Large Hadron Collider at CERN produces immense volumes of complex data from high-energy particle collisions, demanding sophisticated analytical

mlx-vis: GPU-Native Dimensionality Reduction on Apple Silicon

arXiv:2603.04035v4 Announce Type: replace Abstract: Dimensionality reduction is a foundational tool for visualizing high-dimensional data, yet its reference implementations span a fragmented stack of

local-aiarxiv-cs-lg

MMGist: A Comprehensive Multimodal Benchmark for 2027

arXiv:2606.22437v1 Announce Type: new Abstract: We conduct a systematic study of 18 widely used vision-language benchmarks and identify three major issues: 1) many items do not rely on visual cues and

MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction

arXiv:2606.20906v1 Announce Type: new Abstract: Molecular message-passing neural networks commonly propagate chemically diverse interactions through a single graph, which may mix interaction-specific

MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos

arXiv:2603.14145v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However,

Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis

arXiv:2505.12462v3 Announce Type: replace Abstract: Robust reinforcement learning (RL) under the average-reward criterion is essential for long-term decision-making, particularly when the environment

Model Inversion meets Cryptographic Fuzzy Extractors

arXiv:2510.25687v3 Announce Type: replace-cross Abstract: Model inversion attacks pose an open challenge to privacy-sensitive applications that use machine learning (ML) models. For example, face auth