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Model Organisms Are Leaky: Perplexity Differencing Often Reveals Finetuning Objectives

arXiv:2605.00994v1 Announce Type: new Abstract: Finetuning can significantly modify the behavior of large language models, including introducing harmful or unsafe behaviors. To study these risks, rese

researcharxiv-cs-cl

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Model Releases

MOGO: Residual Quantized Hierarchical Causal Transformer for High-Quality and Real-Time 3D Human Motion Generation

DGX agent

arXiv:2506.05952v4 Announce Type: replace Abstract: Recent advances in transformer-based text-to-motion generation have led to impressive progress in synthesizing high-quality human motion. Neverthele

model-releasesarxiv-cs-cv

5 May 2026

Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading

arXiv:2605.01954v1 Announce Type: cross Abstract: Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback i

applicationsarxiv-cs-cl

Molecular Representations for Large Language Models

arXiv:2605.01822v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly being used to support scientific discovery. In chemistry, tasks such as reaction prediction and structure

MolmoAct2: Action Reasoning Models for Real-world Deployment

model-releasesarxiv-cs-ro

arXiv:2605.02881v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models aim to provide a single generalist controller for robots, but today's systems fall short on the criteria that matter

MolViBench: Evaluating LLMs on Molecular Vibe Coding

arXiv:2605.02351v1 Announce Type: new Abstract: Molecular Vibe Coding, a paradigm where chemists interact with LLMs to generate executable programs for molecular tasks, has emerged as a flexible alter

Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification

arXiv:2605.02292v1 Announce Type: new Abstract: Chest X-ray classification suffers from severe class imbalance where gradient updates bias toward majority classes, causing feature drift and poor perfo

safetyarxiv-cs-cv

MooD: An Efficient VA-Driven Affective Image Editing Framework via Fine-Grained Semantic Control

arXiv:2605.02521v1 Announce Type: new Abstract: Affective image editing (AIE) aims to edit visual content to evoke target emotions. However, existing methods often overlook inference efficiency and pr

MorphIt: Flexible Spherical Approximation of Robot Morphology for Representation-driven Adaptation

model-releasesarxiv-cs-ro

arXiv:2507.14061v2 Announce Type: replace Abstract: What if a robot could rethink its own morphological representation to better meet the demands of diverse tasks? Most robotic systems today treat the

MOSAIC: Multi-agent Orchestration for Task-Intelligent Scientific Coding

arXiv:2510.08804v3 Announce Type: replace Abstract: We present MOSAIC, a multi-agent Large Language Model (LLM) framework for solving challenging scientific coding tasks. Unlike general-purpose coding

Motion-Aware Caching for Efficient Autoregressive Video Generation

arXiv:2605.01725v1 Announce Type: new Abstract: Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computat

MPCS: Neuroplastic Continual Learning via Multi-Component Plasticity and Topology-Aware EWC

arXiv:2605.02509v1 Announce Type: new Abstract: Continual learning systems face a fundamental tension between plasticity -- acquiring new knowledge -- and stability -- retaining prior knowledge. We in

MSMixer: Learned Multi-Scale Temporal Mixing with Complementary Linear Shortcut for Long-Term Time Series Forecasting

arXiv:2605.02689v1 Announce Type: new Abstract: Long-term time series forecasting requires models that simultaneously capture rapid oscillations, medium-range periodicities, and slowly evolving macro-

MTA: Multi-Granular Trajectory Alignment for Large Language Model Distillation

arXiv:2605.01374v1 Announce Type: new Abstract: Knowledge distillation is a key technique for compressing large language models (LLMs), but most existing methods align representations at fixed layers

safetyarxiv-cs-cl

Tutorials

MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation

arXiv:2605.01369v1 Announce Type: cross Abstract: Deep learning has been widely adopted for WiFi CSI-based human activity recognition (HAR) due to its ability to learn spatio-temporal features in a pr

tutorialsarxiv-cs-lg

Multi-Branch Non-Homogeneous Image Dehazing via Concentration Partitioning and Image Fusion

arXiv:2605.00885v1 Announce Type: new Abstract: Existing single image dehazing methods have demonstrated satisfactory performance on homogeneous thin-haze images; however, they often struggle with non

Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection

arXiv:2605.01563v1 Announce Type: new Abstract: We propose a unified cross-domain transfer learning framework that leverages knowledge from multiple heterogeneous medical imaging datasets to improve p

Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks

arXiv:2605.02871v1 Announce Type: cross Abstract: Composite materials exhibit strongly hierarchical and anisotropic properties governed by coupled mechanisms spanning constituents, plies, laminates, s

Multi-Objective Instruction-Aware Representation Learning in Procedural Content Generation RL

arXiv:2508.09193v2 Announce Type: replace Abstract: Recent advancements in generative modeling emphasize the importance of natural language as a highly expressive and accessible modality for controlli

Multi-Perspective Transformers in ARC-AGI-2 Challenge

arXiv:2605.01154v1 Announce Type: new Abstract: ARC-AGI-2 is a benchmark of human-intuitive visual puzzles that measures a machine's ability to generalize from limited examples, interpret symbolic mea

Multi-Rater Calibrated Segmentation Models

arXiv:2605.02437v1 Announce Type: new Abstract: Objective: Accurate probability estimates are essential for the safe deployment of medical image segmentation models in clinical decision-making. Howeve

Multi-Scale Gaussian-Language Map for Zero-shot Embodied Navigation and Reasoning

arXiv:2605.01736v1 Announce Type: new Abstract: Understanding the geometric and semantic structure of environments is essential for embodied navigation and reasoning. Existing semantic mapping methods

safetyarxiv-cs-cv

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare

arXiv:2605.01961v1 Announce Type: new Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents. However

safetyarxiv-cs-lg

Multi-View Hierarchical Representation Learning of Fetal Hemodynamics for Maternal Hypertension Detection at the Edge

arXiv:2605.00872v1 Announce Type: cross Abstract: Hypertensive disorders of pregnancy remain a leading cause of maternal and fetal morbidity worldwide, yet diagnosis relies on intermittent cuff-based

safetyarxiv-cs-cv

MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety

arXiv:2605.01687v1 Announce Type: new Abstract: We present MultiBreak, a scalable and diverse multi-turn jailbreak benchmark to evaluate large language model (LLM) safety. Multi-turn jailbreaks mimic

Multimodal Confidence Modeling in Audio-Visual Quality Assessment

arXiv:2605.01219v1 Announce Type: cross Abstract: Audio-visual quality assessment (AVQA) is essential for streaming, teleconferencing, and immersive media. In realistic streaming scenarios, distortion

applicationsarxiv-cs-cv

Multimodal Data Curation Through Ranked Retrieval

arXiv:2605.01163v1 Announce Type: cross Abstract: Shared embedding spaces are widely used for multimodal search and data curation. In practice, two problems often limit how well this works. First, emb

safetyarxiv-cs-lg

Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong

arXiv:2501.09775v3 Announce Type: replace Abstract: Multiple Choice Question (MCQ) tests are among the most used methods for evaluating large language models (LLMs). Besides checking the correctness o

MultiSense-Pneumo: A Multimodal Learning Framework for Pneumonia Screening in Resource-Constrained Settings

arXiv:2605.02207v1 Announce Type: new Abstract: Pneumonia remains a leading global cause of morbidity and mortality, particularly in low resource settings where access to imaging, laboratory testing,

applicationsarxiv-cs-cv

Tutorials

Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation

arXiv:2605.02471v1 Announce Type: new Abstract: Mapping standing dead trees is crucial for acquiring information on the effects of climate change on forests and forest biodiversity. However, leveragin

tutorialsarxiv-cs-cv

Hardware

MusicInfuser: Making Video Diffusion Listen and Dance

arXiv:2503.14505v3 Announce Type: replace Abstract: We introduce MusicInfuser, an approach that aligns pre-trained text-to-video diffusion models to generate high-quality dance videos synchronized wit

hardwarearxiv-cs-cv

MV-S2V: Multi-View Subject-Consistent Video Generation

arXiv:2601.17756v3 Announce Type: replace Abstract: Existing Subject-to-Video Generation (S2V) methods have achieved high-fidelity and subject-consistent video generation, yet remain constrained to si

applicationsarxiv-cs-cv

MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction

arXiv:2602.03668v2 Announce Type: replace-cross Abstract: Latent actions learned from diverse human videos serve as pseudo-labels for vision-language-action (VLA) pretraining, but provide effective su

NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals

model-releasesarxiv-cs-cv

arXiv:2605.00871v1 Announce Type: cross Abstract: State space models (SSMs) achieve linear-time complexity but struggle with multi-channel physiological signals due to three limitations: fixed kernels

NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals

arXiv:2511.03488v2 Announce Type: replace Abstract: Physiological signals are inherently heterogeneous: they are collected under diverse acquisition setups, differ in the number and type of modalities

applicationsarxiv-cs-lg

Tutorials

Natural Gradient Bayesian Filtering: Geometry-Aware Filter for Dynamical Systems

arXiv:2605.02306v1 Announce Type: new Abstract: Bayesian filtering is a cornerstone of state estimation in complex systems such as aerospace systems, yet exact solutions are available only for linear

tutorialsarxiv-cs-ro

NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks

arXiv:2508.02046v4 Announce Type: replace-cross Abstract: Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isol

safetyarxiv-cs-lg

Agents

Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Bandits

arXiv:2306.04498v3 Announce Type: replace Abstract: We study fair multi-agent multi-armed bandit learning under collision-only coordination. Agents cannot communicate explicitly during learning and ob

agentsarxiv-cs-lg

Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information

arXiv:2502.00204v3 Announce Type: replace Abstract: We study the problem of online learning in Stackelberg games with side information between a leader and a sequence of followers. In every round the

Hardware

Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion

arXiv:2603.10584v2 Announce Type: replace Abstract: We introduce Marigold-SSD, a single-step, late-fusion depth completion framework that leverages strong diffusion priors while eliminating the costly

hardwarearxiv-cs-cv

Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT

model-releasesarxiv-cs-cv

arXiv:2605.01075v1 Announce Type: new Abstract: Propagation-based X-ray phase-contrast imaging (PBI) enables high-contrast visualization of lung structures and holds strong medical potential. However,

Agents

Networked Information Aggregation for Binary Classification

arXiv:2605.01082v1 Announce Type: new Abstract: We study networked binary classification on a directed acyclic graph (DAG) where each agent observes only a subset of the feature columns of a shared da

agentsarxiv-cs-lg

Agents

Neural Backward Reach-Avoid Tubes with MPC Supervision for High-Dimensional Systems: An Application to Safe Spacecraft Docking

arXiv:2605.02021v1 Announce Type: new Abstract: Autonomous spacecraft docking requires control policies that simultaneously ensure collision avoidance and target reachability under coupled, high-dimen

agentsarxiv-cs-ro

Local Ai

Neural Cellular Automata: From Cells to Pixels

arXiv:2506.22899v3 Announce Type: replace Abstract: Neural Cellular Automata (NCAs) are bio-inspired dynamical systems in which identical cells iteratively apply a learned local update rule to self-or

local-aiarxiv-cs-cv

NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Training

arXiv:2605.02044v1 Announce Type: new Abstract: Training neural networks is difficult to interpret, particularly for newcomers. We introduce NeuroViz, an interactive visualization tool that supports r

New Bounds for Kernel Sums via Fast Spherical Embeddings

arXiv:2605.01263v1 Announce Type: cross Abstract: We study query time bounds for the fundamental problem of estimating the kernel mean frac1{|X|}sum_{xin X}mathbf{k}(x,y) of a query y in a finite data

NH-CROP: Robust Pricing for Governed Language Data Assets under Cost Uncertainty

arXiv:2605.01745v1 Announce Type: cross Abstract: Language data are increasingly acquired and governed as assets, yet platforms often price candidate resources before knowing their true privacy or acc

researcharxiv-cs-cl

Noise is All You Need: Solving Linear Inverse Problems by Noise Combination Sampling with Diffusion Models

arXiv:2510.23633v2 Announce Type: replace-cross Abstract: Pretrained diffusion models have demonstrated strong capabilities in zero-shot inverse problem solving by incorporating observation informatio