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Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification

arXiv:2606.03593v1 Announce Type: cross Abstract: Embodied AI systems are increasingly deployed in open-world environments, yet ensuring their reliability remains a fundamental challenge. Drawing on d

applicationsarxiv-cs-ro

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Mamba-Enhanced Implicit Motion Learning for Audio-Driven Portrait Animation

DGX agent

arXiv:2606.03402v1 Announce Type: new Abstract: Audio-driven human motion video generation aims to synthesize realistic and temporally coherent human animations from a single static image, with applic

researcharxiv-cs-cv

3 Jun 2026

Margin Play: A Multi-Agent System For Public Policy Analysis In The Brazilian Equatorial Margin

arXiv:2606.02614v1 Announce Type: cross Abstract: The Brazilian Equatorial Margin (BEM) is Brazil's next offshore oil frontier, with operations expected to begin in 2026 in the Foz do Amazonas basin.

MariData: One-Step Unpaired Image Translation for Maritime Environments

arXiv:2606.03246v1 Announce Type: new Abstract: The development on robust perception systems for Maritime Autonomous Surface Ships (MASS) is heavily constrained by the scarcity of diverse training dat

agentsarxiv-cs-cv

MARIO: Motion-Augmented Real-Time Multi-Sensor Inertial Odometry

model-releasesarxiv-cs-cv

arXiv:2606.02996v1 Announce Type: cross Abstract: Inertial odometry (IO) using only Inertial Measurement Units (IMUs) provides a lightweight solution for human motion tracking in augmented reality (AR

Measuring Weak-to-Strong Legibility of Reasoning Models

arXiv:2603.20508v2 Announce Type: replace-cross Abstract: Reasoning language models (RLMs) and the intermediate chains of thought they emit play an increasingly central role in multi-agent setups such

MedCUA-Bench: A Screenshot-Only Benchmark for Clinical Computer-Use Agents

arXiv:2606.03203v1 Announce Type: new Abstract: Computer-use agents could automate repetitive screen-based clinical work, but their reliability in medical graphical user interfaces remains largely unv

MemoGen: Can Past Experience Improve Future Text-to-Image Generation?

model-releasesarxiv-cs-cv

arXiv:2606.03243v1 Announce Type: new Abstract: Modern text-to-image models have achieved strong visual synthesis, yet remain unreliable when prompts require implicit visual constraints, relational re

Memory Retrieval for Changing Preferences

arXiv:2606.02976v1 Announce Type: new Abstract: Long-context dialogue systems must decide both when to access memory and which parts of the interaction history are relevant. Existing approaches typica

researcharxiv-cs-cl

MemTrain: Self-Supervised Context Memory Training

arXiv:2606.03197v1 Announce Type: new Abstract: Memory is an indispensable capability for long-horizon LLM agents, enabling them to preserve and utilize information accumulated across extended interac

agentsarxiv-cs-cl

MemVerse: Multimodal Memory for Lifelong Learning Agents

arXiv:2512.03627v2 Announce Type: replace Abstract: Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember. Withou

Merit or networks? What decides where research is published

arXiv:2606.03763v1 Announce Type: cross Abstract: Does scientific publishing reward the quality of ideas or the advantage of connections? The question is universal to prestige-driven science, yet it h

applicationsarxiv-cs-ai

Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective

arXiv:2606.03290v1 Announce Type: cross Abstract: Graph Foundation Models (GFMs), built upon the Pre-training and Adaptation paradigm, have emerged as a research hotspot in graph learning. For GNN-bas

MetaWorld: Scaling Multi-Agent Video World Model from Single-view Video Data

arXiv:2606.02753v1 Announce Type: cross Abstract: Video world models are a foundational generative technology for embodied AI and the Metaverse, yet existing approaches are inherently limited to a sin

MIND: Multi-rationale INtegrated Discriminative Reasoning Framework for Multi-modal Large Models

arXiv:2512.05530v2 Announce Type: replace Abstract: Recently, multimodal large language models (MLLMs) have been widely applied to reasoning tasks. However, they suffer from limited multi-rationale se

Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning

arXiv:2603.03480v2 Announce Type: replace Abstract: We study reinforcement learning with delayed state observation, where the agent observes the current state after some random number of time steps. W

agentsarxiv-cs-lg

Mitigating False Credit Propagation: Probabilistic Graphical Reward Aggregation for Rubric-Based Reinforcement Learning

arXiv:2606.03361v1 Announce Type: new Abstract: Rubric-based rewards are increasingly used for open-ended language model post-training, but criterion-level scores are often aggregated as independent u

safetyarxiv-cs-lg

Mitigating Spurious Correlations with Memorization-Guided Dataset De-Biasing

arXiv:2606.02830v1 Announce Type: new Abstract: Real-world datasets often contain spurious correlations that are not causally related to the target label. When such correlations dominate the majority

applicationsarxiv-cs-lg

Mixed-Modality Dual Face-Hair Retrieval

model-releasesarxiv-cs-cv

arXiv:2606.03470v1 Announce Type: new Abstract: We introduce Dual Face-Hair Retrieval (DFHR), a new mixed-modality dual-reference task in image retrieval where a query consists of a face image specify

MLP Splatting: Object-Centric Neural Fields

arXiv:2606.03877v1 Announce Type: new Abstract: 3D representations are fundamental to scene rendering, understanding, and interaction. Recent approaches, such as 3D Gaussian Splatting and Neural Radia

researcharxiv-cs-cv

MLSkip: Data Skipping for ML Filters via Lightweight Metadata

arXiv:2606.03946v1 Announce Type: cross Abstract: Database vendors recently released AI functions that can be used in filter predicates. As such functions often rely on costly, black-box ML models, th

ModuLoop : Low-Level Code Generation using Modular Synthesizer and Closed-Loop Debugger for Robotic Control

arXiv:2606.03047v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive performance across various domains, including code generation and problem solving. However, th

applicationsarxiv-cs-ro

Hardware

MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency

arXiv:2606.03014v1 Announce Type: new Abstract: Mixture-of-Agents (MoA) systems improve reasoning accuracy by routing each query to multiple expert LLMs and aggregating their outputs. Efficiently exec

hardwarearxiv-cs-lg

Motion Planning in Dynamic Environments: A Survey from Classical to Modern Methods

arXiv:2606.02677v1 Announce Type: new Abstract: Motion planning in dynamic environments requires robots to continuously adapt their paths in response to environmental changes for safe and uninterrupte

researcharxiv-cs-ro

MuLoCo: Muon is a practical inner optimizer for DiLoCo

arXiv:2505.23725v3 Announce Type: replace Abstract: DiLoCo is a powerful framework for training large language models (LLMs), enabling larger optimal batch sizes and increased accelerator utilization

researcharxiv-cs-lg

Multi-component Causal Tracing in Large Language Models

arXiv:2606.03085v1 Announce Type: cross Abstract: Causal tracing systematically intervenes on a large language model's (LLM's) internal representations to uncover and quantify the causal pathways link

safetyarxiv-cs-cl

Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification

arXiv:2606.03322v1 Announce Type: cross Abstract: The graphical representation of the brain offers critical insights into diagnosing and prognosing neurodegenerative disease via relationships between

Multi-Modal Machine Learning for Breast Cancer Recurrence Prediction

arXiv:2606.02892v1 Announce Type: new Abstract: Breast cancer recurrence, a leading cause of long-term mortality among survivors, requires timely and accurate risk assessment to guide follow-up care a

Multi-Robot Bearing-only Pose Estimation via Angle Rigidity

arXiv:2606.03931v1 Announce Type: new Abstract: This letter proposes a novel distributed bearing-based pose estimator for time-varying multi-robot systems. The method uses angles computed from body-fr

researcharxiv-cs-ro

Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving

arXiv:2606.02964v1 Announce Type: cross Abstract: Large Language Model (LLM) inference relies on key-value (KV) caches to avoid redundant attention computation. While approximate KV cache retention te

safetyarxiv-cs-cl

Multi^2: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments

arXiv:2606.03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynam

Multilingual Unlearning in LLMs: Transfer, Dynamics, and Reversibility

model-releasesarxiv-cs-cl

arXiv:2606.03291v1 Announce Type: new Abstract: Large language models (LLMs) can memorize sensitive facts, motivating unlearning methods that remove targeted knowledge without costly retraining. Howev

Multiple Choice Learning of Low-Rank Adapters for Language Modeling

arXiv:2507.10419v3 Announce Type: replace-cross Abstract: We propose LoRA-MCL, a training scheme that extends next-token prediction in language models with a method designed to decode diverse, plausib

MultiTurnPSB: Evaluating Multi-Turn Jailbreak Attacks an dClassifier-Based Defenses for Medical AI Safety

arXiv:2606.02630v1 Announce Type: cross Abstract: Patient-facing medical chatbots are commonly evaluated on single-turn prompts, yet real users push back after refusals, add urgency, and invoke author

MUSE: A Unified Agentic Harness for MLLMs

arXiv:2606.03005v1 Announce Type: cross Abstract: Despite rapid progress, multimodal large language models (MLLMs) still fail on tasks that humans solve effortlessly, such as navigating a grid maze fr

agentsarxiv-cs-ai

Names Don't Matter: Symbol-Invariant Transformer for Open-Vocabulary Learning

arXiv:2601.23169v2 Announce Type: replace Abstract: Current neural architectures lack a principled way to handle interchangeable tokens, i.e., symbols that are semantically equivalent yet distinguisha

researcharxiv-cs-lg

Local Ai

NetKV: Network-Aware Decode Instance Selection for Disaggregated LLM Inference

arXiv:2606.03910v1 Announce Type: cross Abstract: Disaggregated LLM inference forces the KV cache to traverse the datacenter network before decoding begins, so transfer time enters directly into the T

local-aiarxiv-cs-ai

Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models

arXiv:2602.03681v2 Announce Type: replace Abstract: The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios. In contrast, linear attention mode

researcharxiv-cs-cl

Local Ai

Neural Fields as World Models

arXiv:2602.18690v2 Announce Type: replace-cross Abstract: Humans rehearse possible futures offline, as in mental practice and perhaps dreaming, suggesting that world models may support task learning a

local-aiarxiv-cs-cv

Neural Navigation Functions for Zero-Shot Generalizable Motion Planning

arXiv:2606.03756v1 Announce Type: cross Abstract: We introduce Neural Navigation Functions (Neural-NF), a learned reactive navigation function capable of zero-shot transfer across unseen environment g

Neural Networks Provably Learn Spectral Representations for Group Composition

arXiv:2606.02993v1 Announce Type: new Abstract: Understanding how structured internal structure emerges during neural network training is central to the study of deep learning. We investigate this phe

safetyarxiv-cs-lg

NeuroArmor: Safe-Variant-Guided Representation Consistency for Selective Re-Anchoring in Jailbreak Defense

arXiv:2606.03486v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak attacks that hide harmful intent behind seemingly ordinary requests such as role-play, translatio

Neuron Populations Exhibit Divergent Selectivity with Scale

arXiv:2606.03990v1 Announce Type: cross Abstract: We investigate whether neuron populations within neural networks evolve predictably with scale, extending scaling laws beyond macroscopic observables

applicationsarxiv-cs-cl

Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstruction

arXiv:2606.02788v1 Announce Type: cross Abstract: Reconstructing the direction of incoming neutrinos in the IceCube Neutrino Observatory is an important problem in astrophysics. The public IceCube--Ne

NewtPhys: Do Foundation Models Understand Newtonian Physics?

arXiv:2606.03986v1 Announce Type: new Abstract: Previous work has evaluated physics reasoning in foundation models using synthetic or semi-synthetic scenes and visual question-answering tasks. However

applicationsarxiv-cs-cv

Non-Identical Diffusion Models in MIMO-OFDM Channel Generation

arXiv:2509.01641v3 Announce Type: replace-cross Abstract: We propose a novel diffusion model, termed the non-identical diffusion model, and investigate its application to wireless orthogonal frequency

Hardware

NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics

arXiv:2606.03551v1 Announce Type: new Abstract: Simulation has become a core infrastructure for robotics research. Unlike previous simulators, NVIDIA Isaac Sim leverages GPU acceleration to enable lar

hardwarearxiv-cs-ro

NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation

arXiv:2606.03159v1 Announce Type: cross Abstract: As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-lo