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Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming

arXiv:2604.19670v1 Announce Type: cross Abstract: Effective human-robot teaming is crucial for the practical deployment of robots in human workspaces. However, optimizing joint human-robot plans remai

tutorialsarxiv-cs-ai

Multi-Domain Learning with Global Expert Mapping

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DGX agent

arXiv:2604.18842v1 Announce Type: new Abstract: Human perception generalizes well across different domains, but most vision models struggle beyond their training data. This gap motivates multi-dataset

model-releasesarxiv-cs-cv

22 Apr 2026

Multi-Gait Learning for Humanoid Robots Using Reinforcement Learning with Selective Adversarial Motion Prior

arXiv:2604.19102v1 Announce Type: cross Abstract: Learning diverse locomotion skills for humanoid robots in a unified reinforcement learning framework remains challenging due to the conflicting requir

Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis

arXiv:2604.18765v1 Announce Type: cross Abstract: Fault detection and diagnosis are critical for the optimal and safe operation of industrial processes. The correlations among sensors often display no

local-aiarxiv-cs-ai

Multi-modal Reasoning with LLMs for Visual Semantic Arithmetic

arXiv:2604.19567v1 Announce Type: new Abstract: Reinforcement learning (RL) as post-training is crucial for enhancing the reasoning ability of large language models (LLMs) in coding and math. However,

Multi-modal Test-time Adaptation via Adaptive Probabilistic Gaussian Calibration

arXiv:2604.19093v1 Announce Type: cross Abstract: Multi-modal test-time adaptation (TTA) enhances the resilience of benchmark multi-modal models against distribution shifts by leveraging the unlabeled

Agents

Multi-Step Gaussian Process Propagation for Adaptive Path Planning

arXiv:2604.19148v1 Announce Type: new Abstract: Efficient and robust path planning hinges on combining all accessible information sources. In particular, the task of path planning for robotic environm

agentsarxiv-cs-ro

Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge

arXiv:2603.11665v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across vario

safetyarxiv-cs-cl

Applications

Multi-view Crowd Tracking Transformer with View-Ground Interactions Under Large Real-World Scenes

arXiv:2604.19318v1 Announce Type: new Abstract: Multi-view crowd tracking estimates each person's tracking trajectories on the ground of the scene. Recent research works mainly rely on CNNs-based mult

applicationsarxiv-cs-cv

Multiclass Local Calibration with the Jensen-Shannon Distance

arXiv:2510.26566v2 Announce Type: replace-cross Abstract: Developing trustworthy Machine Learning (ML) models requires their predicted probabilities to be well-calibrated, meaning they should reflect

Multilingual Language Models Encode Script Over Linguistic Structure

model-releasesarxiv-cs-cl

arXiv:2604.05090v2 Announce Type: replace Abstract: Multilingual language models (LMs) organize representations for typologically and orthographically diverse languages into a shared parameter space,

Multimodal embodiment-aware navigation transformer

arXiv:2604.19267v1 Announce Type: new Abstract: Goal-conditioned navigation models for ground robots trained using supervised learning show promising zero-shot transfer, but their collision-avoidance

safetyarxiv-cs-ro

Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties

arXiv:2604.19383v1 Announce Type: cross Abstract: Metal-organic frameworks (MOFs) are a major target of machine-learning-based property prediction, yet most models assume that a single framework repre

tutorialsarxiv-cs-ai

NemeSys: Toward Online Underwater Exploration with Remote Operator-in-the-loop Adaptive Autonomy

model-releasesarxiv-cs-ro

arXiv:2507.11889v2 Announce Type: replace Abstract: Adaptive mission control and dynamic parameter reconfiguration are essential for autonomous underwater vehicles (AUVs) operating in GPS-denied, comm

NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence

arXiv:2604.18637v1 Announce Type: cross Abstract: Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a worksho

Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems

arXiv:2604.18611v1 Announce Type: cross Abstract: Anomaly detection in nuclear industrial control systems (ICS) requires continuous, energy-efficient monitoring across multiple subsystems that are oft

Nexusformer: Nonlinear Attention Expansion for Stable and Inheritable Transformer Scaling

arXiv:2604.19147v1 Announce Type: cross Abstract: Scaling Transformers typically necessitates training larger models from scratch, as standard architectures struggle to expand without discarding learn

Nonmonotone subgradient methods based on a local descent lemma

arXiv:2510.19341v2 Announce Type: replace-cross Abstract: In this paper we present a nonmonotone line search subgradient algorithm tailored to upper-C^2 functions. This is a family of nonsmooth and no

ODMA: On-Demand Memory Allocation Strategy for LLM Serving on LPDDR-Class Accelerators

arXiv:2512.09427v5 Announce Type: replace-cross Abstract: Existing memory management techniques severely hinder efficient Large Language Model serving on accelerators constrained by poor random-access

OLLM: Options-based Large Language Models

arXiv:2604.19087v1 Announce Type: new Abstract: We introduce Options LLM (OLLM), a simple, general method that replaces the single next-token prediction of standard LLMs with a extit{set of learned op

Agents

OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration

arXiv:2505.11765v3 Announce Type: replace-cross Abstract: Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recentl

agentsarxiv-cs-ai

OmniGen2: Towards Instruction-Aligned Multimodal Generation

arXiv:2506.18871v4 Announce Type: replace-cross Abstract: In this work, we introduce OmniGen2, a versatile and open-source generative model designed to provide a unified solution for diverse generatio

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

arXiv:2604.18827v1 Announce Type: cross Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to mode

OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models

arXiv:2502.16161v2 Announce Type: replace-cross Abstract: Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understandin

researcharxiv-cs-cl

OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models

arXiv:2604.00688v3 Announce Type: replace Abstract: We present OmniVoice, a massively multilingual zero-shot text-to-speech (TTS) model that scales to over 600 languages. At its core is a novel diffus

researcharxiv-cs-cl

On Accelerating Grounded Code Development for Research

arXiv:2604.19022v1 Announce Type: new Abstract: A major challenge for niche scientific and technical domains in leveraging coding agents is the lack of access to up-to-date, domain- specific knowledge

On Solving the Multiple Variable Gapped Longest Common Subsequence Problem

arXiv:2604.18645v1 Announce Type: new Abstract: This paper addresses the Variable Gapped Longest Common Subsequence (VGLCS) problem, a generalization of the classical LCS problem involving flexible ga

Applications

On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation

arXiv:2601.13729v2 Announce Type: replace Abstract: In recent years, the non-deterministic properties of language models have garnered considerable attention and have shown a significant influence on

applicationsarxiv-cs-cl

On the Conditioning Consistency Gap in Conditional Neural Processes

arXiv:2604.19312v1 Announce Type: new Abstract: Neural processes are meta-learning models that map context sets to predictive distributions. While inspired by stochastic processes, NPs do not generall

On the Derivation of Tightly-Coupled LiDAR-Inertial Odometry with VoxelMap

arXiv:2603.15471v2 Announce Type: replace Abstract: This note presents a concise mathematical formulation of tightly-coupled LiDAR-Inertial Odometry within an iterated error-state Kalman filter framew

researcharxiv-cs-ro

On the Generalizability of Foundation Models for Crop Type Mapping

arXiv:2409.09451v5 Announce Type: replace Abstract: Foundation models pre-trained using self-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, includi

safetyarxiv-cs-cv

On the Spatiotemporal Dynamics of Generalization in Neural Networks

arXiv:2602.01651v2 Announce Type: replace-cross Abstract: Why do neural networks fail to generalize addition from 16-digit to 32-digit numbers, while a child who learns the rule can apply it to arbitr

local-aiarxiv-cs-ai

On two ways to use determinantal point processes for Monte Carlo integration

arXiv:2604.19698v1 Announce Type: new Abstract: The standard Monte Carlo estimator widehat{I}_N^{MC} of int fdomega relies on independent samples from omega and has variance of order 1/N. Replacing th

Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2602.05437v2 Announce Type: replace Abstract: Vision-language models (VLMs) can achieve high accuracy while still accepting culturally plausible but visually incorrect interpretations. Existing

One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization

arXiv:2601.18572v2 Announce Type: replace Abstract: Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfair outcomes ac

safetyarxiv-cs-cl

One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models

arXiv:2604.18839v1 Announce Type: cross Abstract: Looped transformers scale computational depth without increasing parameter count by repeatedly applying a shared transformer block and can be used for

Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernels

arXiv:2506.18186v3 Announce Type: replace Abstract: The restless multi-armed bandit (RMAB) framework is a popular approach to solving resource allocation problems in networked systems. In this paper,

safetyarxiv-cs-lg

Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents

arXiv:2604.00555v2 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory complianc

local-aiarxiv-cs-ai

Opinion de-polarization in social networks with GNNs

arXiv:2412.09404v3 Announce Type: replace-cross Abstract: Nowadays, social media is the ground for political debate and exchange of opinions. There is a significant amount of research that suggests th

Optimal Exploration of New Products under Assortment Decisions

arXiv:2604.18800v1 Announce Type: cross Abstract: We study online learning for new products on a platform that makes capacity-constrained assortment decisions on which products to offer. For a newly l

tutorialsarxiv-cs-lg

Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?

arXiv:2604.19399v1 Announce Type: new Abstract: Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources. Communication in each FL round typically con

local-aiarxiv-cs-lg

Optimized Architectures for Kolmogorov-Arnold Networks

arXiv:2512.12448v2 Announce Type: replace Abstract: Efforts to improve Kolmogorov--Arnold networks (KANs) with architectural enhancements have been stymied by the complexity those enhancements bring,

tutorialsarxiv-cs-lg

ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models

arXiv:2509.15435v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) exhibit strong multimodal capabilities but remain vulnerable to hallucinations from intrinsic errors and

Owner-Harm: A Missing Threat Model for AI Agent Safety

arXiv:2604.18658v1 Announce Type: cross Abstract: Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a d

Agents

PanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Driving

arXiv:2604.19379v1 Announce Type: new Abstract: This paper presents the first study on Unsupervised Domain Adaptation (UDA) for multimodal 3D panoptic segmentation (mm-3DPS), aiming to improve general

agentsarxiv-cs-cv

Paparazzo: Active Mapping of Moving 3D Objects

model-releasesarxiv-cs-cv

arXiv:2604.19556v1 Announce Type: new Abstract: Current 3D mapping pipelines generally assume static environments, which limits their ability to accurately capture and reconstruct moving objects. To a

Applications

ParamBoost: Gradient Boosted Piecewise Cubic Polynomials

arXiv:2604.18864v1 Announce Type: new Abstract: Generalized Additive Models (GAMs) can be used to create non-linear glass-box (i.e. explicitly interpretable) models, where the predictive function is f

applicationsarxiv-cs-lg

Pause or Fabricate? Training Language Models for Grounded Reasoning

arXiv:2604.19656v1 Announce Type: new Abstract: Large language models have achieved remarkable progress on complex reasoning tasks. However, they often implicitly fabricate information when inputs are

researcharxiv-cs-cl