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arXiv:2604.08626v1 Announce Type: new Abstract: Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection--

applicationsarxiv-cs-cv

Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning

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DGX agent

arXiv:2604.08728v1 Announce Type: new Abstract: Cooperation in multi-agent reinforcement learning (MARL) benefits from inter-agent communication, yet most approaches assume idealized channels and exis

safetyarxiv-cs-lg

13 Apr 2026

WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning

arXiv:2604.08958v1 Announce Type: cross Abstract: Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to

tutorialsarxiv-cs-ai

XFED: Non-Collusive Model Poisoning Attack Against Byzantine-Robust Federated Classifiers

arXiv:2604.09489v1 Announce Type: cross Abstract: Model poisoning attacks pose a significant security threat to Federated Learning (FL). Most existing model poisoning attacks rely on collusion, requir

Yes, But Not Always. Generative AI Needs Nuanced Opt-in

arXiv:2604.09413v1 Announce Type: cross Abstract: This paper argues that a one-size-fits-all approach to specifying consent for the use of creative works in generative AI is insufficient. Real-world o

agentsarxiv-cs-ai

You Can't Fight in Here! This is BBS!

arXiv:2604.09501v1 Announce Type: new Abstract: Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models

tutorialsarxiv-cs-cl

You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector

arXiv:2603.15757v2 Announce Type: replace-cross Abstract: What happens when a pretrained generative robot policy is provided a constant initial noise as input, rather than repeatedly sampling it from

safetyarxiv-cs-ai

Zero-Shot Generative De-identification: Inversion-Free Flow for Privacy-Preserving Skin Image Analysis

arXiv:2602.00821v2 Announce Type: replace Abstract: The secure analysis of dermatological images in clinical environments is fundamentally restricted by the critical trade-off between patient privacy

researcharxiv-cs-cv

3DrawAgent: Teaching LLM to Draw in 3D with Early Contrastive Experience

model-releasesarxiv-cs-cv

arXiv:2604.08042v1 Announce Type: new Abstract: Sketching in 3D space enables expressive reasoning about shape, structure, and spatial relationships, yet generating 3D sketches through natural languag

A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset

model-releasesarxiv-cs-lg

arXiv:2604.06227v1 Announce Type: new Abstract: Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developi

A Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs

arXiv:2604.04614v2 Announce Type: replace-cross Abstract: Deep learning-based modeling of multimodal Electronic Health Records (EHRs) has become an important approach for clinical diagnosis and risk p

safetyarxiv-cs-ai

A comparative analysis of machine learning models in SHAP analysis

arXiv:2604.07258v1 Announce Type: new Abstract: In this growing age of data and technology, large black-box models are becoming the norm due to their ability to handle vast amounts of data and learn i

tutorialsarxiv-cs-lg

Applications

A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction

arXiv:2604.06207v1 Announce Type: cross Abstract: This paper investigates demonstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs), ai

applicationsarxiv-cs-ai

A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions

arXiv:2501.16150v3 Announce Type: replace Abstract: Agents for computer use (ACUs) are an emerging class of systems capable of executing complex tasks on digital devices -- such as desktops, mobile ph

agentsarxiv-cs-ai

A Data-Informed Variational Clustering Framework for Noisy High-Dimensional Data

model-releasesarxiv-cs-lg

arXiv:2604.06864v1 Announce Type: cross Abstract: Clustering in high-dimensional settings with severe feature noise remains challenging, especially when only a small subset of dimensions is informativ

Applications

A Decomposition Perspective to Long-context Reasoning for LLMs

arXiv:2604.07981v1 Announce Type: new Abstract: Long-context reasoning is essential for complex real-world applications, yet remains a significant challenge for Large Language Models (LLMs). Despite t

applicationsarxiv-cs-cl

A First Guess is Rarely the Final Answer: Learning to Search in the Travelling Salesperson Problem

arXiv:2604.06940v1 Announce Type: cross Abstract: Most neural solvers for the Traveling Salesperson Problem (TSP) are trained to output a single solution, even though practitioners rarely stop there:

safetyarxiv-cs-ai

A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection

arXiv:2604.08381v1 Announce Type: new Abstract: Sarcasm is a rhetorical device that expresses criticism or emphasizes characteristics of certain individuals or situations through exaggeration, irony,

researcharxiv-cs-cl

A Generalized Sinkhorn Algorithm for Mean-Field Schrodinger Bridge

arXiv:2604.06531v2 Announce Type: cross Abstract: The mean-field Schrodinger bridge (MFSB) problem concerns designing a minimum-effort controller that guides a diffusion process with nonlocal intera

agentsarxiv-cs-lg

A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation

arXiv:2402.12797v4 Announce Type: replace Abstract: We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations. Our method processes all skeletal points as a

researcharxiv-cs-cv

A Giant-Step Baby-Step Classifier For Scalable and Real-Time Anomaly Detection In Industrial Control Systems and Water Treatment Systems

arXiv:2504.20906v4 Announce Type: replace-cross Abstract: The continuous monitoring of the interactions between cyber-physical components of any industrial control system (ICS) is required to secure a

safetyarxiv-cs-lg

A Goal-Oriented Chatbot for Engaging the Elderly Through Family Photo Conversations

arXiv:2604.06184v1 Announce Type: cross Abstract: We propose a personalized chatbot designed for elderly individuals. The chatbot initiates discussions based on family photos, encouraging users to int

researcharxiv-cs-ai

A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM

arXiv:2604.06666v1 Announce Type: cross Abstract: Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating

researcharxiv-cs-ai

Hardware

A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures

arXiv:2602.03604v3 Announce Type: replace-cross Abstract: We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEPAs

hardwarearxiv-cs-ai

A-MBER: Affective Memory Benchmark for Emotion Recognition

arXiv:2604.07017v1 Announce Type: new Abstract: AI assistants that interact with users over time need to interpret the user's current emotional state in order to respond appropriately and personally.

A Novel Automatic Framework for Speaker Drift Detection in Synthesized Speech

arXiv:2604.06327v1 Announce Type: cross Abstract: Recent diffusion-based text-to-speech (TTS) models achieve high naturalness and expressiveness, yet often suffer from speaker drift, a subtle, gradual

A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP

arXiv:2604.06650v1 Announce Type: cross Abstract: Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at

A Physical Agentic Loop for Language-Guided Grasping with Execution-State Monitoring

arXiv:2604.07395v1 Announce Type: cross Abstract: Robotic manipulation systems that follow language instructions often execute grasp primitives in a largely single-shot manner: a model proposes an act

safetyarxiv-cs-cv

A Severity-Based Curriculum Learning Strategy for Arabic Medical Text Generation

arXiv:2604.06365v1 Announce Type: cross Abstract: Arabic medical text generation is increasingly needed to help users interpret symptoms and access general health guidance in their native language. Ne

tutorialsarxiv-cs-ai

A-SLIP: Acoustic Sensing for Continuous In-hand Slip Estimation

arXiv:2604.08528v1 Announce Type: new Abstract: Reliable in-hand manipulation requires accurate real-time estimation of slip between a gripper and a grasped object. Existing tactile sensing approaches

researcharxiv-cs-ro

A Soft Robotic Interface for Chick-Robot Affective Interactions

arXiv:2604.08443v1 Announce Type: new Abstract: The potential of Animal-Robot Interaction (ARI) in welfare applications depends on how much an animal perceives a robotic agent as socially relevant, no

agentsarxiv-cs-ro

A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics

arXiv:2604.07129v1 Announce Type: cross Abstract: Numerical simulation of wave propagation and run-up is a cornerstone of coastal engineering and tsunami hazard assessment. However, applying these for

A Spatial-Spectral-Frequency Interactive Network for Multimodal Remote Sensing Classification

model-releasesarxiv-cs-cv

arXiv:2510.04628v2 Announce Type: replace Abstract: Deep learning-based methods have achieved significant success in remote sensing Earth observation data analysis. Numerous feature fusion techniques

A Study of LLMs' Preferences for Libraries and Programming Languages

arXiv:2503.17181v3 Announce Type: replace-cross Abstract: Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntact

researcharxiv-cs-ai

A systematic framework for generating novel experimental hypotheses from language models

arXiv:2408.05086v3 Announce Type: replace Abstract: Neural language models (LMs) have been shown to capture complex linguistic patterns, yet their utility in understanding human language and more broa

safetyarxiv-cs-cl

A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering

arXiv:2604.07274v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in medical question answering; however, purely parametric models often suffer from

A Unified Multi-Layer Framework for Skill Acquisition from Imperfect Human Demonstrations

arXiv:2604.08341v1 Announce Type: new Abstract: Current Human-Robot Interaction (HRI) systems for skill teaching are fragmented, and existing approaches in the literature do not offer a cohesive frame

safetyarxiv-cs-ro

ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning

arXiv:2604.08050v1 Announce Type: new Abstract: In this study, we focus on video captioning by fully open multimodal large language models (MLLMs). The comprehension of visual sequences is challenging

researcharxiv-cs-cv

Accelerating 4D Hyperspectral Imaging through Physics-Informed Neural Representation and Adaptive Sampling

arXiv:2604.06561v1 Announce Type: cross Abstract: High-dimensional hyperspectral imaging (HSI) enables the visualization of ultrafast molecular dynamics and complex, heterogeneous spectra. However, ap

Accuracy Improvement of Semi-Supervised Segmentation Using Supervised ClassMix and Sup-Unsup Feature Discriminator

arXiv:2604.07122v1 Announce Type: cross Abstract: In semantic segmentation, the creation of pixel-level labels for training data incurs significant costs. To address this problem, semi-supervised lear

ACIArena: Toward Unified Evaluation for Agent Cascading Injection

model-releasesarxiv-cs-cl

arXiv:2604.07775v1 Announce Type: cross Abstract: Collaboration and information sharing empower Multi-Agent Systems (MAS) but also introduce a critical security risk known as Agent Cascading Injection

Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

arXiv:2604.08545v1 Announce Type: new Abstract: The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a pro

agentsarxiv-cs-cv

Action Without Interaction: Probing the Physical Foundations of Video LMMs via Contact-Release Detection

model-releasesarxiv-cs-cv

arXiv:2511.20162v2 Announce Type: replace Abstract: Large multi-modal models (LMMs) show increasing performance in realistic visual tasks for images and, more recently, for videos. For example, given

Active Reward Machine Inference From Raw State Trajectories

arXiv:2604.07480v1 Announce Type: new Abstract: Reward machines are automaton-like structures that capture the memory required to accomplish a multi-stage task. When combined with reinforcement learni

safetyarxiv-cs-ro

Active Statistical Inference

arXiv:2403.03208v3 Announce Type: replace-cross Abstract: Inspired by the concept of active learning, we propose active inferencenicode{x2013}a methodology for statistical inference with machine-l

ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration

arXiv:2604.08534v1 Announce Type: new Abstract: Large-scale real-world robot data collection is a prerequisite for bringing robots into everyday deployment. However, existing pipelines often rely on s

safetyarxiv-cs-ro

AdaBoost Does Not Always Cycle: A Computer-Assisted Counterexample

model-releasesarxiv-cs-lg

arXiv:2604.07055v1 Announce Type: new Abstract: We give a computer-assisted counterexample to the open question, posed by Rudin, Schapire, and Daubechies in COLT 2012, of whether exhaustive AdaBoost a

ADAG: Automatically Describing Attribution Graphs

model-releasesarxiv-cs-cl

arXiv:2604.07615v1 Announce Type: new Abstract: In language model interpretability research, extbf{circuit tracing} aims to identify which internal features causally contributed to a particular outp