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3DPhysVideo: Consistency-Guided Flow SDE for Video Generation via 3D Scene Reconstruction and Physical Simulation

arXiv:2605.16795v1 Announce Type: cross Abstract: Video generative models have made remarkable progress, yet they often yield visual artifacts that violate grounding in physical dynamics. Recent works

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4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving

DGX agent

arXiv:2605.18074v1 Announce Type: new Abstract: We present 4DLidarOpen, a large-scale open multi-modal dataset for autonomous driving, centered on 4D frequency-modulated continuous-wave (FMCW) Lidar s

model-releasesarxiv-cs-ro

19 May 2026

A Comprehensive Survey of Action Quality Assessment: Method and Benchmark

model-releasesarxiv-cs-cv

arXiv:2412.11149v2 Announce Type: replace Abstract: Action Quality Assessment (AQA) aims to automatically evaluate how well human actions are performed and has been widely applied in sports analysis,

A Conditional U-Net Pipeline with Pre- and Post-Processing for Aerial RGB-to-Thermal Image Translation

arXiv:2605.17564v1 Announce Type: new Abstract: Paired RGB-thermal data has shown significant utility across a range of applications, including image fusion, object tracking, and anomaly detection; ho

A Conflict-aware Evidential Framework for Reliable Sleep Stage Classification

arXiv:2605.17021v1 Announce Type: new Abstract: Multi-view learning has been widely applied for sleep stage classification using multi-modal data. However, existing methods typically assume that diffe

applicationsarxiv-cs-ai

A Critical Assessment of PINNs and Operator Learning for Geotechnical Engineering

arXiv:2512.24365v2 Announce Type: replace-cross Abstract: Scientific machine learning (SciML) offers neural-network alternatives to numerical workflows in geotechnical engineering. This paper benchmar

A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAMDelta Integration into Upcycled MoE

model-releasesarxiv-cs-cl

arXiv:2605.18083v1 Announce Type: new Abstract: Expanding Large Language Models~(LLMs) to new languages is a costly endeavor, demanding extensive Continued Pre-Training~(CPT) and data-intensive alignm

A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation

arXiv:2605.18436v1 Announce Type: new Abstract: A large amount of musical heritage has been digitised by memory institutions: libraries, museums, and archives. Nevertheless, the field of Optical Music

A Dexterous and Compliant Gripper With Soft Hydraulic Actuation for Microgravity Manipulation

arXiv:2605.17851v1 Announce Type: new Abstract: Astrobee's existing one-degree-of-freedom (DOF) underactuated compliant claw gripper enables perching on the International Space Station (ISS), but prov

researcharxiv-cs-ro

A Distributional View for Visual Mechanistic Interpretability: KL-Minimal Soft-Constraint Principle

arXiv:2605.17504v1 Announce Type: cross Abstract: Most current paradigms in visual mechanistic interpretability (MI) remain confined to interpreting internal units of the vision model via heuristic me

A Feature-Driven Framework for Software Fault Prediction

arXiv:2605.17611v1 Announce Type: cross Abstract: Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software qua

Safety

A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights

arXiv:2605.16913v1 Announce Type: cross Abstract: Neural networks trained with gradient-based methods exhibit a strong simplicity bias: they learn simpler statistical features of their data before mov

safetyarxiv-cs-lg

A Global-Local Graph Attention Network for Traffic Forecasting

arXiv:2605.16726v1 Announce Type: new Abstract: Traffic forecasting is a significant part of intelligent transportation systems. One of the critical challenges of traffic forecasting is to find spatio

applicationsarxiv-cs-ai

A Holistic Method for Superquadric Fitting Using Unsupervised Clustering Analysis

arXiv:2605.16779v1 Announce Type: cross Abstract: This work presents a novel method for fitting superquadrics to point clouds under the contamination of noise and outliers, which has many applications

A Hybrid Gaussian Process Regression Framework for Stable Volatility-Covariance Estimation: Evidence from Global Equity Indices

arXiv:2605.17275v1 Announce Type: cross Abstract: Accurate forecasting of the Volatility-Covariance Matrix (VCV) is central to regulatory capital adequacy processes such as the Internal Capital Adequa

A Large-Scale Study on the Accuracy vs Cost Trade-offs of Training and Evaluation Settings in Fine-Grained Image Recognition

arXiv:2605.18700v1 Announce Type: new Abstract: Prior work on fine-grained image recognition (FGIR) has established the importance of the backbone selection, but has neglected the accuracy-vs-cost tra

A Machine Learning Framework for EEG-Based Prediction of Treatment Efficacy in Chronic Neck Pain

arXiv:2605.16326v1 Announce Type: cross Abstract: Chronic neck pain is a leading cause of disability worldwide, and current treatment selection remains largely trial and error. We present a machine le

applicationsarxiv-cs-ai

A Machine With Human-Like Memory Systems

arXiv:2204.01611v3 Announce Type: replace Abstract: Inspired by the cognitive science theory, we explicitly model an agent with both semantic and episodic memory systems, and show that it is better th

A Machine with Short-Term, Episodic, and Semantic Memory Systems

arXiv:2212.02098v5 Announce Type: replace Abstract: Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memo

Agents

A Mechanistic Model for Collective Motion from Sensorimotor Regularities

arXiv:2605.16522v1 Announce Type: new Abstract: Collective behavior in animals has long been modeled through self-propelled particle models, which reproduce striking group-level phenomena through abst

agentsarxiv-cs-ro

A More Word-like Image Tokenization for MLLMs

arXiv:2605.17954v1 Announce Type: cross Abstract: Modern multimodal large language models (MLLMs) typically keep the language model fixed and train a visual projector that maps the pixels into a seque

A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification

arXiv:2605.16383v1 Announce Type: cross Abstract: Deep neural networks achieve high accuracy on image classification tasks. Yet, they often produce overconfident predictions as which fail to express e

A New Perspective on Precision and Recall for Generative Models

arXiv:2511.02414v2 Announce Type: replace Abstract: With the recent success of generative models in image and text, the question of their evaluation has recently gained a lot of attention. While most

A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?

arXiv:2605.18666v1 Announce Type: new Abstract: Gradient-based adversarial attacks subtly manipulate inputs of Machine Learning (ML) models to induce incorrect predictions. This paper investigates whe

A note on connections between the Follmer process and the denoising diffusion probabilistic model

arXiv:2605.18040v1 Announce Type: cross Abstract: The Follmer process is a Brownian motion conditioned to have a pre-specified distribution at time 1. This process can be interpreted as an 'augmented'

A Novel Model for 3D Motion Planning for a Generalized Dubins Vehicle with Pitch and Yaw Rate Constraints

arXiv:2509.24143v2 Announce Type: replace Abstract: In this paper, we propose a new modeling approach and a fast algorithm for 3D motion planning, applicable for fixed-wing unmanned aerial vehicles. T

researcharxiv-cs-ro

A Pilot Benchmark for NL-to-FOL Translation in Planetary Exploration

model-releasesarxiv-cs-cl

arXiv:2605.17911v1 Announce Type: new Abstract: Future planetary exploration envisions autonomous robotic agents operating under severe communication constraints, without global positioning, and with

A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy

arXiv:2605.18511v1 Announce Type: new Abstract: A lightweight and reproducible denoising pipeline for high-throughput Raman spectroscopy is presented. The approach relies on a one-dimensional convolut

A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems

arXiv:2605.16344v1 Announce Type: cross Abstract: Large-scale recommenders encode multi-objective trade-offs by combining multiple predicted outcomes into a single utility score. Although this utility

A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback

arXiv:2605.18073v1 Announce Type: cross Abstract: Large Language Models (LLMs) demonstrate strong potential for automated code generation, yet their ability to iteratively refine solutions using execu

A Randomized Algorithm for Sparse PCA based on the Basic SDP Relaxation

arXiv:2507.09148v2 Announce Type: replace-cross Abstract: Sparse Principal Component Analysis (SPCA) is a fundamental technique for dimensionality reduction, and is NP-hard. In this paper, we introduc

applicationsarxiv-cs-lg

A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability

arXiv:2605.18750v1 Announce Type: cross Abstract: Pipeline parallelism is a key technique for scaling large-model training, but modern workloads exhibit runtime variability in computation and communic

A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks

arXiv:2512.04329v2 Announce Type: replace Abstract: Reusing existing neural-network components is central to research efficiency, yet discovering, extracting, and validating such modules across thousa

A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research

arXiv:2605.16654v1 Announce Type: cross Abstract: Manner and result verbs encode different aspects of event structure and have been discussed in developmental work as a potentially informative distinc

A semantic mutation metric for metamorphic relation adequacy in scientific computing programs

arXiv:2605.17437v1 Announce Type: cross Abstract: Context. Metamorphic Testing addresses the test-oracle problem in scientific computing, but classical Mutation Score operates on syntactic AST mutatio

A simple approach for biometrics: Finger-knuckle prints recognition based on a Sobel filter and similarity measures

arXiv:2605.17673v1 Announce Type: new Abstract: The objective of this work is to propose a novel methodology for the finger knuckle print recognition, which is essentially a digital photo of the finge

Safety

A Simplex Witness Certificate for Constant Collapse in Variational Autoencoders

arXiv:2605.18224v1 Announce Type: cross Abstract: This note studies exact constant collapse in variational autoencoders, where the encoder mean becomes independent of the input. The goal is to make th

safetyarxiv-cs-ai

A Sliced Learning Framework for Online Disturbance Identification in Quadrotor SO(3) Attitude Control

arXiv:2508.14422v4 Announce Type: replace-cross Abstract: This paper introduces a dimension-decomposed geometric learning framework called Sliced Learning for disturbance identification in quadrotor g

applicationsarxiv-cs-ro

A Structural Threshold in Decision Capacity Governs Collapse in Self-Play Reinforcement Learning

arXiv:2605.16315v1 Announce Type: cross Abstract: We show that a threshold in decision capacity determines whether self-play reinforcement learning agents collapse under asymmetric rule perturbations.

A Survey on Foundation Models for Personalized Federated Intelligence

arXiv:2505.06907v2 Announce Type: replace Abstract: The rise of large language models (LLMs), such as ChatGPT, Gemini, and Grok, has reshaped the AI landscape. As prominent instances of foundational m

A Systematic Analysis of Out-of-Distribution Detection Under Representation and Training Paradigm Shifts

model-releasesarxiv-cs-cv

arXiv:2511.11934v3 Announce Type: replace-cross Abstract: We present a systematic benchmark of out-of-distribution (OOD) detection CSFs through a representation-centric lens. Our study spans CNN and V

A Systematic Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation

arXiv:2605.17131v1 Announce Type: cross Abstract: Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity. However, its

A Theory of Training Profit-Optimal LLMs

arXiv:2605.16430v1 Announce Type: cross Abstract: Scaling LLMs requires tremendous computational resources, and recent advances in AI have gone hand in hand with massive amounts of capital expenditure

A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows

arXiv:2605.17808v1 Announce Type: new Abstract: We develop a unified theoretical framework for data-free one-step sampling from unnormalized target distributions based on Wasserstein gradient flows. F

A Unified Framework for Structured Flow Modeling: From Continuous Fields to Data-Driven Representations

arXiv:2605.18250v1 Announce Type: cross Abstract: Many dynamical systems can be described in terms of structured flows combining source/sink behavior, cyclic dynamics, and topology-constrained transpo

Safety

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks

arXiv:2504.14820v2 Announce Type: replace Abstract: For peg-in-hole tasks, humans rely on binocular visual perception to locate the peg above the hole surface and then proceed with insertion. This pap

safetyarxiv-cs-ro

A2RBench: An Automatic Paradigm for Formally Verifiable Abstract Reasoning Benchmark Generation

arXiv:2605.17278v1 Announce Type: new Abstract: Abstract reasoning ability reflects the intelligence and generalization capacity of LLMs to extract and apply abstract rules. However, accurately measur

AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge

arXiv:2408.17352v2 Announce Type: replace-cross Abstract: Automatic Speaker Verification (ASV) systems, which identify speakers based on their voice characteristics, have numerous applications, such a