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A Computational Operationalisation of Competing Maturational Theories of Syntactic Development via Statistical Grammar Induction

arXiv:2605.08476v1 Announce Type: new Abstract: This paper is concerned with what intermediate syntactic categories children acquire during first language development, and in what order. Maturational

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A Controlled Diagnostic Study of Hardware-Induced Distortions in Hardware-Aware Training

DGX agent

arXiv:2605.09416v1 Announce Type: new Abstract: Hardware-aware training (HAT) is widely used to improve the robustness of neural networks on non-ideal AI accelerators, such as analog in-memory computi

researcharxiv-cs-lg

12 May 2026

A low-cost mockup to simulate robotic laser cutting in nuclear decommissioning

arXiv:2605.08947v1 Announce Type: new Abstract: This paper introduces a low-cost experimental mockup to simulate the laser cutting process of containers in nuclear decommissioning. It is composed of a

researcharxiv-cs-ro

A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting

arXiv:2605.09061v1 Announce Type: cross Abstract: Accurate and efficient imbalance electricity price forecasting is critical for industrial energy trading systems, especially as battery assets and aut

A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation

arXiv:2602.00834v4 Announce Type: replace-cross Abstract: Score-based methods are powerful across machine learning, but they face a paradox: theoretically path-independent, yet practically path-depend

A Paired Point-of-Care Ultrasound Dataset for Image Quality Enhancement and Benchmarking via a cGAN Baseline

arXiv:2605.08282v1 Announce Type: cross Abstract: Purpose: We aim to enhance the image quality of point-of-care ultrasound (POCUS) devices using deep learning and a novel paired dataset of POCUS and h

A PyTorch Library of Turing-Complete Neural Networks

arXiv:2605.08150v1 Announce Type: new Abstract: We present a PyTorch package that compiles neural networks and their weights from Turing machine descriptions, producing models that exactly simulate th

A Qualitative Test-Risk Mechanism for Scaling Behavior in Normalized Residual Networks

arXiv:2605.08297v1 Announce Type: cross Abstract: The scaling behavior, in which test performance often improves as model size and data increase, is a central empirical phenomenon in modern deep learn

A Random-Matrix Criterion for Initializing Gated Recurrent Neural Networks

arXiv:2605.10650v1 Announce Type: new Abstract: Proper weight initialization prior to training has historically been one of the key factors that helped kick off the deep learning revolution. Initializ

A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge

arXiv:2605.10592v1 Announce Type: new Abstract: Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows

A Robust Out-of-Distribution Detection Framework via Synergistic Smoothing

arXiv:2605.08191v1 Announce Type: cross Abstract: Reliable out-of-distribution (OOD) detection is a critical requirement for the safe deployment of machine learning systems. Despite recent progress, s

A Single-Layer Model Can Do Language Modeling

arXiv:2605.10643v1 Announce Type: new Abstract: Modern language models scale depth by stacking layers, each holding its own state - a per-layer KV cache in transformers, a per-layer matrix in Mamba, G

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models

arXiv:2605.08504v1 Announce Type: new Abstract: We investigate the origins of massive activations in large language models (LLMs) and identify a specific layer named the extbf{Massive Emergence Layer

A Spectral Framework for Closed-Form Relative Density Estimation

arXiv:2605.10668v1 Announce Type: new Abstract: We propose a closed-form spectral framework for relative log-density estimation in linearly parameterized probabilistic models, including unnormalized a

A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization

arXiv:2605.09094v1 Announce Type: new Abstract: In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing w

A Two-Stage Motion-Aware Framework for mmWave-based Human Mesh Recovery

arXiv:2605.08530v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar has emerged as a promising sensing modality for human perception due to its robustness under challenging environmental co

A Unified Lyapunov-IQC Framework for Uniform Stability of Smooth Quadratic First-Order Accelerated Optimizers

arXiv:2605.08488v1 Announce Type: cross Abstract: We develop a unified Lyapunov-integral quadratic constraint (IQC) framework for establishing uniform stability of first-order accelerated optimization

A Visuo-Tactile Data Collection System with Haptic Feedback for Coarse-to-Fine Imitation Learning

arXiv:2605.08757v1 Announce Type: new Abstract: We present a visuo-tactile data-collection system that generates temporally structured, contact-rich demonstrations for imitation learning. Conventional

researcharxiv-cs-ro

Achieving Better Local Regret Bound for Online Non-Convex Bilevel Optimization

arXiv:2602.06457v2 Announce Type: replace Abstract: Online bilevel optimization (OBO) has emerged as a powerful framework for many machine learning problems. Prior works have developed several algorit

Active Testing of Large Language Models via Approximate Neyman Allocation

arXiv:2605.10075v1 Announce Type: new Abstract: Large language models (LLMs) require reliable evaluation from pre-training to test-time scaling, making evaluation a recurring rather than one-off cost.

Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum

arXiv:2605.09623v1 Announce Type: cross Abstract: In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly. However, existing approaches to DNN p

Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization

arXiv:2510.04988v3 Announce Type: replace Abstract: The vast majority of modern deep learning models are trained with momentum-based first-order optimizers. The momentum term governs the optimizer's m

AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting

arXiv:2605.10239v1 Announce Type: new Abstract: This work explores a simple yet powerful lightweight adapter design for feed-forward 3D Gaussian Splatting (3DGS). Existing methods typically apply comp

Additive Atomic Forests for Symbolic Function and Antiderivative Discovery

arXiv:2605.08130v1 Announce Type: new Abstract: We present a framework for the simultaneous symbolic recovery of a function and its antiderivative from data. The framework rests on three ideas. First,

Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III

arXiv:2605.08161v1 Announce Type: new Abstract: Tumor segmentation in whole-body PET/CT imaging is crucial for precise disease evaluation and treatment planning. However, it remains challenging due to

Affine Tracing: A New Paradigm for Probabilistic Linear Solvers

arXiv:2605.10566v1 Announce Type: cross Abstract: Probabilistic linear solvers (PLSs) return probability distributions that quantify uncertainty due to limited computation in the solution of linear sy

AllocMV: Optimal Resource Allocation for Music Video Generation via Structured Persistent State

arXiv:2605.10723v1 Announce Type: cross Abstract: Generating long-horizon music videos (MVs) is frequently constrained by prohibitive computational costs and difficulty maintaining cross-shot consiste

(alpha,eta)-Stability for Boosting Vector-Valued Prediction

arXiv:2602.18866v2 Announce Type: replace Abstract: Despite the widespread use of boosting in structured prediction, a general theoretical understanding of aggregation beyond scalar prediction remains

Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks

arXiv:2605.10590v1 Announce Type: cross Abstract: Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods fo

An Efficient Token Compression Framework for Visual Object Tracking

arXiv:2605.08329v1 Announce Type: new Abstract: Refining visual representations by eliminating their internal feature-level redundancy is crucial for simultaneously optimizing the performance and comp

An Elastic Shape Variational Autoencoder for Skeleton Pose Trajectories

arXiv:2605.09231v1 Announce Type: new Abstract: Deep generative models provide flexible frameworks for modeling complex, structured data such as images, videos, 3D objects, and texts. However, when ap

An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data

arXiv:2605.08242v1 Announce Type: cross Abstract: Clinical dietary assessment can generate detailed but high-dimensional nutrient and food-group information that is difficult to translate quickly into

ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Models

arXiv:2605.10328v1 Announce Type: new Abstract: A central challenge in large-scale decision-making under incomplete information is estimating reliable probabilities. Recent approaches leverage Large L

Anchor-guided Hypergraph Condensation with Dual-level Discrimination

arXiv:2605.10001v1 Announce Type: new Abstract: The increasing prevalence of large-scale hypergraphs poses significant computational challenges for hypergraph neural network (HNN) training. To address

Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning

arXiv:2605.08764v1 Announce Type: cross Abstract: Deep vision models degrade sharply in low-data regimes, particularly in medical imaging where labeled samples are scarce. We show this arises not mere

Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI

arXiv:2605.09575v1 Announce Type: cross Abstract: Background: Prenatal germinal matrix-intraventricular hemorrhage (GMH-IVH) is a leading cause of infant mortality and neurodevelopmental impairment. M

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study

arXiv:2605.09622v1 Announce Type: cross Abstract: Voxel-wise dose prediction is a critical yet challenging task in practical radiotherapy (RT) planning, as bespoke models trained from scratch often st

APEX: Audio Prototype EXplanations for Classification Tasks

arXiv:2605.10153v1 Announce Type: cross Abstract: Explainable AI (XAI) has achieved remarkable success in image classification, yet the audio domain lacks equally mature solutions. Current methods app

Applying Graph Analysis for Unsupervised Fast Malware Fingerprinting

arXiv:2510.12811v2 Announce Type: replace-cross Abstract: Malware proliferation is increasing at a tremendous rate, with hundreds of thousands of new samples identified daily. Manual investigation of

AQMP: Image compression through Adaptive Quadtree Refinement and Matching Pursuit with Hyperparameter Optimization

arXiv:2605.09190v1 Announce Type: new Abstract: We present AQMP, a novel image codec combining Adaptive Quadtree Refinement with Matching Pursuit. Unlike conventional Matching Pursuit methods that ope

AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models

arXiv:2603.10126v2 Announce Type: replace-cross Abstract: We propose a standalone autoregressive (AR) Action Expert that generates actions as a continuous causal sequence while conditioning on refresh

ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation

arXiv:2502.09891v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs) for solving ques

ATAAT: Adaptive Threat-Aware Adversarial Tuning Framework against Backdoor Attacks on Vision-Language-Action Models

arXiv:2605.08612v1 Announce Type: new Abstract: Addressing the escalating security vulnerabilities in Vision-Language-Action (VLA) models, this study investigates backdoor attacks targeting the visual

researcharxiv-cs-ro

Attention Itself Could Retrieve.RetrieveVGGT: Training-Free Long Context Streaming 3D Reconstruction via Query-Key Similarity Retrieval

arXiv:2605.09644v1 Announce Type: new Abstract: Visual Geometry Grounded Transformer (VGGT) advances 3D reconstruction via scalable Transformer architecture, but the quadratic complexity of global att

Attractor-Vascular Coupling Theory: Formal Grounding and Empirical Validation for AAMI-Standard Cuffless Blood Pressure Estimation from Smartphone Photoplethysmography

arXiv:2605.10871v1 Announce Type: cross Abstract: This work proposes Attractor-Vascular Coupling Theory (AVCT), a mathematical framework showing that cardiac attractor geometry encodes blood pressure

Attribution-based Explanations for Markov Decision Processes

arXiv:2605.09780v1 Announce Type: new Abstract: Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on

AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs

arXiv:2509.08031v3 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient and non-standardized too

Augmented Equivariant Mesh Networks for Anatomical Segmentation

arXiv:2605.08172v1 Announce Type: new Abstract: Anatomical mesh segmentation requires models that operate directly on irregular surface geometry while remaining robust to arbitrary patient pose and me