Search: “applications”

Multi-output Extreme Spatial Model for Complex Aircraft Production Systems

arXiv:2604.22548v1 Announce Type: cross Abstract: Problem definition: Data-driven models in machine learning have enabled efficient management of production systems. However, a majority of machine lea

27 Apr 2026

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Research

NRGS: Neural Regularization for Robust 3D Semantic Gaussian Splatting

DGX agent

arXiv:2604.22439v1 Announce Type: new Abstract: We propose a neural regularization method that refines the noisy 3D semantic field produced by lifting multi-view inconsistent 2D features, in order to

researcharxiv-cs-cv

27 Apr 2026

UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents

model-releasesarxiv-cs-cl

arXiv:2602.07038v2 Announce Type: replace-cross Abstract: Key Information Extraction (KIE) from real-world documents remains challenging due to substantial variations in layout structures, visual qual

27 Apr 2026

DMAP: A Distribution Map for Text

arXiv:2602.11871v2 Announce Type: replace Abstract: Large Language Models (LLMs) are a powerful tool for statistical text analysis, with derived sequences of next-token probability distributions offer

researcharxiv-cs-cl

GFlowState: Visualizing the Training of Generative Flow Networks Beyond the Reward

arXiv:2604.21830v1 Announce Type: new Abstract: We present GFlowState, a visual analytics system designed to illuminate the training process of Generative Flow Networks (GFlowNets or GFNs). GFlowNets

Low-Rank Adaptation Redux for Large Models

arXiv:2604.21905v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation

On the algebra of Koopman eigenfunctions and on some of their infinities

arXiv:2604.21825v1 Announce Type: cross Abstract: For continuous-time dynamical systems with reversible trajectories, the nowhere-vanishing eigenfunctions of the Koopman operator of the system form a

Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis

model-releasesarxiv-cs-ai

arXiv:2511.11439v2 Announce Type: replace-cross Abstract: Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics. However, the performance ofte

The CriticalSet problem: Identifying Critical Contributors in Bipartite Dependency Networks

arXiv:2604.21537v1 Announce Type: new Abstract: Identifying critical nodes in complex networks is a fundamental task in graph mining. Yet, methods addressing an all-or-nothing coverage mechanics in a

The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview

model-releasesarxiv-cs-ai

arXiv:2604.21312v1 Announce Type: cross Abstract: This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The

Hardware

Algorithm and Hardware Co-Design for Efficient Complex-Valued Uncertainty Estimation

arXiv:2604.19993v1 Announce Type: cross Abstract: Complex-Valued Neural Networks (CVNNs) have significant advantages in handling tasks that involve complex numbers. However, existing CVNNs are unable

hardwarearxiv-cs-lg

Confidence-Based Mesh Extraction from 3D Gaussians

arXiv:2603.24725v2 Announce Type: replace Abstract: Recently, 3D Gaussian Splatting (3DGS) greatly accelerated mesh extraction from posed images due to its explicit representation and fast software ra

Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models

arXiv:2604.01965v2 Announce Type: replace-cross Abstract: Scientific knowledge discovery increasingly relies on large language models, yet many existing scholarly assistants depend on proprietary syst

Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories

arXiv:2604.20797v1 Announce Type: cross Abstract: Local gauge symmetry underlies fundamental interactions and strongly correlated quantum matter, yet existing machine-learning approaches lack a genera

Learning When Not to Decide: A Framework for Overcoming Factual Presumptuousness in AI Adjudication

model-releasesarxiv-cs-ai

arXiv:2604.19895v1 Announce Type: new Abstract: A well-known limitation of AI systems is presumptuousness: the tendency of AI systems to provide confident answers when information may be lacking. This

Separable Pathways for Causal Reasoning: How Architectural Scaffolding Enables Hypothesis-Space Restructuring in LLM Agents

arXiv:2604.20039v1 Announce Type: new Abstract: Causal discovery through experimentation and intervention is fundamental to robust problem solving. It requires not just updating beliefs within a fixed

Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning

arXiv:2604.20308v1 Announce Type: new Abstract: Graph neural networks face two fundamental challenges rooted in the linear structure of Euclidean vector spaces: (1) Current architectures represent geo

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models

arXiv:2508.18609v4 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is a critical strategy for efficient Large Language Models (LLMs) deployment. However, existing scaling laws

Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams

arXiv:2507.06803v3 Announce Type: replace-cross Abstract: This paper contributes to speeding up the design and deployment of engineering dynamical systems by proposing a strategy for exploiting domain

Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges

arXiv:2604.19788v1 Announce Type: new Abstract: As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of

Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization

arXiv:2604.08404v2 Announce Type: replace Abstract: Out-of-distribution (OoD) generalization occurs when representation learning encounters a distribution shift. This occurs frequently in practice whe

Agents

PLaMo 2.1-VL Technical Report

arXiv:2604.19324v1 Announce Type: cross Abstract: We introduce PLaMo 2.1-VL, a lightweight Vision Language Model (VLM) for autonomous devices, available in 8B and 2B variants and designed for local an

agentsarxiv-cs-ai

Regression with Large Language Models for Materials and Molecular Property Prediction

arXiv:2409.06080v2 Announce Type: replace-cross Abstract: We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviatio

Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs

arXiv:2410.03294v4 Announce Type: replace Abstract: This study addresses the deployment challenges of integer-only quantized Transformers on resource-constrained embedded FPGAs (Xilinx Spartan-7 XC7S1

RL-ABC: Reinforcement Learning for Accelerator Beamline Control

arXiv:2604.19146v1 Announce Type: new Abstract: Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLA

SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension

model-releasesarxiv-cs-cl

arXiv:2508.01959v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units f

SketchFaceGS: Real-Time Sketch-Driven Face Editing and Generation with Gaussian Splatting

arXiv:2604.19202v1 Announce Type: cross Abstract: 3D Gaussian representations have emerged as a powerful paradigm for digital head modeling, achieving photorealistic quality with real-time rendering.

Local Ai

Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation

arXiv:2407.05102v2 Announce Type: replace-cross Abstract: Executing flow estimation using Deep Learning (DL)-based soft sensors on resource-limited IoT devices has demonstrated promise in terms of rel

local-aiarxiv-cs-ai

Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model

arXiv:2604.19635v1 Announce Type: cross Abstract: While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in

A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations

arXiv:2502.13570v4 Announce Type: replace-cross Abstract: Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics

Advancing Vision Transformer with Enhanced Spatial Priors

arXiv:2604.18549v1 Announce Type: new Abstract: In recent years, the Vision Transformer (ViT) has garnered significant attention within the computer vision community. However, the core component of Vi

Attention Is not Everything: Efficient Alternatives for Vision

arXiv:2604.17439v1 Announce Type: new Abstract: Recently computer vision has seen advancements mainly thanks to Transformer-based models. However many non-Transformer methods are still doing well bein

AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction

arXiv:2510.15339v3 Announce Type: replace Abstract: Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However

safetyarxiv-cs-cl

AvatarPointillist: AutoRegressive 4D Gaussian Avatarization

arXiv:2604.04787v2 Announce Type: replace Abstract: We introduce AvatarPointillist, a novel framework for generating dynamic 4D Gaussian avatars from a single portrait image. At the core of our method

Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting

arXiv:2604.18492v1 Announce Type: new Abstract: This paper proposes a multi-step probabilistic forecasting framework using a single neural-network based model to generate simultaneous point and interv

BasketHAR: A Multimodal Dataset for Human Activity Recognition and Sport Analysis in Basketball Training Scenarios

model-releasesarxiv-cs-cv

arXiv:2604.17065v1 Announce Type: new Abstract: Human Activity Recognition (HAR) involves the automatic identification of user activities and has gained significant research interest due to its broad

Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer

arXiv:2604.16563v1 Announce Type: new Abstract: Systolic murmurs are extra heart sounds that occur during the contraction phase of the cardiac cycle, often indicating heart abnormalities caused by tur

DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty

arXiv:2506.12622v2 Announce Type: replace Abstract: Deep reinforcement learning (RL) has achieved remarkable success, yet its deployment in real-world scenarios is often limited by vulnerability to en

Flow marching for a generative PDE foundation model

arXiv:2509.18611v2 Announce Type: replace Abstract: Pretraining on large-scale collections of PDE-governed spatiotemporal trajectories has recently shown promise for building generalizable models of d

Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving

arXiv:2604.16436v1 Announce Type: cross Abstract: This paper develops an end-to-end fuzzy encoder-decoder architecture for enhancing vision-based multi-modal deep spiking Q-networks in autonomous driv

Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations

arXiv:2604.18083v1 Announce Type: new Abstract: Reconstructing continuous environmental fields from sparse and irregular observations remains a central challenge in environmental modelling and biodive

Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging

arXiv:2604.17694v1 Announce Type: cross Abstract: Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We

Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models

arXiv:2512.02636v3 Announce Type: replace-cross Abstract: Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and

L1 Regularization Paths in Linear Models by Parametric Gaussian Message Passing

arXiv:2604.16949v1 Announce Type: new Abstract: The paper considers the computation of L1 regularization paths in a state space setting, which includes L1 regularized Kalman smoothing, linear SVM, LAS

Late Fusion Neural Operators for Extrapolation Across Parameter Space in Partial Differential Equations

arXiv:2604.16721v1 Announce Type: new Abstract: Developing neural operators that accurately predict the behavior of systems governed by partial differential equations (PDEs) across unseen parameter re

LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations

model-releasesarxiv-cs-cl

arXiv:2509.12539v2 Announce Type: replace-cross Abstract: We present LEAF ('Lightweight Embedding Alignment Framework'), a knowledge distillation framework for text embedding models. A key distinguish

On The Mathematics of the Natural Physics of Optimization

arXiv:2604.17645v1 Announce Type: cross Abstract: A number of optimization algorithms have been inspired by the physics of Newtonian motion. Here, we ask the question: do algorithms themselves obey so

Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference

arXiv:2604.18319v1 Announce Type: cross Abstract: Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to