Search: “applications”

A general tensor-structured compression scheme for efficient large language models

arXiv:2605.25344v1 Announce Type: cross Abstract: Large language models (LLMs) are dominated by dense linear transformations, whose storage, memory and computational overheads hinder efficient adaptat

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Tutorials

A perspective on fluid mechanical environments for challenges in reinforcement learning

DGX agent

arXiv:2605.25011v1 Announce Type: new Abstract: We consider the challenge of developing agents that efficiently interact with high-dimensional, evolving environments, towards a view of practical reinf

tutorialsarxiv-cs-lg

26 May 2026

A Principled Self-Referenced Early Stopping Approach for Deep Image Prior

arXiv:2605.25299v1 Announce Type: cross Abstract: Recently, Deep Image Prior (DIP) has demonstrated strong capabilities for solving inverse imaging problems (IIPs) by optimizing a randomly initialized

A Tertiary Review of Large Language Model-Based Code Generating Tasks: Trends, Challenges, and Future Directions

arXiv:2605.25536v1 Announce Type: cross Abstract: Context. Large language models (LLMs) are increasingly applied to code-generating tasks (CGTs) in software engineering. While reported results are pro

Accelerating Bayesian inverse design in computational fluid dynamics using neural operators

arXiv:2605.26059v1 Announce Type: cross Abstract: Bayesian inverse design provides a principled framework for inferring aerodynamic geometries from sparse flow observations while quantifying uncertain

Advancing Graph Few-Shot Learning via In-Context Learning

arXiv:2605.24410v1 Announce Type: new Abstract: Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning

Affinity Graph Connectivity in Convex Clustering

arXiv:2605.24673v1 Announce Type: cross Abstract: We generalize finite-sample bounds for convex clustering to the setting where affinity weights appearing in the objective correspond to a general conn

AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions

arXiv:2605.25707v1 Announce Type: new Abstract: Autonomous computer use agents that powered by multimodal large language models (MLLMs) are emerging as capable assistants for completing complex digita

AnyScene: Towards Highly Controllable Driving Scene Generation at Anywhere and Beyond

arXiv:2605.26113v1 Announce Type: new Abstract: Generating high-fidelity and controllable synthetic data is critical for advancing end-to-end autonomous driving, particularly for addressing the long t

safetyarxiv-cs-ro

Agents

Architecting Agentic Communities using Design Patterns

arXiv:2601.03624v3 Announce Type: replace Abstract: The rapid evolution of Large Language Models (LLM) and subsequent Agentic AI technologies requires systematic architectural guidance for building so

agentsarxiv-cs-ai

Agents

Attested Tool-Server Admission: A Security Extension to the Model Context Protocol

arXiv:2605.24248v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) standardizes how a large-language-model (LLM) agent and an external tool server exchange messages, but not trust: a h

agentsarxiv-cs-ai

Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors

arXiv:2605.23938v1 Announce Type: new Abstract: Large language models (LLMs) increasingly fuse heterogeneous inputs in ubiquitous systems. Yet, how LLMs implicitly allocate authority when sensor measu

Authority Signals in Claude AI Health Citations: A Descriptive Analysis Using the Authority Signals Framework

arXiv:2605.23921v1 Announce Type: cross Abstract: This study seeks to determine the authority signals used by Anthropic's Claude AI in its presentation of sources when answering consumer health questi

AvAtar: Learning to Align via Active Optimal Transport

arXiv:2605.24395v1 Announce Type: new Abstract: Alignment plays a fundamental role in many machine learning problems, such as multi-network analysis, multimodal learning, and point cloud registration.

safetyarxiv-cs-lg

Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization

arXiv:2605.25129v1 Announce Type: new Abstract: Diffusion models have shown promise in learning to solve constraint optimization problems. However, they are mostly restricted to problems with binary v

safetyarxiv-cs-lg

Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models

arXiv:2605.24053v1 Announce Type: new Abstract: Large Language Models (LLMs) are predominantly governed by probabilistic frameworks in which the sum of outcome probabilities is constrained to unity. T

Agents

Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks

arXiv:2510.19731v3 Announce Type: replace-cross Abstract: HAPS are emerging as key enablers in the evolution of 6G wireless networks, bridging terrestrial and non-terrestrial infrastructures. Operatin

agentsarxiv-cs-lg

Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei

arXiv:2601.05004v2 Announce Type: replace Abstract: Self-destructive behaviors are linked to complex psychological states and can be challenging to diagnose. These behaviors may be even harder to iden

safetyarxiv-cs-cl

Capability and Robustness Cannot Both Be Free: An Information-Theoretic Bound for Vision-Language-Action Models

arXiv:2605.25889v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are increasingly deployed on real robots, where each predicted action is executed and each failure carries a safet

safetyarxiv-cs-lg

Cluster Frequency Conformal Prediction for Local Coverage

arXiv:2605.24872v1 Announce Type: new Abstract: Conformal prediction provides distribution-free coverage guarantees, but in many-class classification it may still under-cover specific classes or subpo

Complement Submodular Information Measures for Balanced and Robust Data Selection

arXiv:2605.24779v1 Announce Type: cross Abstract: Submodular optimization has become a fundamental paradigm for data selection, retrieval, summarization, and representation learning due to its ability

Continual Speaker Identity Unlearning with Minimal Interference

arXiv:2605.25962v1 Announce Type: cross Abstract: Machine unlearning removes designated concepts or knowledge from pre-trained models. Recent work has extended this paradigm to speaker identity unlear

Convex-Neural RRT*: Fast and Reliable Learning-Guided Sampling for High-Quality Robot Path Planning

arXiv:2605.25006v1 Announce Type: cross Abstract: Sampling-based algorithms for robot path planning offer probabilistic completeness and strong empirical convergence properties across environments wit

DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models

arXiv:2605.26038v1 Announce Type: cross Abstract: Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objec

EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization

arXiv:2605.25395v1 Announce Type: new Abstract: Lookahead-based acceleration methods, such as Nesterov's momentum, are widely used in optimization, but they often become unreliable in deep learning tr

Emergent Analogical Reasoning in Transformers

arXiv:2602.01992v4 Announce Type: replace Abstract: Analogy is a central faculty of human intelligence, enabling abstract patterns discovered in one domain to be applied to another. Despite its centra

Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection

arXiv:2508.09801v3 Announce Type: replace-cross Abstract: Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive tech

Tutorials

Fermi-Dirac machines as quantizations of neurons

arXiv:2605.24386v1 Announce Type: cross Abstract: Fermi-Dirac machines were proposed recently as an approach to solving semidefinite optimization problems on quantum computers. Here, we reinterpret th

tutorialsarxiv-cs-lg

Fine-Tuning Masked Diffusion for Provable Self-Correction

arXiv:2510.01384v4 Announce Type: replace Abstract: A natural desideratum for generative models is self-correction--detecting and revising low-quality tokens at inference. While Masked Diffusion Model

From Index to Equity: Pre-Training Transformers for Stock Return Prediction

arXiv:2605.23962v1 Announce Type: cross Abstract: This research aims to leverage machine learning to improve stock price prediction and support informed investment decisions related to buying, selling

Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion Model

arXiv:2412.07333v2 Announce Type: replace-cross Abstract: Pose-Guided Person Image Synthesis (PGPIS) aims to generate human images in specified poses while preserving the identity and appearance of a

Fuzzy PyTorch: Rapid Numerical Variability Evaluation for Deep Learning Models

arXiv:2605.25991v1 Announce Type: new Abstract: We introduce Fuzzy PyTorch, a framework for rapid evaluation of numerical variability in deep learning (DL) models. As DL is increasingly applied to div

Generative modeling of granular flow on inclined planes using conditional flow matching

arXiv:2604.04453v2 Announce Type: replace-cross Abstract: Granular flows govern many natural and industrial processes, yet their interior kinematics and mechanics remain largely unobservable, as exper

GeoSVG-RL: Geometry-Aware Reinforcement Learning for Layout-Constrained Text-to-SVG Diagram Generation

arXiv:2605.25447v1 Announce Type: new Abstract: Generating structured, editable diagrams remains a significant challenge for contemporary large language models, despite their proficiency in general-pu

safetyarxiv-cs-cl

Hardware

H^{2}MT: Semantic Hierarchy-Aware Hierarchical Memory Transformer

arXiv:2605.24930v1 Announce Type: new Abstract: Transformer-based LLMs achieve strong results on many language tasks; however, long inputs remain challenging because context windows are finite, and pr

hardwarearxiv-cs-cl

How does Bayesian Sampling help Membership Inference Attacks?

arXiv:2503.07482v2 Announce Type: replace-cross Abstract: Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Existing state-of

Hyperspectral Image Data Reduction for Endmember Extraction

arXiv:2512.10506v3 Announce Type: replace-cross Abstract: Endmember extraction from hyperspectral images aims to identify the spectral signatures of materials present in a scene. Recent studies have s

Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach

arXiv:2605.23924v1 Announce Type: new Abstract: Segment-level disclosures are a central component of financial reporting, providing insight into firms' internal organization and the allocation of econ

safetyarxiv-cs-cl

Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System

arXiv:2406.04374v2 Announce Type: replace-cross Abstract: Recommender systems play a crucial role in internet economies by connecting users with relevant products. However, designing effective recomme

IsaacIPC: Coupling High-Fidelity Simulation and Realistic Rendering for Contact-Rich Robotic Systems

arXiv:2605.24339v1 Announce Type: new Abstract: We present IsaacIPC, a robotic simulation framework that couples GPU accelerated incremental potential contact (IPC) with IsaacSim/Lab. IsaacIPC maps si

safetyarxiv-cs-ro

IV-Net: A neural network for elliptic PDEs with random and highly varying coefficients

arXiv:2605.24876v1 Announce Type: cross Abstract: We introduce a novel neural operator architecture designed to approximate solutions of linear elliptic partial differential equations with high-contra

LAPLEX: The FFT of Learnable Laplace Kernels

arXiv:2605.24584v1 Announce Type: cross Abstract: Fast linear algebra in deep learning usually comes with a choice: fixed geometry and exact computation, as in the Fourier transform, or adaptive geome

Lean Formalization of Generalization Error Bound by Rademacher Complexity and Dudley's Entropy Integral

arXiv:2503.19605v5 Announce Type: replace-cross Abstract: Understanding and certifying the generalization performance of machine learning algorithms -- i.e. obtaining theoretical estimates of the test

researcharxiv-cs-cl

Learning manifold diffusion semigroups from graph transition matrices

arXiv:2605.25383v1 Announce Type: cross Abstract: We consider graph diffusion processes constructed from finite i.i.d. samples drawn from an unknown manifold embedded in ambient Euclidean space, where

Hardware

Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm

arXiv:2602.05052v3 Announce Type: replace Abstract: We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variat

hardwarearxiv-cs-lg

Learning Sparse Compositional Functions with Norm-Constrained Neural Networks

arXiv:2605.25608v1 Announce Type: cross Abstract: The ability of deep neural networks to learn hierarchical features is widely regarded as a key mechanism underlying their success in high-dimensional

Local Ai

Local MAP Sampling for Diffusion Models

arXiv:2510.07343v3 Announce Type: replace-cross Abstract: Diffusion Posterior Sampling (DPS) provides a principled Bayesian approach to inverse problems by sampling from p(x_0 mid y). While posterior

local-aiarxiv-cs-ai

Memory-Induced Tool-Drift in LLM Agents

arXiv:2605.24941v1 Announce Type: cross Abstract: Modern LLM agents combine long-term memory for personalization with tool-calling interfaces for taking actions in the world -- a combination underpinn