Search: “applications”

Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments

arXiv:2605.15734v1 Announce Type: new Abstract: The use of large language models to assess user states in conversational and adaptive systems is based on the assumption that the metrics used for such

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Centralized vs Decentralized Federated Learning: A trade-off performance analysis

arXiv:2605.16089v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed edge devices while preserving data pri

researcharxiv-cs-ai

Constrained MPC-Based Motion Planning for Morphing Quadrotors in Ultra-Narrow Passages under Limited Perception

arXiv:2605.15999v1 Announce Type: new Abstract: This paper introduces a motion planning framework to plan morphology and trajectory for morphing quadrotors under extremely constrained environments. We

safetyarxiv-cs-ro

Context-aware Entity-Relation Extraction for Threat Intelligence Knowledge Graphs

arXiv:2605.15904v1 Announce Type: new Abstract: Cybersecurity Knowledge Graphs (CKGs) unify diverse Cyber Threat Intelligence (CTI) sources into structured, queryable formats, offering scalable soluti

CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models

arXiv:2605.15549v1 Announce Type: cross Abstract: The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, desi

safetyarxiv-cs-ai

Tutorials

Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation

arXiv:2605.15254v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) combine deep learning with physical constraints for solving partial differential equations (PDEs), and are wide

tutorialsarxiv-cs-lg

Decentralized LoRA augmented transformer with multi-scale feature learning for secured eye diagnosis

model-releasesarxiv-cs-cv

arXiv:2505.06982v3 Announce Type: replace Abstract: Accurate and privacy-preserving diagnosis of ophthalmic diseases remains a critical challenge in medical imaging, particularly given the limitations

Tutorials

Deep Learning Alternatives of the Kolmogorov Superposition Theorem

arXiv:2410.01990v3 Announce Type: replace Abstract: This paper explores alternative formulations of the Kolmogorov Superposition Theorem (KST) as a foundation for neural network design. The original K

tutorialsarxiv-cs-lg

Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models

arXiv:2503.00794v2 Announce Type: replace Abstract: Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where pre

safetyarxiv-cs-ro

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection

model-releasesarxiv-cs-cl

arXiv:2605.15518v1 Announce Type: new Abstract: The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misus

Deterministic Coreset for Lp Subspace

arXiv:2601.00361v3 Announce Type: replace-cross Abstract: We introduce the first iterative algorithm for constructing a arepsilon-coreset that guarantees deterministic ell_p subspace embedding for any

Discretizing Group-Convolutional Neural Networks for 3D Geometry in Feature Space

arXiv:2605.15368v1 Announce Type: new Abstract: Group-convolutional neural networks (GCNNs) are among the most important methods for introducing symmetry as an inductive bias in deep learning: In each

safetyarxiv-cs-cv

EgoExo-WM: Unlocking Exo Video for Ego World Models

arXiv:2605.15477v1 Announce Type: new Abstract: Egocentric world models present a promising direction for enabling agents to predict and plan, but their performance is constrained by the limited avail

safetyarxiv-cs-cv

Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes

arXiv:2605.15586v1 Announce Type: cross Abstract: Complementary-label learning (CLL) is a weakly supervised paradigm where instances are labeled with classes they do not belong to. Despite a decade of

safetyarxiv-cs-ai

EndoGSim: Physics-Aware 4D Dynamic Endoscopic Scene Simulations via MLLM-Guided Gaussian Splatting

arXiv:2605.16022v1 Announce Type: new Abstract: In robot-assisted minimally invasive surgery, high-fidelity dynamic endoscopic scene reconstruction and simulation are crucial to enhancing downstream t

researcharxiv-cs-cv

Entropy-Based Characterisation of the Polarised Regime in Latent Variable Models

arXiv:2605.15965v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) often exhibit a polarised regime in which latent variables separate into active, passive, and mixed subsets. Existing cr

f-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data

arXiv:2605.15417v1 Announce Type: cross Abstract: In GFlowNets and variational inference, it has been shown that the mean square error between target and model log probabilities is an effective, low v

safetyarxiv-cs-ai

Fair outputs, Biased Internals: Causal Potency and Asymmetry of Latent Bias in LLMs for High-Stakes Decisions

arXiv:2605.15217v1 Announce Type: new Abstract: Instruction-tuned language models exhibit behavioural fairness in high-stakes decisions while retaining biased associations in their internal representa

Hardware

FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization

arXiv:2605.15824v1 Announce Type: new Abstract: Human-centric video customization, particularly at the garment level, has shown significant commercial value. However, existing approaches cannot suppor

hardwarearxiv-cs-cv

Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions

arXiv:2605.15355v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are biologically inspired energy-efficient models that use sparse binary spike-based communication between neurons, makin

Hardware

Fine-Tuning NVIDIA Cosmos Predict 2.5 with LoRA/DoRA for Robot Video Generation

This guide demonstrates how to fine-tune NVIDIA's Cosmos Predict 2.5 video generation model using parameter-efficient techniques like LoRA (Low-Rank Adaptation) and DoRA (Mixture of Experts-based adap

hardwarehugging-face

From Layers to Networks: Comparing Neural Representations via Diffusion Geometry

arXiv:2605.15901v1 Announce Type: new Abstract: Diffusion geometry is a manifold learning framework that uses random walks defined by Markov transition matrices to characterize the geometry of a datas

From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI

arXiv:2506.22440v2 Announce Type: replace-cross Abstract: This paper introduces the Generality-Accuracy-Simplicity (GAS) framework to analyze how large language models (LLMs) are reshaping organizatio

From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks

arXiv:2605.15328v1 Announce Type: new Abstract: Fully Connected Neural Networks (FCNNs) are often regarded as simple and intuitive architectures, yet they serve as the foundation for more complex mode

GenShield: Unified Detection and Artifact Correction for AI-Generated Images

arXiv:2605.16122v1 Announce Type: cross Abstract: Diffusion-based image synthesis has made AI-generated images (AIGI) increasingly photorealistic, raising urgent concerns about authenticity in applica

Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces

model-releasesarxiv-cs-cv

arXiv:2605.15753v1 Announce Type: cross Abstract: Functional 3D scene graphs offer a versatile and flexible representation for 3D scene understanding and robotic manipulation, defined by object nodes,

Tutorials

Integrate Atlassian Confluence Cloud with Amazon Quick

In this post, you will learn how to set up the Confluence Cloud integration with Quick. This includes creating a knowledge base for semantic search, setting up Actions to query and manage Confluence p

tutorialsaws-ml-blog

Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation

arXiv:2605.15789v1 Announce Type: new Abstract: Uncertainty quantification is essential in safety-critical settings--from autonomous driving to aviation, finance, and health--where decisions must rely

Learning in Structured Stackelberg Games

arXiv:2504.09006v4 Announce Type: replace-cross Abstract: We initiate the study of structured Stackelberg games, a novel form of strategic interaction between a leader and a follower where contextual

Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision

model-releasesarxiv-cs-ro

arXiv:2604.02812v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have recently demonstrated strong capabilities in mapping multimodal observations to robot behaviors. However, most cu

Linked Multi-Model Data on Russian Domestic and Foreign Policy Speeches

arXiv:2605.15886v1 Announce Type: new Abstract: This paper introduces a dataset of interlinked multimodal political communications from the Russian government, addressing persistent deficiencies in th

safetyarxiv-cs-cl

Neural Activation Patterns Across Language Model Architectures: A Comprehensive Analysis of Cognitive Task Performance

arXiv:2605.15436v1 Announce Type: new Abstract: This paper presents a comprehensive analysis of neural activation patterns across six distinct large language model (LLM) architectures, examining their

researcharxiv-cs-cl

Local Ai

Neural Policy Composition from Free Energy Minimization

arXiv:2512.04745v3 Announce Type: replace-cross Abstract: The ability to flexibly compose previously acquired skills to execute intelligent behaviors is a hallmark of natural intelligence. Such compos

local-aiarxiv-cs-ai

Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting

arXiv:2605.15470v1 Announce Type: new Abstract: Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilis

Local Ai

Not All Tasks Quantize Equally: Fisher-Guided Quantization for Visual Geometry Transformer

arXiv:2605.15828v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models, represented by Visual Geometry Grounded Transformer (VGGT), jointly predict multiple visual geometry tasks such a

local-aiarxiv-cs-cv

OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data

arXiv:2605.15511v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime n gg p, wher

Overfitting has a limitation: a model-independent generalization gap bound based on Renyi entropy

arXiv:2506.00182v3 Announce Type: replace-cross Abstract: Will further scaling up of machine learning models continue to bring success? A significant challenge in answering this question lies in under

PanoWorld: Geometry-Consistent Panoramic Video World Modeling

arXiv:2605.15391v1 Announce Type: cross Abstract: We present PanoWorld, a panoramic video world model that generates geometry-consistent 360egree video from a single image and a caption. Existing pano

researcharxiv-cs-ai

Perforated Neural Networks for Keyword Spotting

arXiv:2605.15647v1 Announce Type: new Abstract: Edge machine learning presents a unique set of constraints not encountered in cloud-scale model deployment: strict memory budgets, limited compute, and

PhyDetEx: Detecting and Explaining the Physical Plausibility of T2V Models

model-releasesarxiv-cs-cv

arXiv:2512.01843v2 Announce Type: replace Abstract: Driven by the growing capacity and training scale, Text-to-Video (T2V) generation models have recently achieved substantial progress in video qualit

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered

arXiv:2605.15622v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization, learning from finite differences of function evaluations without backpropagation, has recently regained attention in dee

Privacy Evaluation of Generative Models for Trajectory Generation

arXiv:2605.15246v1 Announce Type: new Abstract: Trajectory data is fundamental to modern urban intelligence, yet its sensitivity raises significant privacy concerns. Generative models such as Generati

Industry

Reachy mini rap battle at @aiDotEngineer Singapore!

Reachy, a humanoid robot, participated in a rap battle event at an AI engineer community gathering in Singapore. The event was hosted or documented by Clem Delangue, co-founder of Hugging Face, sugges

industryclem-delangue--x

RoiMAM: Region-of-Interest Medical Attention Model for Efficient Vision-Language Understanding

arXiv:2605.15561v1 Announce Type: new Abstract: Vision-Language Models (VLMs) facilitate medical visual question answering (MedVQA) by jointly interpreting images and text. However, existing models ty

researcharxiv-cs-cv

RTL-BenchMT: Dynamic Maintenance of RTL Generation Benchmark Through Agent-Assisted Analysis and Revision

arXiv:2605.15537v1 Announce Type: new Abstract: This paper introduces RTL-BenchMT, an agentic framework for dynamically maintaining RTL generation benchmarks. Large Language Models (LLMs) assisted aut

Sampling-Based Global Optimal Control and Estimation via Semidefinite Programming

arXiv:2507.17572v3 Announce Type: replace Abstract: Global optimization has gained attraction over the past decades, thanks to the development of both theoretical foundations and efficient numerical r

researcharxiv-cs-ro

Searching on a Budget: HW-NAS with 10 Latency Probes

arXiv:2504.00663v2 Announce Type: replace Abstract: Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa the target device, either via analytical approxima

Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs

arXiv:2503.02597v3 Announce Type: replace-cross Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated significant progress in perceiving and reasoning over multimodal inquiries,

researcharxiv-cs-ai