Search: “applications”

Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques

arXiv:2312.10089v2 Announce Type: replace Abstract: Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visua

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AI/ML Life Cycle Management for Interoperable AI Native RAN

DGX agent

arXiv:2507.18538v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral to the 5G Radio Access Network (RAN), enabling beam manag

researcharxiv-cs-lg

27 Aug 2026

CAT-GS: Balanced Multimodal Learning via Calibrated Gating and Fusion Surgery

arXiv:2608.24947v1 Announce Type: new Abstract: End-to-end training of multimodal neural networks often exhibits unstable neural dynamics characterized by three coupled failure modes that degrade lear

safetyarxiv-cs-lg

Clearing the Underbrush: AI-Enhanced RF Interference Suppression

arXiv:2608.24974v1 Announce Type: new Abstract: AI-based structured interference rejection has grown more popular because deep learning approaches can outperform traditional methods by jointly conside

researcharxiv-cs-lg

Hardware

Comparing Corrupted Constrained Learning Problems

arXiv:2608.25745v1 Announce Type: new Abstract: A key result in statistics is the data processing inequality, originally proved by Blackwell (1951) and later refined by DeGroot (1962) in terms of stat

hardwarearxiv-cs-lg

Context-Guided Semantic Alignment for Feature Fusion Networks

arXiv:2606.14005v2 Announce Type: replace Abstract: Feature fusion networks are fundamental components in modern object detectors, aggregating multi-scale features to detect objects of varying sizes.

safetyarxiv-cs-cv

CRAMER: Control via Request-Aware Masking for Editing Recommenders

arXiv:2608.25370v1 Announce Type: cross Abstract: Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt thei

Hardware

CRESSim-Neo: A Batched GPU Simulation Engine for Surgical Robotics and Robot Learning

arXiv:2608.25192v1 Announce Type: new Abstract: We introduce CRESSim-Neo, a batched GPU simulation engine for surgical robotics and robot learning. CRESSim-Neo combines position-based simulation of ri

hardwarearxiv-cs-ro

DCEO: Direct Causal Effect Optimization for Long-Term User Value Modeling in E-commerce Search

arXiv:2608.25635v1 Announce Type: new Abstract: Industrial e-commerce search systems ultimately aim to optimize the user-level long-term objective, such as n-day cumulative purchases or gross merchand

safetyarxiv-cs-lg

DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction

model-releasesarxiv-cs-ro

arXiv:2608.26002v1 Announce Type: new Abstract: Advanced automation is a key technology for enhancing the safety of ground operations amidst the increasing density of commercial air traffic. While mot

EncoTESS: Age-Sensitive Encodings from Raw TESS Light Curves

arXiv:2608.25019v1 Announce Type: cross Abstract: Main sequence stars of spectral types late F through M exhibit systematic variability in photometric light curves, particularly when they are young. R

Enterprise AI's real risk isn't autonomous agents. It's the complexity between them.

Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it.That’s because enterprises don't deploy a single agent and watch it r

safetyventurebeat-ai

Extending Ground-Constraint LiDAR-IMU Calibration to Tilted Surfaces in a Continuous-Time Framework

arXiv:2608.25135v1 Announce Type: new Abstract: This paper presents a novel method that extends targetless LiDAR-IMU calibration for ground vehicles to non- flat environments. Calibration typically ne

researcharxiv-cs-ro

FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction

arXiv:2608.25828v1 Announce Type: new Abstract: We present FlowMoDL, an unrolled neural network for highly accelerated 4D flow MRI reconstruction that directly optimizes for both anatomical magnitude

Generalized Riesz Regression: A Unified Framework for Debiased Machine Learning with Riesz Representer Fitting under Bregman Divergence

arXiv:2601.07752v4 Announce Type: replace-cross Abstract: Estimating the Riesz representer is central to debiased machine learning, yet the generator and representer model determine which regression d

researcharxiv-cs-lg

Generative Modeling: A Review

arXiv:2501.05458v3 Announce Type: replace-cross Abstract: We organize the generative-modeling literature around three classes of generators, corresponding to three distinct inferential tasks: estimati

GreenLeaf Law Embed Tiny: A Compact Embedding Model for Legal Domain Retrieval

model-releasesarxiv-cs-cl

arXiv:2608.24936v1 Announce Type: cross Abstract: We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal

GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction

arXiv:2601.13207v2 Announce Type: replace Abstract: Geo-localization aims to infer the geographic location where an image was captured using observable visual evidence. Traditional methods achieve imp

InteractGesture: Progressive Chunk Guidance for Continuous Streaming Co-Speech Gesture Control

arXiv:2608.25734v1 Announce Type: new Abstract: Co-speech gesture generation has made significant progress toward realistic full-body motion from speaker audio, yet existing models lack fine-grained s

LAC: Linear and Angular Compliance for Humanoid Whole-body Control

arXiv:2608.25405v1 Announce Type: new Abstract: Real-world humanoid tasks involve physical interaction with objects and humans, yet current controllers either reject external forces as disturbances or

safetyarxiv-cs-ro

Learning Mixtures of Plackett-Luce Models for Multi-Objective Alignment

arXiv:2608.25200v1 Announce Type: cross Abstract: We consider the problem of learning a mixture of k Plackett-Luce models given multi-way ranking responses from annotators that may represent heterogen

safetyarxiv-cs-cl

Learning to Accelerate Vision-Language-Action Models through Adaptive Visual Token Caching

arXiv:2602.00686v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable generalization capabilities in robotic manipulation tasks, yet their substantial co

safetyarxiv-cs-ro

LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale

model-releasesarxiv-cs-cl

arXiv:2608.25204v1 Announce Type: cross Abstract: We introduce LibriBrain100, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation. Libri

Loss-Based Active Learning for Neural Abstractive Summarization

model-releasesarxiv-cs-cl

arXiv:2608.25881v1 Announce Type: new Abstract: Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as i

Lowering the Barrier to AI-Driven Inspection: A No-Code Workflow for Automated Structural Defect Detection

arXiv:2608.25176v1 Announce Type: new Abstract: Structural health monitoring (SHM) is essential in modern engineering, providing data for condition-based maintenance, lifecycle assessment, and predict

Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception

arXiv:2509.09297v2 Announce Type: replace Abstract: Open-set detection is crucial for robust UAV autonomy in air-to-air object detection under real-world conditions. Traditional closed-set detectors d

Multi-Modal Anomaly Detection: A Survey

arXiv:2608.24937v1 Announce Type: new Abstract: Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-cr

safetyarxiv-cs-lg

Multi-Touch and Bending Sensing Using Electrical Impedance Tomography for Robotics

arXiv:2503.13048v4 Announce Type: replace Abstract: Electrical Impedance Tomography (EIT) offers a promising solution for distributed tactile sensing with minimal wiring and full-surface coverage in r

researcharxiv-cs-ro

MyoMechanix: Biomechanically-Grounded Compositional Skilled Activity Understanding and Coaching

arXiv:2608.26094v1 Announce Type: new Abstract: Existing action quality assessment (AQA) datasets and methods rely primarily on visual inputs such as RGB and pose, overlooking physiological dynamics s

Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs

arXiv:2410.10368v3 Announce Type: replace Abstract: We present the first linear time complexity randomized algorithms for unbiased approximation of the celebrated family of general random walk kernels

researcharxiv-cs-lg

Physics-Informed Error Field Learning: A Post-Training Optimization Framework for Physics-Informed Neural Networks

arXiv:2608.24970v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) have emerged as an important class of numerical methods for solving partial differential equations (PDEs). Howe

Quantum Scrambling Born Machine

arXiv:2602.17281v2 Announce Type: replace-cross Abstract: Quantum generative modeling, where the Born rule naturally defines probability distributions through measurement of parameterized quantum stat

Reimagining work: How Pythian’s internal AI playbook delivers customer ROI

model-releasesgoogle-cloud-ai

When Pythian rolled out Google Cloud’s Gemini Enterprise across our 500-person company in 27 countries, the goal was simple: use our own company as a proving ground to discover how enterprise AI actua

ROS2 Connect: A new ROS2 over WAN Solution

arXiv:2608.25102v1 Announce Type: new Abstract: The Robot Operating System 2 (ROS2) has become a widely adopted framework for the development of distributed robotic systems. However, its communication

researcharxiv-cs-ro

Local Ai

SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models

arXiv:2608.25304v1 Announce Type: cross Abstract: Multinomial choice models allow flexible substitution patterns but become computationally demanding with many alternatives or observations. With a fix

local-aiarxiv-cs-lg

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation

arXiv:2412.12660v2 Announce Type: replace Abstract: Recently, developing unified medical image segmentation models gains increasing attention, especially with the advent of the Segment Anything Model

safetyarxiv-cs-cv

Agents

Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation

arXiv:2510.10679v2 Announce Type: replace Abstract: The 3D multi-modal brain tumor segmentation is critical to multi-modal healthcare, and it requires accurate identification of distinct internal anat

agentsarxiv-cs-cv

Local Ai

THA-Flow Generative Model: Prosthesis Geometry Prediction from Preoperative CT

arXiv:2608.25845v1 Announce Type: new Abstract: Preoperative planning for total hip arthroplasty (THA) is commonly framed as selecting a single prosthesis configuration and placement for a patient's o

local-aiarxiv-cs-cv

Theoretically Principled Federated Learning for Balancing Privacy and Utility

arXiv:2305.15148v3 Announce Type: replace Abstract: We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the t

Towards Fine-Grained Text-to-3D Quality Assessment: A Benchmark and A Two-Stage Rank-Learning Metric

arXiv:2509.23841v3 Announce Type: replace Abstract: Recent advances in Text-to-3D (T23D) generative models have enabled the synthesis of diverse, high-fidelity 3D assets from textual prompts. However,

Towards Reliable, Generalizable, and Specific In-Context Knowledge Editing via Multi-Objective Reinforcement Learning

arXiv:2608.25100v1 Announce Type: cross Abstract: Large Language Models (LLMs) are powerful but limited by static parametric knowledge that becomes outdated once pretraining ends. Knowledge editing ad

Agents

When Obstacles Bend: Modeling Vegetation Deformation in the context of Field Robotics

arXiv:2608.26050v1 Announce Type: new Abstract: Autonomous robots operating in natural environments must often interact with vegetation rather than simply avoid it. In this context, traversability is

agentsarxiv-cs-ro

When Personality Meets Quantization: A Layer-wise MBTI Analysis of Quantized LLMs

arXiv:2608.25977v1 Announce Type: new Abstract: Personality is increasingly important in large language models (LLMs), as it shapes users' trust, engagement, and emotional experiences. While the Myers

researcharxiv-cs-cl

A Dual-Dimensional LLM Framework for Automated Item Incidental Content Similarity Analysis in Large-Scale Assessments

model-releasesarxiv-cs-ai

arXiv:2608.24825v1 Announce Type: new Abstract: The rapid expansion of large-scale assessments and the growing adoption of automatic item generation have intensified concerns about incidental content

A tale of perfect fit and phantom optima: how data-driven models can fail in real-time optimization

model-releasesarxiv-cs-ai

arXiv:2608.23885v1 Announce Type: cross Abstract: Real-time optimization (RTO) relies on process models to locate economically optimal operating conditions. Because developing first-principles models

Amortized Set Prediction for Inverse IFS Reconstruction from Density Maps

arXiv:2608.24175v1 Announce Type: new Abstract: Iterated Function Systems (IFS) generate self-similar fractals from a few contractive affine maps. The forward map from parameters to images is computat

Agents

Beyond the Mandate: A Systematic Security Analysis of the Agent Payments Protocol (AP2)

arXiv:2608.23858v1 Announce Type: cross Abstract: The Agent Payments Protocol (AP2), introduced by Google, enables large language model (LLM)-driven shopping agents to authorize and execute payments o

agentsarxiv-cs-ai

Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations

arXiv:2601.00282v2 Announce Type: replace-cross Abstract: Quantization is widely used to accelerate inference and streamline the deployment of large language models (LLMs), yet its effects on self-exp

researcharxiv-cs-ai