Search: “applications”

Scalable extensions to given-data Sobol' index estimators

arXiv:2509.09078v3 Announce Type: replace-cross Abstract: Given-data methods for variance-based sensitivity analysis have significantly advanced the feasibility of Sobol' index computation for computa

11 Aug 2026

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SymboUQ: Symbolic Uncertainty Quantification for Spatial Reasoning in LLMs

DGX agent

arXiv:2608.00417v2 Announce Type: replace Abstract: Although large language models (LLMs) can produce fluent spatial reasoning traces, their intermediate relations may fail to support the final conclu

researcharxiv-cs-ai

11 Aug 2026

Better Together: Quantifying the Benefits of AI-Assisted Recruitment

arXiv:2507.08029v2 Announce Type: replace Abstract: Hiring algorithms have mostly scored the materials recruiters already see. Large language models (LLMs) can instead generate new information about c

researcharxiv-cs-cl

10 Aug 2026

CyberForge: Verified Vulnerability Injection at Repository Level for Cybersecurity Agent Training

arXiv:2608.06471v1 Announce Type: cross Abstract: Despite recent advances, frontier large language model (LLM) agents remain limited in discovering and patching complex vulnerabilities in real-world s

10 Aug 2026

Active View Selection for Scene-level Multi-view Crowd Counting and Localization with Limited Labeling Budget

arXiv:2509.16684v2 Announce Type: replace Abstract: Multi-view crowd counting and localization fuse the input multi-views for estimating the crowd number or locations on the ground. Existing methods m

Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation

model-releasesarxiv-cs-cv

arXiv:2503.03556v3 Announce Type: replace Abstract: Object affordance reasoning, the ability to infer object functionalities based on physical properties, is fundamental for task-oriented planning and

Counterfactual Analysis via Large Language Models

arXiv:2608.05367v1 Announce Type: new Abstract: Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making. This paper inve

researcharxiv-cs-ai

Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity

arXiv:2608.05336v1 Announce Type: cross Abstract: Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships. Despite th

Curiosity-Diffuser: Curiosity Guide Diffusion Models for Reliability

arXiv:2503.14833v2 Announce Type: replace-cross Abstract: One of the bottlenecks in robotic intelligence is the instability of neural network models. This leads to risks when applying intelligence in

Hardware

Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM

arXiv:2507.04004v3 Announce Type: replace Abstract: This paper presents the first photo-realistic LiDAR-Inertial-Camera Gaussian Splatting SLAM system that simultaneously addresses visual quality, geo

hardwarearxiv-cs-ro

GeoReward: Mitigating Contextual Variable Overestimation in Vision-Language Models for Cross-Market Preference Prediction

arXiv:2608.04504v1 Announce Type: cross Abstract: Vision-language models excel in many multimodal tasks but remain prone to a subtle yet impactful failure mode: they tend to overestimate dominant visu

Multi-Objective Ranking for Live-Streaming: Balancing Fresh and Delayed Signals with Segment-Aware Targeting

arXiv:2608.04455v1 Announce Type: cross Abstract: One of the most challenging problems entertainment live-streaming services face in recommendation systems is that user behaviors are sparse and delaye

Hardware

Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph

arXiv:2608.03695v1 Announce Type: cross Abstract: This work addresses community detection in temporal networks through GPU-accelerated extensions of spectral clustering and modularity-based algorithms

hardwarearxiv-cs-lg

Calibrating Semantic Uncertainty from Observable Language-Model Probabilities

arXiv:2607.17447v2 Announce Type: replace-cross Abstract: As generative artificial intelligence enters scientific and professional work, its uncertainty must be defined on the states that matter for i

researcharxiv-cs-cl

Agents

Improving Sample Efficiency in Multi-Agent Reinforcement Learning for Simulated Football Games via Exploration

arXiv:2503.13077v2 Announce Type: replace Abstract: Multi-agent reinforcement learning has shown promise in learning cooperative behaviors in team-based environments. However, such methods often deman

agentsarxiv-cs-lg

Lightweight Chunk Selection for Mobile Retrieval-Augmented Generation

arXiv:2608.03148v1 Announce Type: cross Abstract: RAG improves the factual grounding of LLM by incorporating external knowledge, but deploying RAG on mobile and edge devices remains challenging becaus

Multi-Camera Trajectory Forecasting with Trajectory Tensors

arXiv:2108.04694v2 Announce Type: cross Abstract: We introduce the problem of multi-camera trajectory forecasting (MCTF), which involves predicting the trajectory of a moving object across a network o

researcharxiv-cs-ai

Toward Visual Grounding: A Survey

arXiv:2412.20206v4 Announce Type: replace Abstract: Visual Grounding, also known as Referring Expression Comprehension and Phrase Grounding, aims to ground the specific region(s) within the image(s) b

VIBE: Vector Index Benchmark for Embeddings

model-releasesarxiv-cs-lg

arXiv:2505.17810v2 Announce Type: replace Abstract: Approximate nearest neighbor (ANN) search is a performance-critical component of many machine learning pipelines, and rigorous benchmarking is essen

A Benchmark Dataset for MLLM-Generated Image Detection: GPT Image2 & Nano Banana2

model-releasesarxiv-cs-cv

arXiv:2608.01258v1 Announce Type: new Abstract: The realism of images generated by multimodal large language models (MLLMs), such as GPT Image2 and Nano Banana2, has improved rapidly in recent years.

A Semiparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior

arXiv:2509.21996v3 Announce Type: replace-cross Abstract: Hawkes processes are used in settings where past events increase the likelihood of future events occurring, resulting in a natural clustering

Adaptive Reconstruction of Bosonic Quantum States

model-releasesarxiv-cs-lg

arXiv:2608.02049v1 Announce Type: cross Abstract: Bosonic quantum systems provide a hardware-efficient platform for quantum information processing but remain challenging to characterise due to their l

Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability

arXiv:2512.09850v2 Announce Type: replace Abstract: We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential deci

Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey

arXiv:2510.01925v3 Announce Type: replace Abstract: Reward models (RMs) play a critical role in enhancing the reasoning performance of LLMs. For example, they can provide training signals to finetune

researcharxiv-cs-cl

Loggia dei Lanzi: AI Thermography Enhancement Comparisons through 3D Photogrammetry

model-releasesarxiv-cs-cv

arXiv:2608.02404v1 Announce Type: new Abstract: The Loggia dei Lanzi in the Piazza della Signoria is one of Florence's most prominent structures visited by millions every year. Its construction histor

Not All EEG Moments Are Equal: Position-Adaptive Time Scheduling for EEG Generation

arXiv:2608.00048v1 Announce Type: cross Abstract: Electroencephalography (EEG) generation is essential for alleviating data scarcity and enabling large scale neural modeling in brain computer interfac

Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation

arXiv:2608.00872v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative training of machine learning models across multiple institutions without sharing sensitive data, making

Sparse Covariance Neural Networks

arXiv:2410.01669v3 Announce Type: replace Abstract: Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix of input data to leverage correlation information as pairwise

Hardware

Stipple: Real-Time Incremental Gaussian Splatting with Visual-Inertial Tracking

arXiv:2608.00931v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) provides efficient rendering of photo-realistic scenes, but its heavy preprocessing and training steps make it a poor fit

hardwarearxiv-cs-cv

Agentic Harness for Real-World Compilers

arXiv:2603.20075v2 Announce Type: replace-cross Abstract: Compilers are critical to modern computing, yet fixing compiler bugs is difficult. While recent large language model (LLM) advancements enable

Do Medical Foundation Models Generalize on the African Brain?

arXiv:2607.28771v1 Announce Type: new Abstract: Medical foundation models (FMs) are increasingly used for brain MRI analysis. However, their evaluation remains dominated by high-resource datasets, lea

safetyarxiv-cs-cv

Latent Lie-Poisson Neural Networks (LLPNNs): Discovering the motion of Lie-Poisson systems through observable data and latent dynamics

arXiv:2607.28939v1 Announce Type: new Abstract: Structure-preserving neural networks are essential for the long-term prediction of Hamiltonian systems from data. Many important Hamiltonian systems in

Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support

arXiv:2607.28677v1 Announce Type: new Abstract: LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning. These developments have accelerated the

Stratified Negation in RDF Rules: A Correct Approach (Extended Version)

arXiv:2607.28778v1 Announce Type: cross Abstract: Combining RDF rule languages, such as N3 or SHACL Rules, with default negation is challenging. Existing methods to stratify negation often fail for RD

A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation

arXiv:2607.27501v1 Announce Type: new Abstract: We present a lightweight approach to foundation modeling (extbf{NEXUS}) that leverages pre-trained learning from collider physics data towards out-of-do

Local Ai

Auto Research for Materials: Auditable AI-Scientist Workflows with Held-Out Transfer

arXiv:2607.17100v2 Announce Type: replace-cross Abstract: Auto Research uses language-model agents to propose, implement, and evaluate machine-learning changes in a closed loop, but is usually judged

local-aiarxiv-cs-ai

Agents

CRMWeaver: Building Powerful Business Agent via Agentic RL and Shared Memories

arXiv:2510.25333v2 Announce Type: replace Abstract: Recent years have witnessed the rapid development of LLM-based agents, which shed light on using language agents to solve complex real-world problem

agentsarxiv-cs-cl

Eco3S: Complex Socio-Economic System Simulation via Agent-Based Models

arXiv:2607.26588v1 Announce Type: new Abstract: The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM). However, current LLM-based ABM research faces

Neural Network Approximation of Solutions to Fractional Parabolic Partial Differential Equations

arXiv:2607.27781v1 Announce Type: cross Abstract: We establish a dimension-efficient neural network approximation theory for solutions to fractional parabolic equations with lower-order drift and pote

NMINE: Normalized Mutual Information Neural Estimation

arXiv:2607.27710v1 Announce Type: new Abstract: Mutual information is a general measure of statistical dependence that captures both linear and nonlinear relationships between random variables. For co

Physical prior guided cooperative learning framework for joint turbulence degradation estimation and infrared video restoration

arXiv:2408.04227v2 Announce Type: replace-cross Abstract: Infrared imaging and turbulence strength measurements are in widespread demand in many fields. This paper introduces a Physical Prior Guided C

CLBench-V: Evaluating Multimodal Context Learning from Grounding to Knowledge Acquisition

arXiv:2607.25294v1 Announce Type: cross Abstract: Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge. While recent work has hig

Input Shaping for Point-to-Point Motion with a Continuum Robot Arm

arXiv:2607.25071v1 Announce Type: new Abstract: A cable-driven continuum robot arm is an underactuated mechanism and may suffer residual vibration at the end of a rest-to-rest maneuver. In this work,

researcharxiv-cs-ro

Learning from the Unseen: Offline Reinforcement Learning with Hidden Actions

arXiv:2607.25241v1 Announce Type: cross Abstract: Standard offline reinforcement learning (RL) algorithms typically assume that the actions in the dataset are observed without error. However, in many

Agents

Towards an Agent Operating System - Lessons from Classical and Cloud OS

arXiv:2607.25076v1 Announce Type: new Abstract: Every major wave of platform software follows the same arc: an initial period of experimentation with competing frameworks and ad-hoc implementations, f

agentsarxiv-cs-ai

An empirical investigation into the properties of standard word embeddings

arXiv:2607.23675v1 Announce Type: cross Abstract: The embedding of word sequences into continuous vector spaces has been one of the most important developments in Natural Language Processing in the re

researcharxiv-cs-ai

28 Jul 2026

Local Ai

An Integrated Deep Learning and Statistical Framework for Whole-Network Gene--Environment Association with Leaf Vascular Architecture

arXiv:2607.22763v1 Announce Type: new Abstract: Leaf veins exhibit remarkable diversity in architecture and patterning, yet existing gene--environment association studies have primarily quantified lea

local-aiarxiv-cs-lg

28 Jul 2026

Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities

arXiv:2607.23018v1 Announce Type: cross Abstract: Nonstationary Gaussian process (GP) models are powerful tools for capturing input-dependent variability by adapting to observed data. However, with li