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Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking

arXiv:2605.23118v1 Announce Type: cross Abstract: Tracking tumor lesions across serial CT scans is essential for oncological response assessment. Existing automated methods face a fundamental trade-of

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Exploring deep learning for Event-Based Saliency Prediction with a Transformer-based model

DGX agent

arXiv:2605.23790v1 Announce Type: new Abstract: Saliency prediction has been extensively studied in RGB images and videos as a computational model of human visual attention. In contrast, predicting sa

model-releasesarxiv-cs-cv

25 May 2026

ExpOS: Explainable Open-Surgery Skills Assessment Using 3D Hand Reconstruction

arXiv:2605.23653v1 Announce Type: new Abstract: Timely and transparent feedback is essential for effective surgical training, yet current assessment remains dependent on expert observation, limiting s

agentsarxiv-cs-cv

Expressive Power of Deep Homomorphism Networks over Relational Databases

arXiv:2605.22852v1 Announce Type: cross Abstract: The expressive limitations of message-passing Graph Neural Networks (GNNs) have motivated a wide range of more powerful graph learning architectures.

Extending Deep Event Visual Odometry with Sparse Point-Cloud Export

arXiv:2605.22890v1 Announce Type: cross Abstract: Event cameras are well suited for visual odometry under high-speed motion and challenging lighting conditions due to their low latency, high temporal

Eye Gaze-Informed and Context-Aware Pedestrian Trajectory Prediction in Shared Spaces with Automated Shuttles: A Virtual Reality Study

arXiv:2603.19812v2 Announce Type: replace Abstract: To address this gap, we conduct a Virtual Reality experiment in which pedestrians interact with automated shuttles under varying approach angles (45

researcharxiv-cs-lg

Fast-dDrive: Efficient Block-Diffusion VLM for Autonomous Driving

arXiv:2605.23163v1 Announce Type: new Abstract: End-to-end autonomous driving via Vision-Language-Action (VLA) models demands a precarious balance between high-fidelity trajectory planning and efficie

safetyarxiv-cs-cl

FAST-ME: Foundation-aware Adaptive Stopping for Motion Estimation for Efficient IoT Video Analysis

model-releasesarxiv-cs-cv

arXiv:2605.23428v1 Announce Type: new Abstract: In modern multimedia systems, efficient video processing is critical, especially in resource-constrained environments such as IoT-based camera networks,

FastKernels: Benchmarking GPU Kernel Generation in Production

arXiv:2605.23215v1 Announce Type: cross Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize agai

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation

arXiv:2510.08945v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduc

Local Ai

FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data

arXiv:2605.22954v1 Announce Type: new Abstract: Multi-center survival prediction can improve robustness and generalizability, yet privacy regulations and institutional governance often prevent pooling

local-aiarxiv-cs-lg

Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches

arXiv:2512.12677v2 Announce Type: replace-cross Abstract: We explore efficient strategies to fine-tune decoder-only Large Language Models (LLMs) for downstream text classification under resource const

FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning

arXiv:2605.22898v1 Announce Type: new Abstract: Federated learning protocols face a structural trilemma: canonical server-based aggregation~ite{mcmahan2017} creates a single point of failure and gradi

safetyarxiv-cs-lg

Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models

arXiv:2605.23070v1 Announce Type: new Abstract: We propose Flow Mismatching, an unsupervised anomaly detection method that deliberately avoids reconstruction-based paradigms. Instead, we treat flow ma

Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks

arXiv:2602.00567v2 Announce Type: replace Abstract: The deployment of quantized neural networks on edge devices, combined with privacy regulations like GDPR, creates an urgent need for machine unlearn

researcharxiv-cs-lg

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning

arXiv:2509.12958v2 Announce Type: replace Abstract: Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to acc

Foundation Protocol: A Coordination Layer for Agentic Society

arXiv:2605.23218v1 Announce Type: new Abstract: Autonomous agents are moving from tools into a layer of social infrastructure: they browse, purchase, deploy software, manage systems, and increasingly

safetyarxiv-cs-ai

Four Simple Proprioceptive Estimators for Legged Robots

arXiv:2605.23100v1 Announce Type: new Abstract: Legged robots carry an IMU, but the inertial solution drifts because consumer-grade IMUs are noisy. However, the feet create intermittent contacts with

safetyarxiv-cs-ro

Local Ai

From Activation to Causality: Discovery of Causal Visual Representations in the Human Brain

arXiv:2605.23895v1 Announce Type: new Abstract: Identifying which brain regions represent a visual concept in the human brain is a central challenge in neuroscience. Existing approaches have localized

local-aiarxiv-cs-cv

From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning

arXiv:2605.23382v1 Announce Type: new Abstract: Agentic reinforcement learning (Agentic RL) has achieved strong progress in tasks with clear success signals. However, many real-world agent application

safetyarxiv-cs-cl

From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills

arXiv:2605.23899v1 Announce Type: new Abstract: Language agents increasingly improve by reusing skills -- structured procedural artifacts distilled from past experience. In particular, domain-level an

agentsarxiv-cs-ai

From Residuals to Reasons: LLM-Guided Mechanism Inference from Tabular Data

arXiv:2605.22897v1 Announce Type: new Abstract: A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on st

agentsarxiv-cs-lg

From Simulation to Discovery: AI Enabled Probabilistic Emulation of Mechanistic Crop Systems

arXiv:2605.22848v1 Announce Type: cross Abstract: Global food security depends on predicting crop responses to climate variability, yet process based crop models remain too computationally expensive f

researcharxiv-cs-lg

FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning

model-releasesarxiv-cs-lg

arXiv:2605.22869v1 Announce Type: new Abstract: Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral st

FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence

arXiv:2605.22868v1 Announce Type: new Abstract: Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, late

agentsarxiv-cs-lg

GAF: Gaussian Action Field as a 4D Representation for Dynamic World Modeling in Robotic Manipulation

arXiv:2506.14135v5 Announce Type: replace-cross Abstract: Accurate scene perception is critical for vision-based robotic manipulation. Existing approaches typically follow either a Vision-to-Action (V

tutorialsarxiv-cs-cv

GazeBehavior Annotation Toolkit (GBAT): AI-powered toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction

arXiv:2605.22962v1 Announce Type: new Abstract: Video recordings of child-caregiver interactions enable investigation of attentional dynamics during naturalistic behavior. Such multimodal recording al

Applications

GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation

arXiv:2605.22882v1 Announce Type: new Abstract: Video world models can generate realistic futures from a single instruction, but they often fail to preserve consistent point-level motion over time. As

applicationsarxiv-cs-cv

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs

arXiv:2605.23078v1 Announce Type: cross Abstract: Mixture-of-Experts Large Language Models (MoE-LLMs) achieve strong performance but incur substantial memory overhead due to massive expert parameters.

researcharxiv-cs-cl

General Hazard Detection

arXiv:2605.23304v1 Announce Type: new Abstract: Hazard, as an abstract concept, is typically defined through cognitive-level logical reasoning rather than concrete examples. In contrast, existing haza

safetyarxiv-cs-cv

Generative AI and the Reorganization of Labor Demand

arXiv:2605.23159v1 Announce Type: cross Abstract: Generative artificial intelligence (AI) is expected to transform work, but less is known about how firms reorganize labor demand as the technology dif

Generator-Refiner-Examiner: A Tri-Module Data Augmentation Framework for 3D Human Avatar Learning from Monocular Videos

arXiv:2605.23555v1 Announce Type: new Abstract: This paper addresses the challenge of reconstructing photorealistic and animatable 3D human avatars from monocular videos. While existing methods rely o

Local Ai

GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction

arXiv:2605.23888v1 Announce Type: new Abstract: We introduce a new approach to high-fidelity 3D scene reconstruction from multi-view RGB images that tightly couples reconstruction with a strong genera

local-aiarxiv-cs-cv

GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models

arXiv:2605.23238v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as economic agents in marketplaces, auctions, and bidding settings. Anticipating their behavior i

Geo-Align: Video Generation Alignment via Metric Geometry Reward

arXiv:2605.23903v1 Announce Type: new Abstract: Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rel

safetyarxiv-cs-cv

Applications

GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values

arXiv:2508.14083v3 Announce Type: replace-cross Abstract: The ubiquity of missing data in urban intelligence systems, attributable to adverse environmental conditions and equipment failures, poses a s

applicationsarxiv-cs-ai

GFSR: Geometric Fidelity and Spatial Refinement for Reliable Lane Detection

arXiv:2605.23327v1 Announce Type: new Abstract: Lane detection stands as a crucial perception task in autonomous driving and advanced driver assistance systems. However, existing methods still degrade

agentsarxiv-cs-cv

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

arXiv:2510.04567v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise

GlowGS: Generative Semantic Feature Learning for 3D Gaussian Splatting in Nighttime Glow Scenes

arXiv:2605.23602v1 Announce Type: new Abstract: Existing 3DGS methods effectively render high-quality novel views in clear-day scenes. However, they struggle with night scenes, particularly in glow re

GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals

arXiv:2504.09846v2 Announce Type: replace-cross Abstract: Frequent and long-term exposure to hyperglycemia increases the risk of chronic complications, including neuropathy, nephropathy, and cardiovas

tutorialsarxiv-cs-ai

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences

arXiv:2605.23183v1 Announce Type: cross Abstract: Contemporary glioma diagnosis integrates molecular features with histopathology to guide clinical decision-making. However, in clinical settings, dive

tutorialsarxiv-cs-cv

Goal-Conditioned Agents that Learn Everything All at Once

arXiv:2605.23551v1 Announce Type: cross Abstract: A goal-conditioned reinforcement learning agent exploring an environment will see a wealth of information throughout a trajectory, most of which is di

safetyarxiv-cs-ai

Good Token Hunting: A Hitchhiker's Guide to Token Selection for Visual Geometry Transformers

arXiv:2605.23892v1 Announce Type: cross Abstract: Visual geometry transformers have become powerful architectures for multi-view 3D reconstruction, enabling joint prediction of multiple 3D attributes

tutorialsarxiv-cs-ai

Applications

GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks

arXiv:2602.11629v2 Announce Type: replace Abstract: Graph Prompt Learning (GPL) has recently emerged as a promising paradigm for downstream adaptation of pre-trained graph models, mitigating the misal

applicationsarxiv-cs-lg

GradingAttack: Exposing Security Vulnerabilities in LLM Based Educational Grading Agents

arXiv:2602.00979v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as educational agents for automatic short answer grading (ASAG) in real-world education

agentsarxiv-cs-ai

Graph Alignment Topology as an Inductive Bias for Grounding Detection

arXiv:2605.22963v1 Announce Type: cross Abstract: Large Language Models (LLMs) are optimized to produce distributionally plausible continuations rather than to explicitly verify whether generated prop

safetyarxiv-cs-ai

Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions

arXiv:2605.23696v1 Announce Type: new Abstract: Effectively managing Air Traffic Control Officer (ATCO) workload is crucial in maintaining operational safety. Group supervisors use tools that estimate

safetyarxiv-cs-lg

Hardware

Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization

arXiv:2508.10651v3 Announce Type: replace Abstract: We present a novel approach for graph classification based on tabularizing graph data via new variants of the Weisfeiler-Leman algorithm and then ap

hardwarearxiv-cs-lg