activity
20182026
most citedExternalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering

6 citations · 38 across the 62 of their papers we have counts for

collaborators

69 papers

cs.AI2026

Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction

Haoting Zhang, Haoxian Chen, Jiayuan Sheng +4

Building a blockchain digital twin largely requires translating domain knowledge and specific system descriptions into a simulator architecture, calibrating its parameters against…

cs.CV2026

3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects

Zhenyu Zhao, Nanshan Jia, Jihyeon Je +7

Automated evaluation is essential for scaling generative 3D systems, where exhaustive human review is costly and slow. However, the reliability of an automated judge depends on the…

cs.AI2026

ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents

Kaiwen Xiong, Haonian Ji, Shi Qiu +4

Production large language-model (LLM) agents are increasingly deployed not as lone problem-solvers but as managers: a main model creates specialized subagents, delegates work, and…

stat.ME2026

HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data

Xinrui Ruan, Zhenyu Zhao, Waverly Wei +4

Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity. Orga…

cs.CV2026

VisualClaw: A Real-Time, Personalized Agent for the Physical World

Haoqin Tu, Jianwen Chen, Zijun Wang +14

Vision language models are serving as general-purpose interfaces for complex multimodal tasks. However, deployment still faces three gaps: VLMs typically incur high latency and cos…

stat.ME2026

Experimentation for Different Scheduling Policies on Queues: Mixed Differences-in-Q Estimators Based on Little's Law

Nanshan Jia, Ramesh Johari, Nian Si +1

In data centers, tasks are dispatched to various servers to evenly distribute the workload. When a data center considers implementing a new scheduling algorithm, it typically condu…