activity
20232026
most citedAgentBench: Evaluating LLMs as Agents

55 citations · 61 across the 11 of their papers we have counts for

collaborators

11 papers

cs.LG2026

TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling

Yanyu Ren, Xizheng Wang, Xiao Liu +8

Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with gro…

cs.AI2026

SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

Bowen Lv, Xiao Liu, Yanyu Ren +7

Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution. Online reinforcement learning…

cs.LG2026★ 1 cited

GLM-5: from Vibe Coding to Agentic Engineering

GLM-5-Team, :, Aohan Zeng +184

We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (AR…

cs.LG2025★ 1 cited

MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agents

Yifan Xu, Xiao Liu, Xinghan Liu +7

Building general-purpose graphical user interface (GUI) agents has become increasingly promising with the progress in vision language models. However, developing effective mobile G…

cs.CL2025

DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL

Rui Lu, Zhenyu Hou, Zihan Wang +6

Augmenting large language models (LLMs) with browsing tools substantially improves their potential as deep search agents to solve complex, real-world tasks. Yet, open LLMs still pe…

cs.AI2025

AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework

Hanchen Zhang, Xiao Liu, Bowen Lv +11

Recent advances in large language models (LLMs) have sparked growing interest in building generalist agents that can learn through online interactions. However, applying reinforcem…

cs.AI2025

ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents

Hanyu Lai, Xiao Liu, Yanxiao Zhao +7

We introduce ComputerRL, a framework for autonomous desktop intelligence that enables agents to operate complex digital workspaces skillfully. ComputerRL features the API-GUI parad…

cs.HC2024★ 1 cited

AutoGLM: Autonomous Foundation Agents for GUIs

Xiao Liu, Bo Qin, Dongzhu Liang +27

We present AutoGLM, a new series in the ChatGLM family, designed to serve as foundation agents for autonomous control of digital devices through Graphical User Interfaces (GUIs). W…

cs.AI2024★ 1 cited

VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents

Xiao Liu, Tianjie Zhang, Yu Gu +27

Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable Visual Foundation Agent…

cs.CL2024★ 2 cited

AutoWebGLM: A Large Language Model-based Web Navigating Agent

Hanyu Lai, Xiao Liu, Iat Long Iong +8

Large language models (LLMs) have fueled many intelligent web agents, but most existing ones perform far from satisfying in real-world web navigation tasks due to three factors: (1…

cs.AI2023★ 55 cited

AgentBench: Evaluating LLMs as Agents

Xiao Liu, Hao Yu, Hanchen Zhang +19

The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on cha…