most citedUI-TARS: Pioneering Automated GUI Interaction with Native Agents

5 citations · 6 across the 9 of their papers we have counts for

collaborators

11 papers

cs.SE2026

Speculative Forking: Turning Idle Wait into Parallel Exploration for Repository Environment Setup

Yuling Shi, Mingchu Zhong, Zhensu Sun +8

Before developers can use a software project, they must first get it to run. This often requires installing the right language runtime, system packages, build tools, and test comma…

cs.AI2026

RSIHub: A Composable Framework for Agent-Evolution Methods

Zimu Wang, Xiaobo Wang, Siyu Ye +11

Agent-evolution methods use execution feedback to improve prompts, skills, tools, and orchestration, but their implementations often rebuild the same experimental machinery. We pre…

cs.CL2026

SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring

Yuling Shi, Jinghan Xu, Kelin Fu +12

As AI coding agents take on increasingly complex, long-horizon software engineering tasks, existing benchmarks are rapidly saturating and their evaluation quality has come under se…

cs.CL2026

SWE-Pruner Pro: The Coder LLM Already Knows What to Prune

Yuhang Wang, Yuling Shi, Shaoqiu Zhang +6

Pruning long context for coding agents has been a vital technology for efficient context management. While existing context pruning methods such as SWE-Pruner realize this by attac…

cs.CL2026

AutoTrainess: Teaching Language Models to Improve Language Models Autonomously

Zhaojian Yu, Penghao Yin, Shuzheng Gao +3

Training language models (LMs) remains a highly human-intensive process, even as frontier language model agents become increasingly capable at software engineering and other long-h…

cs.SE2026

Dockerless: Environment-Free Program Verifier for Coding Agents

Wenhao Zeng, Yuling Shi, Xiaodong Gu +10

Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning…

cs.SE2026

SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents

Shuzheng Gao, Wenhao Zeng, Zhaojian Yu +5

Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory management methods typically re…

cs.SE2026

SWE-Explore: Benchmarking How Coding Agents Explore Repositories

Shaoqiu Zhang, Yuhang Wang, Jialiang Liang +8

Repository-level coding benchmarks such as SWE-bench have driven a rapid surge in the capabilities of coding agents. Yet they usually treat coding tasks as a holistic, binary predi…

cs.SE2026★ 1 cited

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

Yuhang Wang, Yuling Shi, Mo Yang +7

LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency…

cs.AI2025

UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning

Haoming Wang, Haoyang Zou, Huatong Song +109

The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have sh…

cs.AI2025★ 5 cited

UI-TARS: Pioneering Automated GUI Interaction with Native Agents

Yujia Qin, Yining Ye, Junjie Fang +32

This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e.g., keyboard and mouse operations). U…