activity
20202025
most citedToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

72 citations · 175 across the 14 of their papers we have counts for

collaborators
Showing 2025Show all

5 papers · 1 filter

cs.CR2025

SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning

Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1

The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring…

cs.AI2025

Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents

Zihao Wang, Xujing Li, Yining Ye +24

We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned native keyboard-mouse inputs. Unlike API- or GUI-based approac…

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.CV2025★ 1 cited

Seed1.5-VL Technical Report

Dong Guo, Faming Wu, Feida Zhu +194

We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…

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…