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cs.AI2026

FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis

Kou Shi, Zun Wang, Qisheng Su +10

Training terminal agents requires scalable executable supervision, yet synthesizing high-quality terminal tasks remains challenging. Each task couples an instruction, an initialize…

cs.AI2026

AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities

Kai Chen, Zichen Ding, Jiaye Ge +20

As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly…

cs.AI2026

CLI-Universe: Towards Verifiable Task Synthesis Engine for Terminal Agents

Zhanbo Hua, Yifan Yao, Weihao Xie +14

While recent LLM-based terminal agents have demonstrated promising capabilities, the scarcity of high-quality, executable training data remains a critical bottleneck. Existing synt…

cs.AI2026

OS-Themis: A Scalable Critic Framework for Generalist GUI Rewards

Zehao Li, Zhenyu Wu, Yibo Zhao +11

Reinforcement Learning (RL) has the potential to improve the robustness of GUI agents in stochastic environments, yet training is highly sensitive to the quality of the reward func…

cs.AI2025

OS-Oracle: A Comprehensive Framework for Cross-Platform GUI Critic Models

Zhenyu Wu, Jingjing Xie, Zehao Li +8

With VLM-powered computer-using agents (CUAs) becoming increasingly capable at graphical user interface (GUI) navigation and manipulation, reliable step-level decision-making has e…