Author

Ziji Shi

2 papers indexed here

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Jul 2026

StateAct: Program State, before Pixels, for Long-Horizon Computer-Use Agents

Grounding action, verification, and memory in state, what the authors call state-grounding, shifts the main bottleneck from perception toward reasoning: failures depend more on what the agent thinks than on what it sees.

Yan Yang, Xiang-Ru Jian, Ziyang Luo et al. · arXiv.org · 4 citations

Preprint Aug 2026

MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning

This work trains software-engineering, deep-research, and general tool-use agents on gpt-oss-20b and improves task reward in all three and presents MCP-Universe RL (MCP-U RL), an open-source framework that takes over both.

Ziyang Luo, Yan Yang, Xiang-Ru Jian et al. · 0 citations