Author

Chenliang Li

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Preprint Aug 2026

State2State: Environment-Derived Mid-Training for LLM Agents

State2State is proposed, an environment-derived mid-training method that converts explored environment states into training objectives, challenging agents to reach a specified target state by deriving tasks from environment exploration and verifying success through rule-based state matching.

Xuanyu Lei, Yiqi Zhu, Chenliang Li et al. · 1 citation

#artificial intelligence Preprint Aug 2026

Benchmarking General Mobile Assistants in Challenging Real-World Scenarios

GMA is presented, a benchmark for evaluating general mobile assistants in challenging real-world scenarios, and shows that appropriate harness design can meaningfully improve performance, particularly on demanding workflows, while the effectiveness of specific designs can vary across foundation models.

Yi-Qi Zhu, Feiyu Gao, Jiakang Fan et al. · 0 citations