Papers by Xiaoran Xu
LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) show promise in simulating complex scenarios. |
| Approach: | They examine multiple LLMs to proactively estimate perceived earthquake impacts using multimodal datasets and multimodal imagery. |
| Outcome: | The framework generates Modified Mercalli Intensity (MMI) predictions at zip code and county scales using multimodal datasets. |
Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach (2025.findings-emnlp)
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| Challenge: | Current approaches to automating complex tasks focus on reactive policies and focus on visual observations. |
| Approach: | They propose a framework that prioritizes natural language understanding and structured reasoning to enhance the agent’s global understanding of the environment by developing a task-oriented, refinable world model at the outset of the task. |
| Outcome: | The proposed framework outperforms existing approaches in simulated environments and on real mobile devices. |
Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments (2026.findings-acl)
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Junjie Ye, Changhao Jiang, Zhengyin Du, Yufei Xu, Xuesong Yao, Zhiheng Xi, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang, Jiecao Chen
| Challenge: | Currently, there are no efficient reinforcement learning (RL) frameworks specifically designed for tool use. |
| Approach: | They propose an automated environment construction pipeline that incorporates scenario decomposition, document generation, function integration, complexity scaling, and localized deployment to enable high-quality training environments without external tools. |
| Outcome: | The proposed framework significantly improves the models’ tool-use performance without degrading their general capabilities. |