Papers by Ran Fu
Beyond Blind Following: Evaluating Robustness of LLM Agents under Imperfect Guidance (2026.eacl-long)
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Yao Fu, Ran Qiu, Xinhe Wang, Jacob Sansom, Sathvika Ayyappa Prabhu, Huijie Tang, Jaekyeom Kim, Sungryull Sohn, Honglak Lee
| Challenge: | Large language models (LLMs) have shown strong capabilities as task-solving agents across interactive domains, but in complex environments, auxiliary guidance may be imperfect. |
| Approach: | They propose a benchmark to measure the robustness of large language models under imperfect guidance. |
| Outcome: | The proposed benchmark compared LLM agents in navigation, cooking, and gaming in a variety of environments with auxiliary guidance and noisy or underspecified instructions extracted from demonstrations. |
Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)
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Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang
| Challenge: | Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. |
| Approach: | They propose a new algorithm that uses a random sampling algorithm to control risk. |
| Outcome: | The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms. |
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit (P19-3)
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| Challenge: | NeuralClassifier is a toolkit for hierarchical multi-label text classification. |
| Approach: | They propose a toolkit for neural hierarchical multi-label text classification . they use a variety of text encoders to implement the model . |
| Outcome: | The proposed model achieves comparable performance with reported results in the literature. |