Papers by Jixuan Wang
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)
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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified. |
| Approach: | They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
| Outcome: | Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
Coordinated Replay Sample Selection for Continual Federated Learning (2023.emnlp-industry)
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Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard Zemel, Rahul Gupta
| Challenge: | Continual Federated Learning (CFL) combines decentralized learning with continuous learning . ubiquity of personal devices with a network connection offers rich source of data for learning problems . |
| Approach: | They propose to combine decentralized learning with a continuous learning approach . they propose to coordinate gradient-based replay sample selection across clients . |
| Outcome: | The proposed method shows gains early in the low replay size regime, when the budget for storing past data is small. |
On the data requirements of probing (2022.findings-acl)
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| Challenge: | Existing methods to probe neural networks are expensive and require large datasets. |
| Approach: | They propose a method to estimate the required number of data samples in probing datasets . they use a classification task to encode a text with a deep neural network . |
| Outcome: | The proposed method estimates the required number of data samples in two probing configurations and proves it is statistically powerful. |