Papers by Xiaowen Lin
Self-Evolving Multi-Agent Systems via Textual Backpropagation (2026.findings-acl)
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Xiaowen Ma, Yunpu Ma, Chenyang Lin, Sikuan Yan, Jinhe Bi, Zixuan Cao, Yijun Tian, Volker Tresp, Hinrich Schuetze
| Challenge: | Large Language Models (LLMs) have proven effective for addressing complex, high-dimensional tasks, but current approaches rely on static, manually engineered multi-agent configurations. |
| Approach: | They propose a framework that conceptualizes multi-agent collaboration as a layered neural network architecture. |
| Outcome: | The proposed framework surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements. |
Red-Teaming NSFW Image Classifiers as Text-to-Image Safeguards (2026.findings-acl)
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Tinghao Xie, Yueqi Xie, Alireza Zareian, Shuming Hu, Felix Juefei-Xu, Xiaowen Lin, Ankit Jain, Prateek Mittal, Li Chen
| Challenge: | Not Safe for Work (NSFW) image classifiers play a critical role in safeguarding text-to-image systems. |
| Approach: | They propose an automated red-teaming framework that leverages a set of generative AI tools to uncover NSFW image failures. |
| Outcome: | The proposed framework uncovers and interprets failure modes and enables it to be applied to real-world T2I and T2V systems. |
AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)
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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin
| Challenge: | Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora . |
| Approach: | They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process . |
| Outcome: | The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query . |
Answering Complex Open-domain Questions Through Iterative Query Generation (D19-1)
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| Challenge: | Currently, one-step retrieve-and-read question answering systems cannot answer such questions because they rarely contain retrievable clues about the missing entity. |
| Approach: | They propose a multi-step approach to retrieve relevant content with the question, then reading the paragraphs returned by the information retrieval component to arrive at the final answer. |
| Outcome: | The proposed model outperforms the best previously published model despite not using pretrained language models such as BERT. |