Papers by Yaxin Du
VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization (2026.findings-acl)
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| Challenge: | integrating vision and language models with safety standards is essential to mitigate multimodal complexity . integrating visual inputs with vision and text unveils subtle threats beyond the reach of conventional safeguards . |
| Approach: | They propose a framework that combines vision and language to provide a multimodal reasoning-driven prompt rewriting. |
| Outcome: | The proposed framework outperforms baseline models on five benchmarks with six VLMs. |
Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models (2025.findings-emnlp)
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| Challenge: | Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data. |
| Approach: | They introduce an algorithm that explicitly maximizes cross-client domain coverage through diversity-oriented client center selection and retrieval-based augmentation. |
| Outcome: | The proposed algorithm achieves performance gains of 29.19% and domain coverage improvements of 4.82%-21.36% over 11 baselines. |
MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools (2026.acl-long)
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WenHao Wang, Peizhi Niu, Zhao Xu, Zhaoyu Chen, Jian Du, Yaxin Du, Xianghe Pang, Keduan Huang, Yanfeng Wang, Qiang Yan, Siheng Chen
| Challenge: | Existing research on Large Language Models (LLMs) relies on few servers and lacks training support. |
| Approach: | They propose a web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training that collects and filters data from 1166 servers and 11536 tools. |
| Outcome: | Empirical evidence shows that MCP-Flow generates higher quality instruction-function call pairs and higher agentic task performance than previous work. |
FedDQC: Data Quality Control in Federated Instruction-tuning of Large Language Models (2025.findings-acl)
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| Challenge: | Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models. |
| Approach: | They propose a federated instruction tuning framework with dynamic data quality control to solve this problem. |
| Outcome: | The proposed framework improves performance on mixed-quality datasets on synthetic and real-world datasets. |