Jianmin Guo — accepted AI-conference papers

2025

Superpose Task-specific Features for Model Merging

Haiquan Qiu, You Wu, Dong Li, Jianmin Guo, Quanming Yao

EMNLP 2025

Model merging enables powerful capabilities in neural networks without requiring additional training. In this paper, we introduce a novel perspective on model merging by leveraging the fundamental mechanisms of neural network representation. Our approach is motivated by the linear representation hyp

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2025

ToolSafety: A Comprehensive Dataset for Enhancing Safety in LLM-Based Agent Tool Invocations

Yuejin Xie, Youliang Yuan, Wenxuan Wang, Fan Mo, Jianmin Guo, Pinjia He

EMNLP 2025

LLMs are evolving into assistants that leverage tools, significantly expanding their capabilities but also introducing critical safety risks. Current models exhibit notable vulnerabilities, particularly in maintaining safety during multi-step tool interactions and in scenarios involving indirect har

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