The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution
Chen Qian, Peng Wang, Dongrui Liu†, Junyao Yang, Dadi Guo, Ling Tang, Jilin Mei, Qihan Ren, Shuai Shao, Yong Liu, Jie Fu, Jing Shao, Xia Hu
† Corresponding author
A hierarchical attribution framework explains the historical factors driving LLM-based agent actions, independent of task success or failure. Temporal likelihood identifies critical interaction components, and perturbation analysis localizes influential sentences.