Behavioral Fingerprinting of Large Language Models

• URL: http://arxiv.org/abs/2509.04504v1
• Date: Tue, 02 Sep 2025 07:03:20 GMT
• Title: Behavioral Fingerprinting of Large Language Models
• Authors: Zehua Pei, Hui-Ling Zhen, Ying Zhang, Zhiyuan Yang, Xing Li, Xianzhi Yu, Mingxuan Yuan, Bei Yu,
• Abstract summary: Current benchmarks for Large Language Models (LLMs) primarily focus on performance metrics.<n>This paper introduces a novel Behavioral Fingerprinting'' framework designed to move beyond traditional evaluation.
• Score: 35.18856642496912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current benchmarks for Large Language Models (LLMs) primarily focus on performance metrics, often failing to capture the nuanced behavioral characteristics that differentiate them. This paper introduces a novel ``Behavioral Fingerprinting'' framework designed to move beyond traditional evaluation by creating a multi-faceted profile of a model's intrinsic cognitive and interactive styles. Using a curated \textit{Diagnostic Prompt Suite} and an innovative, automated evaluation pipeline where a powerful LLM acts as an impartial judge, we analyze eighteen models across capability tiers. Our results reveal a critical divergence in the LLM landscape: while core capabilities like abstract and causal reasoning are converging among top models, alignment-related behaviors such as sycophancy and semantic robustness vary dramatically. We further document a cross-model default persona clustering (ISTJ/ESTJ) that likely reflects common alignment incentives. Taken together, this suggests that a model's interactive nature is not an emergent property of its scale or reasoning power, but a direct consequence of specific, and highly variable, developer alignment strategies. Our framework provides a reproducible and scalable methodology for uncovering these deep behavioral differences. Project: https://github.com/JarvisPei/Behavioral-Fingerprinting

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