Research and Analysis of Latency Diagnosis and Structured Prompt Optimization for High-Difficulty Mathematical Reasoning Based on DeepSeek-R1

School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

In view of the extremely high reasoning delay caused by “excessive thinking” in large models such as DeepSeek-R1, this paper built a pollution-free data set based on the real math problems of the postgraduate entrance examination, and innovatively introduced the “Time-Token” two-dimensional evaluation system. The experiment shows that using the “Analysis-Derivation-Conclusion” three-stage structured prompt and single sample guidance, the model significantly reduces the invalid verification while maintaining 100% accuracy, and the time consumption of complex problems drops from 408 seconds to 55 seconds (86.6% faster). In addition, token analysis objectively reveals the computational cost of “Token Inflation” inevitably caused by the typesetting of high-dimensional matrix LaTeX. The research confirms that external structural constraints can effectively balance the logical rigor and computational cost of large-scale models. In the future, this paper will further explore the dynamic prompt routing mechanism to achieve a dynamic balance between the computational efficiency of the large model and divergent innovation.