See-Saw Generative Mechanism for Scalable Recursive Code Generation with Generative AI

TL;DR

The See-Saw generative mechanism is introduced, a novel methodology for dynamic and recursive code generation that alternates between main code updates and dependency generation to ensure alignment and functionality.

Abstract

The generation of complex, large-scale code projects using generative AI models presents challenges due to token limitations, dependency management, and iterative refinement requirements. This paper introduces the See-Saw generative mechanism, a novel methodology for dynamic and recursive code generation. The proposed approach alternates between main code updates and dependency generation to ensure alignment and functionality. By dynamically optimizing token usage and incorporating key elements of the main code into the generation of dependencies, the method enables efficient and scalable code generation for projects requiring hundreds of interdependent files. The mechanism ensures that all code components are synchronized and functional, enabling scalable and efficient project generation. Experimental validation demonstrates the method's capability to manage dependencies effectively while maintaining coherence and minimizing computational overhead.

Figures (5)

• Figure 1: See-Saw mechanism Workflow for single Main Code iteration. The process alternates between See and Saw steps for each dependency, recursively aligning components and updating the main code $M$ as needed. Iterations are tracked until the alignment condition is satisfied.
• Figure 2: Comparison of Dependency Types (Token Usage) between Standard Approach and See-Saw mechanism.
• Figure 3: Token Usage Over Run Time for Standard Approach and See-Saw mechanism.
• Figure 4: Execution Time Trends Over Iterations for Standard Approach and See-Saw mechanism.
• Figure 5: Token Usage Over Iterations for the Standard Approach and the See-Saw Mechanism.