Based on Multi-Modal Context for Dynamic Program Generation

Abstract

This paper introduces a novel approach to dynamic program generation, termed "Based on Multi-Modal Context for Dynamic Program Generation," which moves beyond static template-based code modification by leveraging a combination of code, natural language descriptions, execution logs, and user interaction data. The core of this system is a reinforcement learning (RL) model that dynamically adapts and generates code in response to multi-modal input. The model learns to generate code that aligns with both the specified requirements and the observed behavior of the generated code during execution, guided by user feedback. We demonstrate that this multi-modal approach significantly improves the flexibility and adaptability of program generation compared to traditional methods that rely on single modality inputs or static templates. The key innovation lies in the model's ability to learn a continuous representation of the desired program state, enabling it to handle complex and dynamic programming scenarios. We outline the architecture, training methodology, and initial experimental results, showcasing the potential of this approach for automating software development. ---