Large language models (LLMs) have revolutionized the field of code generation, demonstrating impressive capabilities in automating programming tasks and assisting developers. Despite these advancements, current methods primarily rely on pattern recognition from static code representations, often producing syntactically plausible but functionally incorrect code. Many of these models lack the ability to explicitly reason about runtime behavior, leading to solutions that may fail during execution despite passing superficial tests. This gap between syntactic pattern recognition and practical executability presents a significant challenge in achieving reliable, real-world code synthesis.

UNMET NEED
A critical limitation of existing neural code generation approaches is their absence of dynamic feedback during the inference process. Human programmers execute code fragments iteratively to detect errors, understand program behavior, and refine their solutions accordingly. Automated systems, however, typically generate complete solutions in a single pass without leveraging real-time execution insights. This disconnects results in a high rate of non-executable or partially correct code, reducing the effectiveness of automated code generation tools—particularly in applications requiring high reliability or safety guarantees.

OUR SOLUTION
To address this challenge, we propose a novel methodology called Execution-Guided Classifier-Free Guidance (EG-CFG). This approach integrates real-time execution signals directly into the code generation process, enabling dynamic, line-by-line refinement of programs. It works in multiple stages: first, generating candidate completions for each line using beam search; second, executing these candidates against test cases to gather feedback; and third, incorporating these execution signals into subsequent generation steps. By doing so, EG-CFG continuously guides the model toward producing executable and correct code, mimicking the iterative debugging process of human programmers and significantly improving the quality of generated solutions.

APPLICATIONS
• The primary application of this technology is in automated code synthesis for software engineering, educational tools, and AI-assisted programming. It can be employed in environments where producing reliable, executable code is crucial, such as in software development pipelines, automated testing frameworks, and safety-critical systems.
• Additionally, EG-CFG can enhance tools designed for assisting novice programmers by providing more accurate and functional code suggestions, ultimately accelerating learning and reducing debugging time. Its ability to incorporate execution feedback makes it a promising approach for advancing the robustness and reliability of AI-driven code generation across diverse domains.

INTELLECTUAL PROPERTY
Provisional patent application