COOL: Efficient and Reliable Chain-Oriented Objective Logic with Neural Networks Feedback Control for Program Synthesis

Jipeng Han

Keywords: program synthesis, Chain-of-Logic, neural network feedback control

TL;DR: A novel method for complex program synthesis

Abstract:

Program synthesis methods, whether formal or neural-based, lack fine-grained control and flexible modularity, which limits their adaptation to complex software development. These limitations stem from rigid Domain-Specific Language (DSL) frameworks and neural network incorrect predictions. To this end, we propose the \textbf{Chain of Logic (CoL)}, which organizes synthesis stages into a chain and provides precise heuristic control to guide the synthesis process. Furthermore, by integrating neural networks with libraries and introducing a \textbf{Neural Network Feedback Control (NNFC)} mechanism, our approach modularizes synthesis and mitigates the impact of neural network mispredictions. Experiments on relational and symbolic synthesis tasks show that CoL significantly enhances the efficiency and reliability of DSL program synthesis across multiple metrics. Specifically, CoL improves accuracy by 70\% while reducing tree operations by 91\% and time by 95\%. Additionally, NNFC further boosts accuracy by 6\%, with a 64\% reduction in tree operations under challenging conditions such as insufficient training
data, increased difficulty, and multidomain synthesis. These improvements confirm COOL as a highly efficient and reliable program synthesis framework.

Primary Area: reinforcement learning

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Submission Number: 2964