KAPSO: A Knowledge-grounded framework for Autonomous Program Synthesis and Optimization
This work proposes a modular autonomous programming framework to address key challenges in long-horizon coding tasks, including fragile debugging, frequent loss of experimental state, and insufficient reuse of domain knowledge. The framework embeds program synthesis within a long-term optimization loop structured around the “conceive–generate–execute–evaluate–learn” cycle. It innovatively integrates a Git-native experimentation engine, a structured multi-source knowledge system encompassing code, documentation, and research papers, and a cognitive memory layer grounded in execution logs. This integration enables end-to-end optimization that is reproducible, knowledge-driven, and capable of accumulating experiential learning. Experiments on MLE-Bench and ALE-Bench demonstrate that the approach significantly reduces recurrent errors, accelerates convergence, and enhances overall performance.