AAAI Conference 2026 Short Paper
Self-Guided Planning and Repair Framework for Code Generation (Student Abstract)
- Chun-Wei Kang
- Chung-Chi Chen
- An-Zi Yen
Large Language Models (LLMs) demonstrate strong capabilities in code generation but often lack adaptability in planning and refinement. We propose Self-PR, a framework that integrates adaptive plan selection and iterative repair to improve correctness and generalization. Self-PR constructs a reusable plan database via task clustering and trains a selector to choose task-specific strategies. Incorrect outputs are refined through multi-round feedback until correctness. Trained only on HumanEval, Self-PR generalizes well to out-of-distribution tasks (MBPP), improving pass@1 by +4.9% on HumanEval and +5.5% on MBPP compared to Modularization-of-Thought prompting. Experiments across Llama-3 (8B, 70B) and GPT-4o-mini confirm robustness and scalability. These findings suggest that adaptive planning and feedback-driven repair are essential for reliable LLM-based code generation.