PGS: Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback
Lehan He, Zeren Chen, Zhe Zhang, Xiang Gao, Lu Sheng
Abstract
LLMs excel at code generation, yet ensuring the functional correctness of their outputs remains a persistent challenge. While recent studies have applied Test-Driven Development (TDD) to refine code, these methods are often undermined by poor feedback quality, stemming from the scarcity of high-quality test cases and noisy signals from auto-generated ones. In this work, we shift the focus from test quantity to feedback quality. We introduce the Property-Generated Solver (PGS), a novel paradigm designed to generate highly effective feedback via two principles: it must be property-oriented, to provide semantic guidance beyond simple I/O mismatches, and structurally minimal, to reduce cognitive load and isolate root causes. PGS operates by checking high-level program properties (e.g., a sorting function must produce a non-decreasing sequence) then providing the simplest failing counterexample to the LLM. This property-driven, minimal feedback steers LLMs toward correct and generalizable solutions. Across diverse benchmarks, PGS demonstrates superior performance, achieving a bug fix rate 1.4x-1.6x higher than the strongest debugging-based approaches and establishing a new state-of-the-art in automated code refinement. Source code and data are available in the supplementary.
BibTeX
@inproceedings{
he2026pgs,
title={{PGS}: Effective {LLM} Code Refinement via Property-Oriented and Structurally Minimal Feedback},
author={Lehan He and Zeren Chen and Zhe Zhang and Xiang Gao and Lu Sheng},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=6xeSRifGeL}
}