Abstract: Automatically answer math word problems is a challenging task in artificial intelligence. Previous solvers constructed mathematical expressions in sequence or binary tree. However, these approaches may suffer from the following issues: Models relying on such structures exhibit fixed-order reasoning (e.g., left-to-right), limiting flexibility and increasing error susceptibility; prior models rely on autoregressive reasoning in a single pass, accumulating minor errors (e.g., incorrect math symbols) during generation, resulting in reduced accuracy. To address the above issues, we emulate the human “check and modify” process in reasoning and propose a unified M-tree self-correction solver (UTSC-Solver) by iterative inference with self-correction mechanism. First, we use an iterative, non-autoregressive process for generating mathematical expressions, free from fixed generation orders to handle complex and diverse problems. Additionally, we design a self-correction mechanism based on alternating execution between a generator and a discriminator. This module iteratively detects and rectifies errors in generated expressions, leveraging previous iteration information for subsequent generation guidance. Experimental results show that our UTSC-Solver outperforms traditional models in accuracy on two popular datasets, while it improves the interpretability of mathematical reasoning.