MathFormer: Testing whether symbolic math is pattern matching or reasoning [D]

Reddit r/MachineLearning Papers
symbolic-math pattern-matching reasoning seq2seq language-models attention deep-learning

Summary

MathFormer is a small seq2seq model that achieves ~98.6% accuracy on symbolic math tasks, suggesting that mathematical reasoning in LLMs may be large-scale structured pattern completion rather than true reasoning.

Repo link and results - https://github.com/Abhinand20/MathFormer Task: Given a factorized expression like (7-3*z)*(-5*z-9), predict the expanded form -> 15*z\*2-8\*z-63 Key takeaway: A tiny (4M param) seq2seq model trained with no math knowledge reaches ~98.6% accuracy on symbolic math tasks, suggesting it learns structural token transformations rather than any notion of operators or variables. Scaling this up could help explain why LLMs appear to “reason” mathematically, when they may actually be performing large-scale structured pattern completion. How does RL change this paradigm given the inherent architecture is still based on attention?
Original Article

Similar Articles

Reasoning, Code, or Both? How Large Language Models Handle Variations in Math Questions

arXiv cs.AI

This paper evaluates three approaches (pure chain-of-thought reasoning, single-shot code execution, and iterative code execution) on 1,000 GSM-Symbolic problems using Claude Haiku 4.5, finding that chain-of-thought is the most robust to perturbation, while code execution does not improve reasoning robustness on grade-school math problems.

Math Reasoning in LLMs is Organized by Approach, Not Topic

arXiv cs.AI

This paper investigates whether large language models internally organize mathematical reasoning by topic or approach, finding evidence that approach is the key organizing principle, challenging traditional benchmarking methods.

MathVis: Can AI Reason through Drawing? A Benchmark for Mathematical Figure Generation

arXiv cs.AI

MathVis introduces a 512-task benchmark evaluating LLMs' ability to generate mathematical diagrams and perform diagram-assisted reasoning, with GPT-6-astra, Claude Fable 5, and Gemini 3.1 Pro achieving only 31.25%, 26.56%, and 22.85% Pass@1, revealing that models still rely primarily on symbolic reasoning rather than visual diagrams.

TabularMath: Understanding Math Reasoning over Tables with Large Language Models

arXiv cs.CL

TabularMath introduces a benchmark and AutoT2T framework for evaluating LLMs' mathematical reasoning over tabular data, revealing that table complexity, data quality, and modality significantly impact model performance. The study addresses a gap in LLM evaluation by systematically assessing robustness to incomplete or inconsistent table information in real-world scenarios.