#pattern matching

MathFormer, a 4-million parameter seq2seq model, achieves 98.6% accuracy on symbolic math expansion tasks without any prior mathematical knowledge, suggesting it learns structural token transformations rather than true reasoning. This finding challenges the common assumption that large language models (LLMs) 'reason' mathematically, implying their performance may stem from sophisticated pattern completion. Understanding this distinction is crucial for developing models with genuine reasoning capabilities. The model uses a GPT-style transformer architecture and is trained solely on token-level sequence mapping from factorized to expanded polynomial expressions, without any encoding of mathematical operators or variable semantics.