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arXiv · 2510.25975

SymCode: A Neurosymbolic Approach to Mathematical Reasoning via Verifiable Code Generation

Sina Bagheri Nezhad·Yao Li·Ameeta Agrawal
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Abstract

Large Language Models (LLMs) often struggle with complex mathematical reasoning, where prose-based generation leads to unverified and arithmetically unsound solutions. Current prompting strategies like Chain of Thought still operate within this unreliable medium, lacking a mechanism for deterministic verification. To address these limitations, we introduce SymCode, a neurosymbolic framework that reframes mathematical problem-solving as a task of verifiable code generation using the SymPy library. We evaluate SymCode on challenging benchmarks, including MATH-500 and OlympiadBench, demonstrating significant accuracy improvements of up to 13.6 percentage points over baselines. Our analysis shows that SymCode is not only more token-efficient but also fundamentally shifts model failures from opaque logical fallacies towards transparent, programmatic errors. By grounding LLM reasoning in a deterministic symbolic engine, SymCode represents a key step towards more accurate and trustworthy AI in formal domains.

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Sina Bagheri Nezhad, Yao Li, Ameeta Agrawal. 2025-10-29. SymCode: A Neurosymbolic Approach to Mathematical Reasoning via Verifiable Code Generation. https://arxiv.org/abs/2510.25975

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