Benchmarking Large Language Models on Floating-Point Error Classification

Lisa Taldir; Muhammad Ahmad Saeed; David Defour; Pablo de Oliveira Castro; Eric Petit

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arXiv · 2606.31308

Benchmarking Large Language Models on Floating-Point Error Classification

Lisa Taldir·Muhammad Ahmad Saeed·David Defour·Pablo de Oliveira Castro·Eric Petit

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Abstract

This paper investigates the capability of Large Language Models (LLMs) to detect and classify floating-point errors statically in software code. We introduce InterFLOPBench, a benchmark of 90 C kernels with 1 130 test samples designed to evaluate LLMs across six categories of floating-point error: cancellation, comparison, division by zero, overflow, underflow and NaN, compared across 14 LLMs. The evaluation framework treats floating-point error detection as a multi-label classification problem and employs the F1-score metric to measure performance. Results demonstrate that latest models (Qwen 3 32b, Gemini 2.5 Flash, Phi 4 Reasoning, DeepSeek R1T2, and gpt-oss 20b and 120b) achieve a performance greater than 0.88 overall F1-score. Performance varies between error categories, between explicit operations such as division by zero (Average F1-score: 0.8479) and more subtle numerical phenomena such as underflow (Average F1-score: 0.6059) and cancellation (Average F1-score: 0.6164).

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Lisa Taldir, Muhammad Ahmad Saeed, David Defour, Pablo de Oliveira Castro, Eric Petit. 2026-06-30. Benchmarking Large Language Models on Floating-Point Error Classification. https://arxiv.org/abs/2606.31308

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