LLM4FP: LLM-Based Program Generation for Triggering Floating-Point Inconsistencies Across Compilers
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912790619357184 |
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| author | Wang, Yutong Rubio-González, Cindy |
| author_facet | Wang, Yutong Rubio-González, Cindy |
| contents | Floating-point inconsistencies across compilers can undermine the reliability of numerical software. We present LLM4FP, the first framework that uses Large Language Models (LLMs) to generate floating-point programs specifically designed to trigger such inconsistencies. LLM4FP combines Grammar-Based Generation and Feedback-Based Mutation to produce diverse and valid programs. We evaluate LLM4FP across multiple compilers and optimization levels, measuring inconsistency rate, time cost, and program diversity. LLM4FP detects nearly 2.5x the number of inconsistencies as the state-of-the-art tool Varity. Notably, most of the inconsistencies involve real-valued differences, rather than extreme values like NaN or infinities. LLM4FP also uncovers inconsistencies across a wider range of optimization levels, and finds the most mismatches between host and device compilers. These results show that LLM-guided program generation improves the detection of numerical inconsistencies. In practice, numerical software and HPC developers can use LLM4FP to compare compilers and select those that provide more accurate and consistent floating-point behavior, while compiler developers can use it to identify and address subtle consistency issues in their implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM4FP: LLM-Based Program Generation for Triggering Floating-Point Inconsistencies Across Compilers Wang, Yutong Rubio-González, Cindy Software Engineering Floating-point inconsistencies across compilers can undermine the reliability of numerical software. We present LLM4FP, the first framework that uses Large Language Models (LLMs) to generate floating-point programs specifically designed to trigger such inconsistencies. LLM4FP combines Grammar-Based Generation and Feedback-Based Mutation to produce diverse and valid programs. We evaluate LLM4FP across multiple compilers and optimization levels, measuring inconsistency rate, time cost, and program diversity. LLM4FP detects nearly 2.5x the number of inconsistencies as the state-of-the-art tool Varity. Notably, most of the inconsistencies involve real-valued differences, rather than extreme values like NaN or infinities. LLM4FP also uncovers inconsistencies across a wider range of optimization levels, and finds the most mismatches between host and device compilers. These results show that LLM-guided program generation improves the detection of numerical inconsistencies. In practice, numerical software and HPC developers can use LLM4FP to compare compilers and select those that provide more accurate and consistent floating-point behavior, while compiler developers can use it to identify and address subtle consistency issues in their implementations. |
| title | LLM4FP: LLM-Based Program Generation for Triggering Floating-Point Inconsistencies Across Compilers |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2509.00256 |