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REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming

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REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming
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Computer Science > Software Engineering arXiv:2607.07738 (cs) [Submitted on 7 Jul 2026] Title:REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming Authors:Nicolas Koller, Andreas u. Schmidt View a PDF of the paper titled REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming, by Nicolas Koller and Andreas u. Schmidt View PDF Abstract:Large language models (LLMs) are increasingly applied to reverse-engineering tasks, and recent threat-intelligence reporting shows them operating inside live offensive-security workflows.

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Computer Science > Software Engineering arXiv:2607.07738 (cs) [Submitted on 7 Jul 2026] Title:REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming Authors:Nicolas Koller, Andreas u. Schmidt View a PDF of the paper titled REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming, by Nicolas Koller and Andreas u. Schmidt View PDF Abstract:Large language models (LLMs) are increasingly applied to reverse-engineering tasks, and recent threat-intelligence reporting shows them operating inside live offensive-security workflows. Claims about their capability, however, outpace our ability to measure it.

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