First Systematic Study of LLM-as-a-Judge for Human-Oriented Binary Reverse Engineering

A recent paper on arXiv (2608.07038) introduces the inaugural comprehensive study of the LLM-as-a-Judge framework for assessing outputs from Human-Oriented Binary Reverse Engineering (HOBRE). HOBRE seeks to convert decompiled pseudocode into formats that are more accessible to humans, thereby alleviating cognitive load and enhancing productivity. This research tackles the core issue of effectively evaluating HOBRE outputs: human assessments are expensive and not scalable, while current automated metrics either necessitate executable test cases and runtime settings often missing for real-world binaries or depend on high-quality source code that is usually unavailable and fails to recognize semantically equivalent yet lexically varied outputs. The authors, a group of computer scientists, examined the LLM-as-a-Judge approach in three key areas: function name recovery, binary code summarization, and improving the readability of decompiled code. This significant work for binary analysis, software engineering, and AI evaluation proposes a scalable, reference-free evaluation method for real-world binaries. The paper can be accessed at https://arxiv.org/abs/2608.07038.