How LLM Evaluation Produces Comparable Numbers: Inside the LFORLA Reverse Engineering Benchmark

TL;DR LLM evaluation only becomes useful when every model faces the same prompts, the same fixed judge, per-axis rubrics, and a public verbatim trail. The LFORLA Reverse Engineering benchmark does exactly that: it restores C source from stripped binaries, scores with deterministic token similarity against server-only references, and publishes what each model actually answered. Here is how the numbers are made, what the current leaderboard shows, and how to read it without fooling yourself. The benchmark is called Reverse Engineering (Binary to Source). The task is blunt: given a stripped binar