# agbcc: SLaDe-AGBCC: Neural Matching Decompilation > Source: > Published: 2026-10-28 23:01:02+00:00 ## Evidence at a glance Loading source evidence… ## What was done SLaDe-AGBCC is a research project using LLMs to perform matching decompilation on GBA agbcc assembly. It treats C recovery as a folding problem, aiming for byte-identical output via compiler-in-the-loop recycling. SLaDe-AGBCC is a research project investigating neural matching decompilation for the Game Boy Advance. Unlike traditional decompilers, it does not prioritize readable structure or name recovery; instead, it optimizes for producing C code that recompiles to byte-identical assembly when processed by the `agbcc` compiler. The project draws inspiration from AlphaFold 2, treating C recovery as a folding problem and utilizing a compiler-in-the-loop recycling signal to refine predictions. The author reports that the core hypothesis—that transformers can defeat the `agbcc` optimizer to achieve byte-exactness—is currently being tested rather than assumed. Development involves an automated pipeline where disassembled objects are converted to C and iteratively refined. The project relies on a curated corpus of GBA decompilation projects and decomp.me data. Recent experiments include v4 recycling, which demonstrated progress in loop-based matching, though the author notes that recycling must be explicitly trained. The project documentation credits Claude Fable 5 for co-authorship on various implementation and diagnostic tasks. Sources: [Source 1](https://raw.githubusercontent.com/jrickey/slade-agbcc/HEAD/README.md) ## Reproduce it ``` uv venv && uv pip install -e . && cd vendor/agbcc && ./build.sh && cd - && python scripts/measure_corpus.py ``` ## Community Ask a question, share what worked, or follow the next milestone. [Your library](https://reversedwithai.com/library)