2026 · Preprint
Binary Decompilation LLM with Feedback-Driven Multi-Turn Refinement
AutoDecompiler trains function-level decompilation models to revise assembly- or pseudocode-derived outputs using compilation, execution and I/O feedback. Its reinforcement-learning design combines diagnostic feedback with rewards for valid code, tested behavior, syntax and data-flow structure.
Scope and limitations
Evaluation uses C adaptations of HumanEval and MBPP with validation feedback; finite tests do not establish general equivalence. HumanEval also informs reward-weight selection, and base-model pretraining exposure cannot be ruled out. Refinement is context-limited, whole-program dependencies are not recovered, and unseen toolchains or protected binaries remain limitations.
Why it fits this collection
Adds reinforcement-learning-trained use of program feedback, with separately recorded first submission and latest revision.
Publication and review details
- Venue
- arXiv
- Status
- Preprint
- Publication / posting date
- June 15, 2026
- Date source
- First arXiv submission on 2026-06-15; the reviewed v2 revision is 2026-10-03, not a separate publication.
- First arXiv submission
- June 15, 2026
- arXiv
- 2606.16162
- Reviewed arXiv version
- v2
First submitted 2026-06-15; reviewed v2 is dated 2026-10-03. The arXiv record uses the shorter catalog title, while the v2 PDF/HTML use a longer title beginning “AutoDecompiler: Reinforcement-Learning-Trained”; eight authors match. Zenodo version 23120286 lists code/data archives under CC BY 4.0. The inspected Hugging Face model has no license metadata.
Sources reviewed
Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.