2026 · Conference accepted
Agentic Reverse Engineering: From I/O to Code with Discovery Agent
Synthesizes executable programs from input/output observations using LLM-guided evolutionary search, progressively revealed examples, simple-to-complex mutations and execution-error feedback. An active variant chooses inputs and queries a hidden reference program.
Scope and limitations
IO2CodeBench has 125 generated tasks spanning basic operations, algorithms, geometry and image-to-value mappings, normally with eight visible and fifteen held-out examples. Active exploration covers a 25% subset from each level. Multimodal results average sample pass ratios, while other levels count tasks passing every held-out example. Finite test success does not establish original-source recovery or complete equivalence; arbitrary applications and stateful services are not evaluated.
Why it fits this collection
Provides a concrete observation-to-program reconstruction workflow and separates visible-example fitting from held-out behavior checks.
Publication and review details
- Venue
- NeurIPS 2026
- Status
- Conference accepted
- Publication / posting date
- 2026 (exact date not verified)
- Date source
- Official NeurIPS 2026 poster listing confirms acceptance, not a proceedings publication date. First arXiv submission was 2026-05-14; reviewed v2 was revised on 2026-10-18.
- First arXiv submission
- May 14, 2026
- arXiv
- 2605.15334
- Reviewed arXiv version
- v2
- DOI
- 10.48550/arxiv.2605.15334
The official poster and pinned repository retain the earlier title From I/O to Code with Discovery Agent; all ten authors match v2 and DataCite. The registered DOI identifies the arXiv work. The paper uses the arXiv non-exclusive license. Author-linked code was checked at ef15827ab7090bf2d227fb1eef51d91b0163339e: its README documents Python 3.10+, provider credentials, benchmark/oracle tasks and experiment runners. No repository-wide code/data license was verified in the reviewed README and root tree.
Sources reviewed
Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Primary sources
- https://arxiv.org/abs/2605.15334
- https://arxiv.org/html/2605.15334v2
- https://api.datacite.org/dois/10.48550/arxiv.2605.15334
- https://neurips.cc/virtual/2026/loc/sydney/poster/154711
- README.md GitHub · JiaruQian/IO2Code · Revision ef15827
- https://github.com/JiaruQian/IO2Code/tree/ef15827ab7090bf2d227fb1eef51d91b0163339e