Top-Down Synthesis for Library Learning
Open accessAbstract
This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern matching of intermediate abstractions to intelligently prune the search space and guide the algorithm towards abstractions that maximally capture shared structures in the corpus. We present an implementation of the approach in a tool called Stitch and evaluate it against the state-of-the-art deductive library learning algorithm from DreamCoder. Our evaluation shows that Stitch is 3-4 orders of magnitude faster and uses 2 orders of magnitude less memory while maintaining comparable or better library quality (as measured by compressivity). We also demonstrate Stitch’s scalability on corpora containing hundreds of complex programs that are intractable with prior deductive approaches and show empirically that it is robust to terminating the search procedure early—further allowing it to scale to challenging datasets by means of early stopping.
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- Authors
- Bowers, M., Olausson, T. X., Wong, L., Grand, G., Tenenbaum, J. B., Ellis, K., & Solar-Lezama, A.
- Affiliations
- Massachusetts Institute of Technology (US)
- Cornell University (US)
- ORCID
- Venue
- Proceedings of the ACM on Programming Languages
- Published
- 2023-01-09
- Volume
- 7
- Issue
- POPL
- Pages
- 1182-1213
- ISSN
- 2475-1421 (ISSN-L)
- OpenAlex
- W4310557242
- Cited by
- 33
- References
- 66
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 7 · 2023: 9
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Proceedings of the ACM on Programming Languages (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- arXiv (Cornell University) (cc-by)
- Funders
- NSF (2141064,1918839)
- DARPA (FA8750-20-C-0542)
- AFOSR (FA9550-19-1-026)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, programming, and type systems
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- top-down synthesis, library learning, deductive synthesis, domain-specific languages, program synthesis, search space pruning, stitches, program compression, early stopping
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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