mlsanityData to 7 Oct 2026

Neuro-Symbolic Execution of Generic Source Code

arXiv 2304.00989 ↗First posted 23 Mar 2023cs.AI cs.LGcited by 1

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Abstract (arXiv)

Can a Python program be executed statement-by-statement by neural networks composed according to the source code? We formulate the Neuro-Symbolic Execution Problem and introduce Neural Interpretation (NI), the first neural model for the execution of generic source code that allows missing definitions. NI preserves source code structure, where every variable has a vector encoding, and every function executes a neural network. NI is a novel neural model of computers with a compiler architecture that can assemble neural layers "programmed" by source code. NI is the first neural model capable of executing Py150 dataset programs, including library functions without concrete inputs, and it can be trained with flexible code understanding objectives. We demonstrate white-box execution without concrete inputs for variable misuse localization and repair.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 37 of its 83 references that are in the map. First posted on arXiv 23 Mar 2023.

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37 of 83 references.

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