Program Synthesis via Test-Time Transduction

• URL: http://arxiv.org/abs/2509.17393v3
• Date: Tue, 21 Oct 2025 07:02:56 GMT
• Title: Program Synthesis via Test-Time Transduction
• Authors: Kang-il Lee, Jahyun Koo, Seunghyun Yoon, Minbeom Kim, Hyukhun Koh, Dongryeol Lee, Kyomin Jung,
• Abstract summary: We introduce transductive program synthesis, a new formulation of the program synthesis task that explicitly leverages test inputs during synthesis.<n>We evaluate our approach on four benchmarks: Playgol, MBPP+, 1D-ARC, and programmatic world modeling on MiniGrid.<n>We demonstrate that our method significantly improves program synthesis in both accuracy and efficiency.
• Score: 26.30808249424997
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce transductive program synthesis, a new formulation of the program synthesis task that explicitly leverages test inputs during synthesis. While prior approaches to program synthesis--whether based on natural language descriptions or input-output examples--typically aim to generalize from training examples, they often struggle with robustness, especially in real-world settings where training examples are limited and test inputs involve various edge cases. To address this, we propose a novel framework that improves robustness by treating synthesis as an active learning over a finite hypothesis class defined by programs' outputs. We use an LLM to predict outputs for selected test inputs and eliminate inconsistent hypotheses, where the inputs are chosen via a greedy maximin algorithm to minimize the number of LLM queries required. We evaluate our approach on four benchmarks: Playgol, MBPP+, 1D-ARC, and programmatic world modeling on MiniGrid. We demonstrate that our method significantly improves program synthesis in both accuracy and efficiency. We release our code at https://github.com/klee972/SYNTRA.

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