SOURCE-LINKED INTELLIGENCE
SciWalker: Synthesizing Scientific Coding Problems with Operator Graphs and Execution Feedback
Improving the scientific coding capabilities of large language models (LLMs) requires high-quality training data. However, such data remain scarce because manually authoring realistic problems is costly and time-consuming, while systematically covering diverse scientific domains and algorithmic combinations remains challenging. To address this, we introduce SciWalker, a framework for synthesizing scientific coding problems through operator-chain sampling and execution feedback. The framework combines scientific library interfaces with operation modes to instantiate operators, organizes them in
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T16:12:14.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.