ScratchNet: A Multi-modal Benchmark for Evaluating and Advancing LLMs on Scratch Programming Tasks

Large language models (LLMs) have achieved impressive performance on text-based programming tasks, yet they remain unreliable for block-based languages such as Scratch. Scratch programs feature deeply nested, nonlinear structures, event-driven concurrency across multiple sprites, and tight coupling between code and multimedia assets---properties that differ fundamentally from textual code. Consequently, LLMs frequently misinterpret Scratch semantics and propose large, invasive edits that are syntactically valid but semantically misaligned when repairing buggy programs. We introduce ScratchNet, the first executable benchmark designed to systematically evaluate and advance LLM-based repair for Scratch programs. The benchmark comprises 100 carefully curated projects from the public Scratch repository, each selected for high structural and semantic complexity. Every project is paired with an executable test suite, a bug description and corresponding fix, block-level edit constraints that define a minimal semantically correct repair, and the multimedia assets required for faithful execution. We construct the benchmark through a human-in-the-loop pipeline that combines automated project mining with expert validation of trigger--mechanism--outcome semantics and representative bug patterns, with particular emphasis on event ordering, concurrency, and state management. To enable rigorous and reproducible evaluation, we propose a three-layer executable protocol that measures functional correctness through VM-level execution, repair quality through block-level edit distance and behavioral trajectory comparisons, and explanation quality through structured rubrics. Using this benchmark, we study project and bug understanding, trigger and mechanism identification, functional repair, and the effect of lightweight domain adaptation. ScratchNet establishes a reproducible foundation and a closed-loop framework for evaluating and post-training LLMs on block-based programming tasks.