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arXiv · 2605.19382

PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning

Qiran Zhang·Yuheng Wang·Runde Yang·Lin Wu·Jingru Fan·Shu Yao·Jie Zhang·Tianle Zhou·Huatao Li·Ruijie Shi·Yihan Li·Chen Qian
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Abstract

Programmatic video generation through code offers geometric precision and temporal coherence beyond pixel-level diffusion models, yet rigorously evaluating whether language models can produce spatially correct animated outputs remains an open problem. We introduce PRISM, a large-scale benchmark of 10,372 human-calibrated instruction-code pairs (20 times larger than prior programmatic video generation benchmarks), grounded in real-world knowledge visualization scenarios across English and Chinese and spanning 437 subject categories. We further propose a funnel-style evaluation framework with four complementary metrics: Code-Level Reliability for executability, Spatial Reasoning for layout correctness over full animation sequences, and Prompt-Aware Dynamic Visual Complexity (PADVC) and Temporal Density (TD) for diagnosing dynamic expression and temporal activity. Systematic evaluation of seven mainstream LLMs reveals a striking Execution-Spatial Gap: the average drop from execution success rate to spatial pass rate is approximately 41%, showing that runnable code does not necessarily yield spatially coherent visual output. These findings show that programmatic video generation evaluation should go beyond executability. PRISM provides a principled benchmark for advancing spatially coherent code generation.

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Qiran Zhang, Yuheng Wang, Runde Yang, Lin Wu, Jingru Fan, Shu Yao, Jie Zhang, Tianle Zhou, Huatao Li, Ruijie Shi, Yihan Li, Chen Qian. 2026-05-19. PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning. https://arxiv.org/abs/2605.19382

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