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About this paper
Xuanle Zhao, Qiushi Sun, Jingyu Xiao, Xuexin Liu, Haoyue Yang, Qiaosheng Chen, Xianzhen Luo, Jing Huang and 11 more
This paper presents a structured survey of Multimodal Code Intelligence, a field extending Natural Language-to-Code by incorporating visual information. It categorizes existing tasks, benchmarks, and methodologies across various domains, highlighting the insufficiency of visual plausibility for evaluating generated code correctness and proposing a verification-centered research agenda to address current limitations.
Key insights
- Visual fidelity alone is an inadequate measure of correctness for multimodal code, as visually plausible outputs can contain fundamental errors in data semantics, structural integrity, or functional behavior.
- Code in multimodal contexts serves diverse roles—as a rendered artifact, editable structure, intermediate reasoning trace, or executable policy—each requiring distinct validation strategies.
- Limitations of current proxy metrics necessitate a comprehensive 'validator stack' that combines complementary evidence (visual, data, structural, executable) rather than relying on single, often misleading, metrics.
Summary by alphaXiv.
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