8 citations · 9 across the 13 of their papers we have counts for
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cs.HC2026
Looks Right, Works Right: A Project-Level Benchmark for Multi-Screen Mobile App Generation
Fan Wu, Cuiyun Gao, Yiming Huang +3
Recent multimodal large language models can convert visual designs directly into executable code, but real mobile products require multiple screenshots to become a buildable codeba…
cs.SE2026
Benchmarking Multimodal LLMs on Code Generation for Complex Interactive Webpages
Fan Wu, Lishuai Dong, Cuiyun Gao +4
Recent advancements in multimodal large language models (MLLMs) have achieved remarkable progress in multimodal reasoning and code generation, catalyzing a new paradigm for front-e…
cs.LG2026
Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning
Zhanyue Qin, Jia Feng, Yibo Lyu +4
Code reasoning refers to the task of predicting the output of a program given its source code and specific inputs. It can measure the reasoning capability of large language models…