50 citations · 75 across the 19 of their papers we have counts for
20 papers
VisualRepair: Dynamic Tool Calling and Region Focusing for Visual Software Issue Repair
Jingyu Xiao, Zhongyi Zhang, Haoran Hou +4
Automated Program Repair (APR) has witnessed significant progress with the advent of Large Language Models (LLMs). However, as modern software systems increasingly expose rich grap…
MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models
Tsz Hei Fan, Choi Wing Fung, Yuxuan Wan +2
Multi-scene navigation (clearing an objective in one bounded space and then crossing a portal into the next) is a defining feature of contemporary 3D games, but authoring it is lab…
UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization
Yaozhi Zheng, Yilei Jiang, Manyuan Zhang +5
Visual-to-Code generation, which transforms scientific plots, vector graphics, and webpages into executable scripts, demands a level of pixel-precise alignment that standard Multim…
From Runnable to Shippable: Multi-Agent Test-Driven Development for Generating Full-Stack Web Applications from Requirements
Yuxuan Wan, Tingshuo Liang, Jiakai Xu +3
Coding agents can generate runnable web applications, but their outputs frequently fail to satisfy functional requirements. Although test-driven development (TDD) offers a principl…
UIBenchKit: A unified toolkit for design-to-code model evaluation
Chinh T. Le, Trevor Ong Yee Siang, Jingyu Xiao +2
Recent years have seen substantial progress in automated design-to-code generation, with many methods proposed for generating HTML and CSS from webpage screenshots. However, the ab…
Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification
Yuxuan Wan, Tianqing Fang, Zaitang Li +5
Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. While the majority of existing efforts focus on enhancing policy…
TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework
Shuzheng Gao, Eric John Li, Man Ho Lam +5
Large foundation models are fundamentally transforming the software engineering landscape, demonstrating exceptional capabilities across diverse tasks such as code generation, debu…
Automatically Generating Web Applications from Requirements Via Multi-Agent Test-Driven Development
Yuxuan Wan, Tingshuo Liang, Jiakai Xu +3
Developing full-stack web applications is complex and time-intensive, demanding proficiency across diverse technologies and frameworks. Although recent advances in multimodal large…
90% Faster, 100% Code-Free: MLLM-Driven Zero-Code 3D Game Development
Runxin Yang, Yuxuan Wan, Shuqing Li +1
Developing 3D games requires specialized expertise across multiple domains, including programming, 3D modeling, and engine configuration, which limits access to millions of potenti…
EfficientUICoder: A Bidirectional Token Compression Framework for Efficient MLLM-Based UI Code Generation
Jingyu Xiao, Zhongyi Zhang, Yuxuan Wan +3
Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development efficiency. However, these tasks incur subs…
Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training
Tianqing Fang, Zhisong Zhang, Xiaoyang Wang +16
General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reasoning, web interaction, coding, an…
ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents
Yilei Jiang, Yaozhi Zheng, Yuxuan Wan +4
Automating the transformation of user interface (UI) designs into front-end code holds significant promise for accelerating software development and democratizing design workflows.…
DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Jingyu Xiao, Ming Wang, Man Ho Lam +4
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in automated front-end engineering, e.g., generating UI code from visual designs. However, existi…
MRWeb: An Exploration of Generating Multi-Page Resource-Aware Web Code from UI Designs
Yuxuan Wan, Yi Dong, Jingyu Xiao +3
Multi-page websites dominate modern web development. However, existing design-to-code methods rely on simplified assumptions, limiting to single-page, self-contained webpages witho…
Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping
Jingyu Xiao, Yuxuan Wan, Yintong Huo +6
Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance on the design-to-code task, i.e., generating UI code from UI mock-ups. However, existing benchmark…
Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach
Yuxuan Wan, Chaozheng Wang, Yi Dong +4
Websites are critical in today's digital world, with over 1.11 billion currently active and approximately 252,000 new sites launched daily. Converting website layout design into fu…
New Job, New Gender? Measuring the Social Bias in Image Generation Models
Wenxuan Wang, Haonan Bai, Jen-tse Huang +5
Image generation models can generate or edit images from a given text. Recent advancements in image generation technology, exemplified by DALL-E and Midjourney, have been groundbre…
LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models
Yuxuan Wan, Wenxuan Wang, Yiliu Yang +5
We introduce LogicAsker, a novel approach for evaluating and enhancing the logical reasoning capabilities of large language models (LLMs) such as ChatGPT and GPT-4. Despite LLMs' p…
BiasAsker: Measuring the Bias in Conversational AI System
Yuxuan Wan, Wenxuan Wang, Pinjia He +3
Powered by advanced Artificial Intelligence (AI) techniques, conversational AI systems, such as ChatGPT and digital assistants like Siri, have been widely deployed in daily life. H…
ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark
Haoran Wu, Wenxuan Wang, Yuxuan Wan +2
ChatGPT is a cutting-edge artificial intelligence language model developed by OpenAI, which has attracted a lot of attention due to its surprisingly strong ability in answering fol…