6 citations · 8 across the 7 of their papers we have counts for
8 papers
Occamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work
Wenhui Chen, Shiwen Cheng, Hao Dong +39
Co-work agents execute complex workflows that combine information gathering, tool use, coding, and file manipulation across many model invocations. Because cost and latency accumul…
Beyond Execution: Static-Analysis Rewards and Hint-Conditioned Diffusion RL for Code Generation
Shuyin Ouyang, Zhaozhi Qian, Faroq AL-Tam +2
Reinforcement Learning (RL) is an important paradigm for aligning Diffusion Language Models (DLMs) toward functional correctness in code generation. However, these models often enc…
Benchmarking and Evaluating VLMs for Software Architecture Diagram Understanding
Shuyin Ouyang, Jie M. Zhang, Jingzhi Gong +7
Software architecture diagrams are important design artifacts for communicating system structure, behavior, and data organization throughout the software development lifecycle. Alt…
ReasonMap: Towards Fine-Grained Visual Reasoning from Transit Maps
Sicheng Feng, Song Wang, Shuyi Ouyang +5
Multimodal large language models (MLLMs) have demonstrated significant progress in semantic scene understanding and text-image alignment, with reasoning variants enhancing performa…
DSCodeBench: A Realistic Benchmark for Data Science Code Generation
Shuyin Ouyang, Dong Huang, Jingwen Guo +3
We introduce DSCodeBench, a new benchmark designed to evaluate large language models (LLMs) on complicated and realistic data science code generation tasks. DSCodeBench consists of…
SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers
Yanzheng Xiang, Hanqi Yan, Shuyin Ouyang +2
This study evaluates large language models (LLMs) in generating code from algorithm descriptions in recent NLP papers. The task requires two key competencies: (1) algorithm compreh…
Knowledge-Enhanced Program Repair for Data Science Code
Shuyin Ouyang, Jie M. Zhang, Zeyu Sun +1
This paper introduces DSrepair, a knowledge-enhanced program repair method designed to repair the buggy code generated by LLMs in the data science domain. DSrepair uses knowledge g…
Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting
Yihang Zhao, Bohui Zhang, Xi Hu +6
Past ontology requirements engineering (ORE) has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts, e…