2 citations · 3 across the 7 of their papers we have counts for
8 papers
Recursive self-improvement of AI research agents
Dhruv Srikanth, Bingchen Zhao, Dixing Xu +2
AI agents are beginning to automate research and development across the AI stack, from improving training efficiency to optimizing inference. A natural next step is to improve the…
AutoData: Agentic Search for Pre-training Data Selection
Yan Meng, Dhruv Srikanth, Bingchen Zhao +2
LLM agents have recently shown promise in automating machine learning engineering by editing model and training code under execution feedback. Data, however, remains largely outsid…
FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics
Qiran Zou, Hou Hei Lam, Wenhao Zhao +11
AI research agents accelerate ML research by automating hypothesis generation, experimentation, and empirical refinement. Existing agent strategies range from greedy hill-climbing…
SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents
Bingchen Zhao, Dhruv Srikanth, Yuxiang Wu +1
As long-horizon coding agents produce more code than any developer can review, oversight collapses onto a single surface: the automated test suite. Reward hacking naturally arises…
The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)
Kirill Vasilevski, Benjamin Rombaut, Gopi Krishnan Rajbahadur +10
Foundation Models (FMs) such as Large Language Models (LLMs) are reshaping the software industry by enabling FMware, systems that integrate these FMs as core components. In this KD…
Predicting post-release defects with knowledge units (KUs) of programming languages: an empirical study
Md Ahasanuzzaman, Gustavo A. Oliva, Ahmed E. Hassan +2
Defect prediction plays a crucial role in software engineering, enabling developers to identify defect-prone code and improve software quality. While extensive research has focused…
Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware
Ahmed E. Hassan, Dayi Lin, Gopi Krishnan Rajbahadur +9
Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized software development by enabling new use cases and business models. We refer to software built us…
Bug Characterization in Machine Learning-based Systems
Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon +3
Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML components, i.e., a software component…