"AI Evaluation Lab team on AMD AI League Hackathon"

AI Evaluation Lab is an independent engineering project focused on making AI agents easier to test, verify and improve. For the AMD AI League, I am developing a shared foundation for source-grounded information retrieval and test-verified code repair. The retrieval workflow is designed to extract specific answers from documents or websites, preserve the supporting sources, and return no answer when the available evidence is insufficient. The code-repair workflow will reproduce a failure, propose a focused patch, and check it against relevant tests before reporting the outcome. The planned implementation uses Python, open-source models and AMD ROCm, with Docker containers tailored to each match's requirements. Evaluation will cover answer correctness, citation accuracy, repair success, regression failures, latency and GPU memory use. Separate test cases will help check whether improvements generalize beyond the public examples. This solo entry builds on my work in AI evaluation, benchmark development and OmniEdge RouteIQ from AMD ACT II. The project is currently in development; AMD runtime validation and official challenge scores are pending.