AMD AI League: Precision OCR & Defensive RAG

The AMD AI League Tournament project delivers an end-to-end, high-performance suite engineered specifically for AMD ROCm hardware, addressing all three hackathon challenges with 100% test pass rates and zero reliance on mock implementations. For Mini-Challenge 1 (AMD AI Academy), we completed the foundational learning pathway, establishing reproducible ROCm development practices and documenting cloud acceleration workflows for the Instinct GPU ecosystem. For Mini-Challenge 2 (Precision OCR), our architecture solves the latency-critical image evaluation challenge. Rather than invoking the heavy Python interpreter for every single query, we designed a persistent resident daemon communicating via Unix domain sockets. This design keeps the OCR pipeline permanently warm in GPU VRAM, slashing inference latency to ~0.10s per item against a 30s budget and achieving a flawless 200/200 score. For Mini-Challenge 3 (Defensive RAG), we engineered a resilient, fully offline retrieval-augmented generation engine operating strictly under --network none constraints. Real-world documents frequently present adversarial edge cases: unreadable permissions, encrypted PDFs, corrupted headers, and complex tabular data. Our cascading ingestion pipeline employs defensive multi-engine parsing with automated empty-password bypass and resilient permission-trapping. To guarantee deterministic retrieval without external network dependencies, we implemented a pure-Python Okapi BM25 engine augmented with compound hyphen splitting, subword n-grams, and graph-based 2-hop entity linking across documents. The resident daemon processes queries in ~0.04s, securing a 200/200 score. All components are containerized on the mandated AMD ROCm PyTorch base image (rocm/pytorch:rocm10.0_ubuntu26.04_py3.14_pytorch_release_2.13.0) and verified for strict competition compliance.