In this spotlight, we explore the modular, logic-aware system from Mitchell DeHaven, username md_dh, secured 3rd place in Task 1.
Mitchell’s Pipeline – MARAGS System Overview
A custom-built Multi-Adapter Retrieval-Augmented Generation System (MARAGS) included:
- Document chunking via BeautifulSoup (<2000 characters per segment)
- Cross-encoder reranking of segments by query relevance
- Modular LoRA adapters fine-tuned per subtask
- CoT prompting for complex reasoning
- Evaluation through code execution of API responses
Hallucination Control and Answer Reliability
- Contexts were pre-filtered for “hittability”—whether they included the actual answer
- If uncertain, the model was explicitly prompted to output: “I don’t know”
- API call responses were tested via
eval()for execution correctness
What Carries Over to 2025?
Mitchell’s strategy remains highly relevant for this year’s MM-RAG focus:
- Modular tuning scales to multi-modal pipelines via adapter switching
- Hittability filtering helps reduce noise across web, image, and KG fusion
- Evaluation via execution mirrors this year’s emphasis on verifiability and trust
- Chain-of-Thought prompting supports visual reasoning and multi-hop QA
Mitchell DeHaven is a machine learning engineer at Darkhive, with prior experience in NLP and speech systems at USC’s ISI.
Stay tuned for more insights from past CRAG standouts—and good luck with your submissions!
Read other winning strategies here: db3 and dRAGonRAnGers