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The article outlines a multi-gate architecture for a high-accuracy semantic evidence and RAG system for financial documents, emphasizing traceability, reconciliation, and hybrid retrieval, and seeks feedback on its design.
PixelRAG is an open-source project from UC Berkeley SkyLab and other teams. By capturing web pages and PDFs as screenshots and using visual indexing for retrieval, it improves RAG accuracy and significantly reduces token costs for AI Agents.
MOTHRAG is a multi-hop RAG system that matches the performance of top GPU-dependent systems (HippoRAG 2, CoRAG, NeocorRAG) using only commodity API calls, with no GPU, no fine-tuning, and deployment via pip install plus API keys.
This paper presents Eskwai for Students, a generative AI assistant for legal education in Ghana, using retrieval-augmented generation on a database of over 12K case laws and 1.4K legislation. Deployed in a 30-month study with 3.1K law students, it provides insights into AI usage in legal education in the Global South.
Blue J demonstrates how to scale AI expertise in complex regulated domains by combining GPT-4.1 with retrieval-augmented generation over curated tax documents, achieving <0.14% error rates and 70% weekly user engagement through rigorous feedback loops and domain-specific optimization.