
SARA is a unified RAG framework that balances local factual precision with global coverage by combining natural-language spans with compact semantic compression vectors, achieving consistent gains under strict context budgets.
Jul 1, 2026

Structure-aware multimodal RAG for enterprise Q&A, combining layout-aware report parsing, page-level slide representations, and inference-time context assembly. Includes FastRAGEval for efficient answer evaluation.
Jul 1, 2026

Selective and Adaptive Retrieval-augmented Generation with Context Compression. A unified RAG framework that combines fine-grained natural-language spans with compact semantic compression vectors under strict context budgets. Accepted at ACL 2026 main conference.
Mar 15, 2026