Overview
- Cog-RAG introduces a dual-hypergraph design that models global themes across chunks alongside high‑order entity relations to better guide retrieval.
- Its two-stage, top‑down strategy activates theme-level context first, then diffuses to entity details to align generation from broad topics to specifics.
- The Cog-RAG paper reports significant improvements over state-of-the-art RAG baselines in experimental evaluations.
- Separately, GHAR targets healthcare tasks with a dual-agent setup in which a top agent decides whether to retrieve and a lower agent compiles task-relevant evidence once retrieval is triggered.
- GHAR formalizes agent–retriever–generator interaction as an MDP with tailored rewards and reports superior results across three datasets and three tasks.