# Graph-guided document reranking

Retrieval-augmented multihop question answering suffers from noisy context, and CLaRa's compression of retrieved documents into dense latent memory makes the structural relations between documents harder to exploit. GraphCLaRa places a roughly 1.3M-parameter graph reranker before the frozen CLaRa reader: it builds a query-aware document graph, propagates evidence with a graph attention network, and adaptively decides how many documents to pass on. Across four QA benchmarks it raises answer F1 on three, by 6.6 on 2Wiki, 6.4 on MuSiQue and 2.5 on NQ, while using fewer documents, and trails by 1.1 F1 on the retrieval-saturated HotpotQA.

Retrieval-augmented multihop question answering suffers from noisy context, and CLaRa's compression of retrieved documents into dense latent memory makes the structural relations between documents harder to exploit. GraphCLaRa places a roughly 1.3M-parameter graph reranker before the frozen CLaRa reader: it builds a query-aware document graph, propagates evidence with a graph attention network, and adaptively decides how many documents to pass on. Across four QA benchmarks it raises answer F1 on three, by 6.6 on 2Wiki, 6.4 on MuSiQue and 2.5 on NQ, while using fewer documents, and trails by 1.1 F1 on the retrieval-saturated HotpotQA.