[2603.26815] Resolving the Robustness-Precision Trade-off in Financial RAG through Hybrid Document-Routed Retrieval

Computer Science > Computation and Language arXiv:2603.26815 (cs) [Submitted on 26 Mar 2026] Title:Resolving the Robustness-Precision Trade-off in Financial RAG through Hybrid Document-Routed Retrieval Authors:Zhiyuan Cheng, Longying Lai, Yue Liu View a PDF of the paper titled Resolving the Robustness-Precision Trade-off in Financial RAG through Hybrid Document-Routed Retrieval, by Zhiyuan Cheng and 1 other authors View PDF Abstract:Retrieval-Augmented Generation (RAG) systems for financial document question answering typically follow a chunk-based paradigm: documents are split into fragments, embedded into vector space, and retrieved via similarity search. While effective in general settings, this approach suffers from cross-document chunk confusion in structurally homogeneous corpora such as regulatory filings. Semantic File Routing (SFR), which uses LLM structured output to route queries to whole documents, reduces catastrophic failures but sacrifices the precision of targeted chunk retrieval. We identify this robustness-precision trade-off through controlled evaluation on the FinDER benchmark (1,500 queries across five groups): SFR achieves higher average scores (6.45 vs. 6.02) and fewer failures (10.3% vs. 22.5%), while chunk-based retrieval (CBR) yields more perfect answers (13.8% vs. 8.5%). To resolve this trade-off, we propose Hybrid Document-Routed Retrieval (HDRR), a two-stage architecture that uses SFR as a document filter followed by chunk-based retrieval scope...