FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering

arXiv:2607.18102 (cs)

Authors:Jijun Chi (3), Zhenghan Tai (1 and 3), Hanwei Wu (1 and 12), Tung Sum Thomas Kwok (1 and 4), Hailin He (1), Zixing Liao (1), Bohuai Xiao (1), Chaolong Jiang (1), Jianliang Lei (1), Jerry Huang (7 and 9), Peng Lu (7), Muzhi Li (5), Liheng Ma (1 and 2 and 9), Yihong Wu (7), Sicheng Lyu (1 and 2 and 9), Jingrui Tian (2), Yihan Li (8), Yanzhang Ma (1 and 11), Dingtao Hu (2), Yufei Cui (2), Ling Zhou (10), Lei Ding (1 and 6), Xinyu Wang (1 and 2) ((1) SimpleWay.AI, (2) McGill University, (3) University of Toronto, (4) University of California, Los Angeles, (5) The Chinese University of Hong Kong, (6) University of Manitoba, (7) Université de Montréal, (8) Boston University, (9) Mila - Quebec AI Institute, (10) CG Matrix Technology Limited, (11) Lakehead University, (12) McMaster University)

Abstract:Financial question answering over U.S. Securities and Exchange Commission (SEC) filings requires retrieving and synthesizing heterogeneous evidence dispersed across long, standardized, and highly redundant disclosures. Existing retrieval-augmented and multi-agent systems typically derive retrieval queries directly from the user's question and rank candidates by semantic similarity. Together, these choices create prior-corpus misalignment: a mismatch between model priors and the target filings' structure, terminology, and evidence standards. As a result, query generation misses corpus-specific evidence, while semantic reranking favors topically similar but evidentially invalid false-positive chunks. We propose FinSAgent, an evidence-grounded multi-agent framework that reframes SEC filing QA as corpus-aligned retrieval planning and corrects both ends with a single principle: inject corpus-side conditioning wherever model priors would otherwise dominate. FinSAgent combines (1) role-specialized agents anchored to the mandated 10-K item structure, (2) database-aware query decomposition that conditions each agent's sub-queries on a lightweight, summary-level view of the local corpus, and (3) multi-path retrieval with a learned feature-gated reranker that separates evidential validity from semantic similarity. Across five offline financial QA benchmarks, FinSAgent improves retrieval coverage and answer correctness over strong single-agent and multi-agent baselines; in a three-arm randomized online experiment with 1,000 anonymous user ratings, it also receives higher scores than baselines.

Information Retrieval (cs.IR); Computation and Language (cs.CL); Multiagent Systems (cs.MA)

H.3.3, I.2.7, I.2.11

arXiv:2607.18102 [cs.IR]

(or arXiv:2607.18102v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2607.18102 arXiv-issued DOI via DataCite (pending registration)

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