FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering
2026-07-20 • Information Retrieval
Information RetrievalComputation and LanguageMultiagent Systems
AI summaryⓘ
The authors address the challenge of answering financial questions from long, complex SEC filings, which have lots of repeated and scattered information. They find that existing methods struggle because they base searches on the question alone, missing important details and sometimes picking irrelevant text. To fix this, they created FinSAgent, a system that uses specialized agents aligned with the SEC report structure and plans searches based on the actual document content. Their approach improves the ability to find relevant evidence and provides more accurate answers, as shown in both tests and a user study.
SEC filingsfinancial question answeringretrieval-augmented systemsmulti-agent systems10-K report structurequery decompositionsemantic similarityevidential validityrerankinginformation retrieval
Authors
Jijun Chi, Zhenghan Tai, Hanwei Wu, Tung Sum Thomas Kwok, Hailin He, Zixing Liao, Bohuai Xiao, Chaolong Jiang, Jianliang Lei, Jerry Huang, Peng Lu, Muzhi Li, Liheng Ma, Yihong Wu, Sicheng Lyu, Jingrui Tian, Yihan Li, Yanzhang Ma, Dingtao Hu, Yufei Cui, Ling Zhou, Lei Ding, Xinyu Wang
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.