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FinSAgent framework enhances SEC filing question answering with corpus-aligned retrieval

By PulseAugur Editorial · [3 sources] ·

Researchers have introduced FinSAgent, a novel multi-agent framework designed to improve question answering over SEC filings. This system addresses prior-corpus misalignment by conditioning retrieval on the specific structure and terminology of financial documents. FinSAgent utilizes role-specialized agents, database-aware query decomposition, and a learned reranker to enhance retrieval coverage and answer correctness, outperforming existing baselines in both offline benchmarks and online user ratings. AI

IMPACT This framework could improve the accuracy and efficiency of financial data analysis and compliance.

RANK_REASON The cluster describes a novel framework presented in an academic paper on arXiv.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

FinSAgent framework enhances SEC filing question answering with corpus-aligned retrieval

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COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Jijun Chi (University of Toronto), Zhenghan Tai (SimpleWay.AI, University of Toronto), Hanwei Wu (SimpleWay.AI, McMaster University), Tung Sum Thomas Kwok (SimpleWay.AI, University of California, Los Angeles), Hailin He (SimpleWay.AI), Zixing Liao (Simpl… ·

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

    arXiv:2607.18102v1 Announce Type: cross 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 re…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xinyu Wang ·

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

    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 typicall…

  3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xinyu Wang ·

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

    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 typicall…

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