At a Glance
Aligning query planning and evidence selection with the filing database—via role-focused agents, corpus-aware query decomposition, and a feature-gated reranker—finds company-specific disclosures and cuts noisy boilerplate, improving answer correctness without extra search budget.
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Key Findings
Role-specialized agents first view the question from complementary angles (general, numeric, legal, market, company), then generate sub-queries conditioned on a lightweight summary of the filing corpus so retrieved items match how disclosures are actually written. A learned gate reranks candidates by signals tied to evidential validity (not just surface similarity), suppressing recurring boilerplate that would otherwise crowd out true company disclosures. Across five datasets (three public, two proprietary) the system produced the top correctness on every benchmark while using the same evidence budget as baselines. Human validation and sensitivity tests attribute the gains to better corpus alignment rather than added retrieval or extra model deliberation. Role-based agent pattern
Data Highlights
1Tested on 5 datasets: 3 public benchmarks (FinanceBench, FinDER, stratified 100-question SEC subset) and 2 proprietary collections (Lotus, Zeekr).
2Achieved the highest answer correctness on all 5 benchmarks, outperforming single-agent retrieval pipelines and other multi-agent baselines under a matched evidence budget.
3Feature analysis shows the reranker gate relies mainly on retriever and lexical signals (non-semantic cues) to separate valid, company-specific evidence from frequent boilerplate; a memory extension improved multi-turn metrics across five measured dimensions.
What This Means
Engineers building production question-answering over regulatory or highly redundant corpora should care because the approach reduces false positives from generic boilerplate and finds the right company-specific disclosures. Technical leaders evaluating agent-based pipelines can use the corpus-aware decomposition and feature-gated reranker as practical levers to trade compute for reliability rather than more searching or deeper generation. See how Market-Based Coordination Pattern can inform system-wide orchestration in practice Market-Based Coordination Pattern.
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Key Figures
Fig 1: Figure 1. Answer correctness across five financial QA benchmarks. FinSAgent attains the highest correctness on every benchmark, outperforming single-agent RAG pipelines and multi-agent baselines under a matched evidence budget.
Fig 2: Figure 2. Overall framework of FinSAgent . Role-specialized agents first construct complementary analytical views. Database-aware decomposition then conditions each agent’s sub-queries on a lightweight summary-level view of the local filing database (correcting the front-end query gap), and multi-path retrieval with a feature-gated reranker selects reliable evidence (correcting the back-end validity gap), before evidence aggregation and final answer synthesis.
Fig 3: Figure 3. Agent activation and contribution. Every specialized role, including the low-activation Legal role, contributes exclusive ground-truth evidence.
Fig 4: Figure 4. System overhead. (a) Total token usage split into prompt/completion tokens (inner percentages are the prompt-token ratio). (b) Time-to-first-token (TTFT). FinSAgent achieves far lower latency than external multi-agent configurations while keeping token consumption efficient.
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Learn MoreYes, But...
The current evidence largely comes from whole-system ablations and matched-pool comparisons; direct isolated baselines for the two corrective mechanisms are planned but not yet reported. Fine-grained numerical precision still causes the main remaining errors, and temporal/versioning issues (which filing supersedes which) are not fully solved. Because financial outputs are high-stakes, a human expert should remain in the loop for any deployment. Guardrails Pattern
The Details
Getting accurate answers from long, structured SEC filings is less about making a bigger language model and more about matching how the filing corpus is written. Prior model tendencies lead to sub-queries and reranker choices that favor plausible-sounding text or recurring boilerplate instead of the specific disclosure needed for a given company. The key idea is to fix that mismatch at two points: when forming sub-queries and when selecting final evidence.
The system routes each question to role-focused agents that create complementary views (for example, a numeric view for tables and a legal view for risk factors). Each agent’s sub-queries are conditioned on a lightweight summary of the local filing database so retrieval asks for language that actually appears in those filings. Retrieved candidates then pass through a feature-gated reranker that weights non-semantic validity signals (retriever scores, lexical cues) to demote boilerplate that is semantically similar but not evidentially valid for the company. Experiments across five benchmarks (three public, two proprietary) show consistent correctness gains under a matched evidence budget; human validation and sensitivity analyses indicate the improvements come from corpus alignment rather than more searching or longer generation. Remaining work includes isolating each mechanism with separate baselines, improving numeric precision, and adding explicit temporal/version reasoning for superseded disclosures. Orchestrator-Worker Pattern Chain of Thought Pattern
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Credibility Assessment:
ArXiv preprint with mostly low h-index authors and mixed/unknown affiliations (some from SimpleWay.AI and a couple from Montréal/McGill). Lacks top-venue publication or high h-index authors — emerging/lower confidence.