FilingLoop improves review of corporate documents with evolving AI agents
FilingLoop: Evaluating and Evolving LLM Agents for Corporate Disclosure Review and Revision
Artificial Intelligence
Summary
Corporate documents filed with the U.S. Securities and Exchange Commission often need careful review and revision. To help with this, the authors developed FilingLoop, a system that uses AI agents to check filings for missing information or errors and suggest fixes. The system learns from past reviews without changing its underlying AI model, gradually improving how well it meets regulatory requirements. The authors showed that over time, FilingLoop was able to resolve more review comments and fulfill more document requirements.
What this means in practice
- •For financial compliance teams: Automate checking and updating corporate filings to meet SEC review comments more effectively through iterative AI-guided revisions.
- •For legal document reviewers: Improve review workflows for complex legal disclosures by applying evolving AI agents that learn from prior editing feedback across cases.
Authors
Wentao Zhang, Yilei Zhao, Guangyan Gan, Shuo Sun, Lin William Cong
Abstract
Public disclosure repositories such as SEC EDGAR support informed investment through web access to corporate filings. Supporting their review with language model agents requires discovering unstated deficiencies across related documents, acquiring missing company facts, and verifying that requested revisions satisfy evidence-backed requirements. Existing financial question-answering evaluations provide limited evidence of these interactive capabilities. We introduce \textbf{FilingLoop}, an interactive benchmark and a self-evolving agent framework for corporate disclosure review and revision. Grounded in historical U.S.\ Securities and Exchange Commission correspondence, FilingLoop reconstructs 380 predominantly IPO review tasks from 96 companies under distinct information constraints. An evaluator separate from the acting agents scores final filings and responses against 6,099 eligible requirements and audits unsupported claims and harmful revisions. Our framework couples iterative review and revision within each case with procedural learning across cases, converting requirement-level evaluation feedback into role-specific guidance without updating model weights. Updates affect only subsequent cases, allowing procedural adaptation to be assessed without retrying scored tasks. On the evaluated stream, evolution increases requirement fulfillment from 24.28\% to 27.96\% and full comment resolution from 16.90\% to 20.75\%, with a 5.8\% increase in total cost over the fixed-policy variant. FilingLoop connects the concerns an agent discovers with the company facts it obtains and the revisions it commits, providing requirement-level feedback for procedures used in later review tasks.