AI

FilingLoop: Evaluating and Evolving LLM Agents for Corporate Disclosure Review and Revision

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Briefings

FilingLoop is an interactive benchmark and self-evolving agent framework designed to evaluate and improve LLM agents for corporate disclosure review and revision. The system reconstructs 380 IPO review tasks from 96 companies using historical SEC correspondence, scoring final filings against 6,099 requirements. Evaluation metrics show requirement fulfillment rose from 24.28% to 27.96%, and full comment resolution improved from 16.90% to 20.75%. Updates to the agent's procedural guidance affect only future cases, enabling assessment of adaptation without retraining model weights.

Key points

• FilingLoop reconstructs 380 IPO review tasks from 96 companies using historical U.S. SEC correspondence.
• Requirement fulfillment increased from 24.28% to 27.96% and full comment resolution improved from 16.90% to 20.75%.

Context

This research addresses the need for interactive capabilities in financial question-answering evaluations by introducing FilingLoop, which couples iterative review and revision within each case with procedural learning across cases.

Limitations

The study relies on a single preprint (arXiv:2610.37905v1) with limited evidence on public consequences. Evaluation metrics are based on a fixed-policy variant, and updates affect only subsequent cases without updating model weights.

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