FilingLoop Benchmark Boosts LLM Review of SEC Filings
FilingLoop is an interactive benchmark and self‑evolving framework designed to assist large language model agents in reviewing and revising corporate disclosures. The system draws on historic correspondence from the U.S. Securities and Exchange Commission, reconstructing 380 review tasks that span 96 companies, primarily focused on initial public offering filings. Each task presents distinct information constraints, requiring agents to locate undocumented deficiencies, gather missing company facts, and propose revisions that meet documented regulatory requirements.
The benchmark pairs an iterative review loop with procedural learning that carries guidance from one case to the next. After an agent submits a revised filing, an independent evaluator scores the output against 6,099 eligible requirements and flags unsupported claims or harmful changes. Feedback is transformed into role‑specific instructions without altering the underlying model weights; only subsequent cases receive the updated guidance. This design permits measurement of procedural adaptation while avoiding the need to re‑run previously scored tasks.
On the test stream, the self‑evolving approach raised the proportion of satisfied requirements from 24.28 % to 27.96 % and increased full comment resolution from 16.90 % to 20.75 %, at the expense of a 5.8 % rise in total cost compared with a fixed‑policy baseline. (Opinion/Context: The modest gains suggest that procedural feedback can incrementally improve LLM performance on complex regulatory reviews, a development relevant to compliance teams and AI vendors targeting financial services.)
Why it matters
It shows that procedural feedback can modestly boost LLM accuracy in SEC filing reviews without retraining models.
FAQ
What is FilingLoop?
FilingLoop is an interactive benchmark and self‑evolving agent framework for corporate disclosure review and revision.
How many review tasks does FilingLoop reconstruct?
It reconstructs 380 review tasks from 96 companies.
What evaluation criteria are used?
An evaluator scores final filings against 6,099 eligible requirements and audits unsupported claims and harmful revisions.
Does the framework modify model weights during learning?
No, it converts feedback into role‑specific guidance without updating model weights.
Source: arXiv - AI Research Papers. See the original source for full details.
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