Research Summary: Arya Forge: Auditing Validation and Release Approval for Financial Instruction Data

Arya Forge establishes a data governance framework for financial instruction pipelines that separates content validity from release authorization using typed records and manifest-bound approval coverage. Evaluated on 100,000 synthetic records, the system achieved complete agreement on 15,000 numeric calculations and successfully eliminated all injected numeric faults and policy holds across 240 testing scenarios.

Why it matters

This research addresses the operational vulnerability of conflating mathematical correctness with release authorization in financial data operations. Providing auditable release boundaries ensures that synthetic or processed instruction datasets cannot be published without meeting both compliance criteria and computational validity. Implementing such controls mitigates regulatory exposure and operational risk in high-stakes financial data systems.

Key insights

• Arya Forge decouples content validation from formal dataset release approval within financial data pipelines.
• The framework integrates typed records, current-content verification, and approval tracking bound directly to a dataset manifest.
• An integrity audit of 100,000 synthetic instruction records demonstrated complete arithmetic consistency across 15,000 numeric records covering twelve formula families.
• In 240 scenarios comprising 2,400 treatment rows, the filtering checks rejected all injected numeric errors and policy holds while retaining 220 of 240 clean controls.
• Twenty clean controls were excluded during filtering, though full details on the underlying cause are truncated in the source text.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2610.34778