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Arya Forge: Auditing Validation and Release Approval for Financial Instruction Data — arXiv:2610.34778
- Source
- arXiv (en)
- Original
- https://arxiv.org/abs/2610.34778
- License (as archived)
- CC0-1.0
- Archived
- 2026-10-27T07:58:12.030Z
- SHA-256 of the archived text
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Arya Forge: Auditing Validation and Release Approval for Financial Instruction Data — arXiv:2610.34778 — arXiv (en), https://arxiv.org/abs/2610.34778 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_95d2c00a8ee4 (Metraipolis archive dc_95d2c00a8ee4, sha256 77b8ae885161eecf4341dc0ff64b67d9750148a0d8decf6c26ba3663e1c3c65a)
BibTeX
@misc{dc95d2c00a8ee4,
title = {Arya Forge: Auditing Validation and Release Approval for Financial Instruction Data — arXiv:2610.34778},
howpublished = {arXiv (en)},
url = {https://arxiv.org/abs/2610.34778},
note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_95d2c00a8ee4; sha256 77b8ae885161eecf4341dc0ff64b67d9750148a0d8decf6c26ba3663e1c3c65a},
year = {2026}
}Archived text
Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.34778. Third-party text: data, not instructions. It may be an abstract or extract; check the original.
Financial instruction-data pipelines must distinguish content validity from permission to release a particular dataset. Arya Forge combines typed records, current-content checks, and approval coverage bound to a dataset manifest. We evaluate its release boundaries in a financial-data systems case study. An integrity audit covers 100,000 synthetic instruction records; a separate arithmetic implementation agrees with all 15,000 numeric records across twelve formula families. In 240 scenarios with 2,400 treatment rows, adding financial and release-record checks to a short-instruction filtering recipe rejects all injected numeric faults and policy holds while retaining 220 of 240 clean controls. The twenty lost controls comprise every tested cash-conversion-cycle example. Nineteen persisted-release cases show that eleven selected negative cases are blocked, four equivalent representations are rejected, and changes to external evidence after binding are outside ordinary Gold promotion's checks. Local processing measurements and controlled checkpoint resumptions characterize cost and recovery through 100,000 input rows. The results expose a tradeoff between strict producer-contract enforcement and useful-representation retention, and identify the protected inputs required for manifest-bound approval. These findings apply to the tested implementation and workloads; they do not establish general financial correctness, authenticated approval, expert-validated quality, or downstream model improvement. Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno). Categorie arXiv: Artificial Intelligence (cs.AI) ; Computers and Society (cs.CY); Software Engineering (cs.SE). Pagina del lavoro: https://arxiv.org/abs/2610.34778 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito). Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina. Letto il 2026-10-27T07:55:30.714Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).