Arya Forge: Auditing Validation and Release Approval for Financial Instruction Data
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
arXiv:2610.27672v1 Announce Type: new Abstract: Rerunning every governance control after every change to an artificial intelligence (AI) pipeline is expensive, so teams reuse earlier results. Reuse i...
arXiv:2608. 09025v1 Announce Type: new Abstract: Financial agents can turn correct context into an unauthorized effect: a customer-facing commitment, trade, or deployed policy.
arXiv:2607. 09712v1 Announce Type: new Abstract: Financial control testing increasingly depends on representative enterprise resource planning (ERP) data in quality environments, yet direct production copies expose personal, supplier, banking, and commercially sensitive records.
The paper introduces ClaimReceipt, a specification and verifier that checks whether a claim in an agent evaluation can be recomputed from retained evidence (sufficiency) and whether the evidence covers the entire experiment set (coverage). Using the CR‑2 verifier on 1,392 historical records, the authors demonstrate accurate reproduction of audit verdicts, non‑redundant field groups, and zero false positives on semantic faults. In a prospective CR‑3 run, the system correctly flags missing receipts and preserves coverage when private evidence is withheld, while adding minimal overhead to inference time and transaction size.
The paper introduces PACE (Policy‑Attested Contract Execution), a framework that sits between large‑language‑model (LLM) based autonomous AI agents and on‑chain DeFi operations. PACE defines typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind an approved intent, policy, and simulation report to the exact on‑chain execution bytes, providing replay and expiration protection. In evaluations across 40 tasks and six baselines, PACE achieves zero unsafe executions and zero false positives, outperforming unguarded agents by a large margin.
The paper introduces SecTB-RTL, an auditable framework for evaluating AI-generated RTL verification plans against 31 tasks and 124 hardware‑security regressions. In a confirmatory run, the AI model’s responses were rejected by the provider’s schema, and after a schema‑only repair, only nine of 1,857 accepted responses passed the production semantic validator, revealing a mismatch between generation and execution rules. The study demonstrates that schema acceptance does not guarantee execution validity and provides a benchmark, failure‑preserving contract, incident provenance, and governance controls to prevent misreporting of infrastructure behavior as model behavior.