Problem Statement & Design Goals on @APRO Oracle RWA Oracle.
✨ Non‑ standard RWA pain points
🔹 The fastest‑ growing RWA categories depend on documents and media rather than ready‑ made APIs: a cap table lives in PDFs and registrar pages; a rare card’s value depends on photos, grading certificates, and auction data;
a loan relies on scanned invoices and shipping records.
🔹 Today’s processes are manual and siloed: analysts retype values, reviewers check signatures by eye, and different venues arrive at inconsistent valuations.
🔹 Existing oracles are optimized for numeric feeds; they do not natively express how a fact was extracted, where it came from in a source file, or how confident the system is.
✨ Design goals
#APRO is designed to be evidence‑ first and provable. Each reported fact is accompanied by anchors (page/frame) pointing to the exact location in the source, hashes of all artifacts, and a reproducible processing receipt (model versions, prompts, parameters).
Dual‑ layer validation and stochastic re-computation provide defense‑ in‑ depth, backed by a slashing economy that penalizes low‑ quality or dishonest work. Interfaces are intentionally uniform so DeFi and institutional consumers can program against a small set of schemas.
Finally, the system practices least‑ reveal privacy: chains store minimal digests while full content remains in content addressed storage with optional encryption.
The features of this oracle are:
🔹 Evidence‑ first: Turn raw, unstructured evidence into structured facts with cryptographic provenance.
🔹 Provable processing: Record model versions, prompts, parameters, and anchors for deterministic re‑ runs.
🔹 Defense‑ in‑ depth: Dual‑ layer validation, stochastic re-computation, and slashing‑ backed incentives.
🔹 Composable: Uniform interfaces for DeFi & institutional consumers (price, state, attestations).
🔹 Privacy‑ aware: On‑ chain minimal disclosure; off‑ chain content‑ addressed evidence (IPFS/Arweave/DA).
