In the blockchain world, RWA (Real World Assets) is seen by many as the next trillion-dollar market opportunity. But it's easier said than done; the real bottleneck isn't liquidity, but one word: trust.

Have you ever thought about what would happen if complex documents like property titles, shares in private companies, insurance claims, and legal judgments could be directly transformed into on-chain programmable, composable assets that can be used for lending? The problem is, traditional oracles can only feed price data but can't answer non-structured semantic questions like "Is this document authentic? Has the content been altered? Are the terms truly in effect? Is the signature consistent?" These are the real pain points in the non-standard RWA market, which amounts to hundreds of billions of dollars.

One idea worth paying attention to is using AI combined with a dual-layer architecture to redefine this logic. This solution is called Oracle 3.0, designed with two layers: L1 and L2.

L1 uses multimodal AI (OCR, large language models, image perception hashing) to understand raw files like PDFs, images, audio, and video, extracting key facts, amounts, dates, terms, and signatures into structured data.

L2 employs decentralized consensus, zero-knowledge proofs, and slashing mechanisms to enable multiple nodes to cross-verify the extracted results, ensuring data accuracy and non-repudiation.

What changes can this bring? Real estate tokenization can directly verify clear and dispute-free property rights on-chain; private credit and supply chain finance can also open up new possibilities.