Prediction markets are like a large-scale digital competition, and the "result determination" is the whistle that decides the outcome. But in reality, things are often awkward: the event has ended, yet everyone is still arguing over who the true winner is, and the whistle has yet to blow.

This "result dispute" has long been a chronic issue in prediction markets. Moving into 2025, everything from geopolitical events to technological advancements can be bet on. However, when faced with complex situations—such as interrupted matches due to weather but with some valid results, or election vote counts leading but legal cases still ongoing—traditional oracles start to fail, either crashing systems or suffering from overly lengthy governance processes.

Why is this deadlock so hard to break? Because the core issue is not about data collection. Anyone can gather data; the real challenge is "understanding the true meaning of the facts."

From another perspective, if traditional decentralized oracles are like a thermometer that only reads numbers, then the next-generation solutions should be like a judge equipped with a think tank—able to see the data and also understand the context. The key is to establish a multi-dimensional "consensus verification system."

What exactly does this involve? The first step is layered filtering at the technical level. No longer relying on a single data source, but introducing a network of specialized verification nodes. These nodes are responsible not only for uploading data but also for deeply interpreting the context and boundary conditions behind the data. In this way, decisions in complex scenarios are backed by sufficient reasoning, naturally reducing the scope of disputes.