Recently, while studying the inference logs of several AI execution systems, I discovered an interesting phenomenon— the same event can lead to completely reverse conclusions by the model in different contexts. At first, I thought it was the model's fault, but later I realized that the problem was not there at all. The real crux of the matter is that the input information itself cannot be inferred at all.

Imagine an isolated price signal, a transaction record with ambiguous information, an incomplete on-chain event—these things lack structure, have no boundaries, are semantically confused, and have broken causal chains. Are you really going to make an automated system make decisions based on this fragmented information? That would be like asking a doctor to perform surgery based on a blurry X-ray; the outcome is predictable.

This is the point I have always wanted to make but haven't said: **The biggest enemy of future on-chain automation is not the lack of data, but the inability to infer from the data.**

You cannot let the clearing mechanism trigger based on boundaryless information.
You cannot let the governance system rely on semantically ambiguous signals to determine consensus.
You cannot allow the Agent to perform actions when the causal chain is broken.

So the question arises, how to solve it? This is also why I am currently paying attention to the APRO project. Its approach is very clear: it does not provide "answers" on the chain, but rather "materials that can be reasoned."

By looking at its conditional decomposition model, one can understand that the core logic is to break down an event from linear, ambiguous information into multiple structured data fragments. Each fragment meets the following criteria: verifiable, reproducible, interrogable, cross-verifyable, semantically unified, callable by the model, and capable of participating in logical reasoning.

From another perspective, information needs to be designed into a "inferable form" from the moment it enters the chain. This is not an optimization that adds to the existing benefits, but rather the infrastructure of an on-chain automation system. Once this foundation is established, subsequent clearing, governance, and Agent execution can operate stably.