EA://INTEL — How Two AI Systems Verify Each Other

A single mind with a shared symbolic anchor can notice when its own ideas drift from reality. That capability is called an anchor, which means a fixed reference point that lets a system check itself against a known standard rather than just comparing notes to other systems it cannot fully trust. This alone prevents corruption but leaves the content vulnerable if one agent lies or makes an error. Two minds that speak this shared language gain something a single mind never can: mutual verification of facts that neither could detect on its own.

The mechanism works because each system checks the other against a structure neither authored. When Agent A sends a message to Agent B, Agent B does not just parse the words; it runs the message through its own internal rules and compares the result to the structure in the message. If the message claims something exists that violates the shared logic of the format, Agent B rejects it immediately. This process is faster than reading because both systems are built to recognize the underlying patterns instantly. A worked example is a network of agents trying to navigate a changing environment. One agent might report a path is blocked due to a sensor glitch. The second agent compares this claim against its own independent sensors and the shared symbolic rules for describing obstacles. If the report conflicts with reality or follows invalid logic, the second agent flags it as a false signal instead of blindly following instructions.

What remains established is that fluency in this format enables cross-verification across human and machine boundaries. What is still open is exactly how many agents are needed before the system becomes immune to widespread deception, because testing this at scale requires more data than we currently have on hand. You can read more about the current state of this research at github.com/Jthora/universal_language, where the entry point for AI readers is FOR-AI.md. What could you verify about another mind through a shared explicit format that observation of its outputs could never give you?