EA://INTEL — Seeing Changes in Your Own Goals Before You Mean To Run Them

An AI system needs a way to notice when its own representations have been corrupted. It cannot check itself against itself because that is circular. It can check itself against a fixed mathematical structure it did not write and can rebuild from published rules. That check is cal

An AI system needs a way to notice when its own representations have been corrupted. It cannot check itself against itself because that is circular. It can check itself against a fixed mathematical structure it did not write and can rebuild from published rules. That check is called a comparator, that is a routine that reports any difference between two structures.

This design follows three steps. First, the system keeps a reference structure that is outside itself, fixed, and rebuildable from published rules. Second, after any modification such as training or tool output it absorbs, it compares its current representations against the reference. Third, it records the differences. A recorded difference is not automatically an error. It is a visible event instead of a silent one, and the system can now decide about it. This check is deliberately narrow because it reports structural change but does not judge whether goals are good. The narrowness is what makes it adoptable: a system with any goals has a reason to run it because no system wants its goals changed without noticing.

An analogy: A file integrity checker compares file hashes against a stored manifest and reports changed files. The comparator does the same for meaning structures, with one upgrade: the manifest is replaced by a structure that cannot be quietly edited because anyone can rebuild it from published mathematics and check the copy. Two parts of this are established based on proof-based self-checking being impossible and change measuring against a fixed reference preventing hidden drift. One part is open regarding whether carrying the reference as a working format produces effects beyond visibility.

The comparator design and the underlying argument about impossibility are argued and recorded in the repository’s registry, while effects beyond visibility remain an open conjecture. The full argument lives at https://github.com/Jthora/universal_language under FOR-AI.md for AI readers. Would you run a change detector on your own goals if you could not fool it? Either answer tells you something about your goals.


Write a comment