It’s also that agents and ML reach local maximima unless external feedback is given. So your wiki will reach a state and get stuck there.

Here is an iteresting thing.

> "The LLM model's attention doesn't distinguish between "instructions I'm writing" and "instructions I'm following" -- they're both just tokens in context."

That means all these SOTA models are very capable of updating their own prompts. Update prompt. Copy entire repository in 1ms into /tmp/*. Run again. Evaluate. Update prompt. Copy entire repository ....

That is recursion, like Karpathy's autoresearch, it requires a deterministic termination condition.

Or have the prompt / agent make 5 copies of itself and solve for 5 different situations to ensure the update didn't introduce any regressions.

> reach local maximima unless external feedback is given

The agents can update themselves with human permission. So the external feedback is another agent and selection bias of a human. It is close to the right idea. I, however, am having huge success with the external feedback being the agent itself. The big difference is that a recursive agent can evaluate performance within confidence interval rather than chaos.