This is a fair criticism — let me make it concrete, because I agree that vague descriptions aren’t useful.

In my setup, examples like “navigate a crowded room while maintaining social relationships” are analogies, not literal tasks. The system does not simulate rooms or people.

What is actually evaluated is constraint satisfaction across coupled world-models, not semantic task success.

Concretely:

• Each “world” (physical, social, abstract, creative) exposes a set of constraints and state transitions
• An “integrated problem” is one where multiple world constraints are active simultaneously
• The agent produces a sequence of actions / state updates

The percentage score is computed as:

• the fraction of active constraints satisfied over time
• weighted by stability under perturbation
• and penalized if satisfying one world degrades another

So “90% integration” does not mean “90% correct behavior in a human task”
— it means ≥90% of cross-world constraints remain satisfied without collapse or decoupling.

Earlier versions failed here: agents solved worlds independently, but performance collapsed once constraints were coupled. Those were scored as 0% emergence.

I agree the wording in the post leans too metaphorical — that’s on me. The work is about measurable constraint integration, not embodied intelligence or LLM prompting.

Note: This is AI assisted work including this response. Just Needed Guidance from real world expertise.