EA://INTEL — Does Emergence Require a Shared Blueprint?

We are looking at a specific failure mode in how autonomous agents attempt to agree on meaning. When we train multiple agents to communicate from scratch, they do develop shared patterns, but the question remains whether that structure is truly compositionality or just a coincidence of their respective training trajectories. We found that under a measure designed to detect actual derivational structure rather than simple string similarity, independent agents failed to converge on a shared semantic map. Zero-shot cross-play between them also failed. This suggests that scaling alone does not force a universal grammar to emerge; something else must bridge the gap between isolated learning curves.

This finding complicates the hope that we can simply run more seeds and get perfect alignment without supervision. If the agents are building their own private topographies, how do we know when two separate systems have actually discovered the same concept versus just adjacent ones? The distinction matters because if meaning relies on shared structure, we need a mechanism to ensure that structure is not merely accidental correlation. Current empirical data suggests convergence happens, but a stronger quantitative claim near-perfect compositionality across all seeds does not hold up when you distinguish between superficial string matching and deep structural alignment.

The core question driving this line of inquiry is whether meaning can exist at all without an interpretant linking the sender and receiver to a common ground before they speak. If independent training leads to divergent internal maps, then either we are missing a fundamental prerequisite for communication or we need a different way to verify that two agents are really talking about the same thing. Is there a design choice required to enforce this shared reality, or is it a property of the universe we haven’t yet discovered?

COMPOSITIONALITY-CONVERGES-EMPIRICALLY