I have a novel question about small worlds neural network connectivity

Current approaches to combining specialized AI capabilities tend toward two extremes: fully dense integration (one large model handling everything, expensive and undifferentiated) or loosely coordinated multi-agent systems (separate specialist models — coding, math, language — stitched together by a router/coordinator that reconciles finished outputs after the fact).

What if we structure the connectivity between specialized agent clusters using small-world network principles — densely interconnected within each specialist cluster (tight coordination among agents doing similar work), but only sparse, deliberately placed long-range links between different specialist clusters (math ↔ language, etc.), rather than full mesh connectivity or a single after-the-fact coordinator.

This is distinct from existing small-world neural net research I could find, which applies the topology within a single network’s layers (e.g., SWNet, clustered small-world sub-networks in neuroscience-inspired nets). It’s also distinct from standard Mixture-of-Experts, where routing between experts is mostly emergent from training rather than a deliberately engineered small-world structure, and from typical multi-agent orchestration, where specialists don’t have direct sparse connections to each other at all — everything routes through a central coordinator.

Question for anyone with relevant background: has this specific combination (small-world wiring applied between full specialized agent/model systems, not within a single network) been tried or studied? Worth a real experiment, or is there a known reason it wouldn’t work at scale?