The Problem With Current Routing
Most AI systems today route at the wrong layer. Multi-agent pipelines route at the text level — slow, brittle, expensive. MoE architectures route at the token level — better, but still late.
There is a more fundamental level: the latent space.
What Is Latent Routing?
Latent Routing makes inference-time decisions based on the geometric position of an input’s representation in a model’s latent space.
Given input x and encoder E, the routing decision r is:
r = Router(E(x))
The Router operates on the latent vector, not raw tokens.
Three Forms
Expert Routing — extends MoE from per-token to per-concept routing.
Agent Routing — selects specialist agents based on latent-space proximity to their competence regions.
Compute Routing — exits inference early when the latent representation lands near a confident region. Same quality, less compute.
Why Now
Three trends converging: models are becoming modular, inference cost is the bottleneck, and latent space is becoming legible via mechanistic interpretability.
Latent Routing is the primitive that ties them together.
Follow @latentroute for updates.