Evidence for Shared Routing Geometry and Dynamics in Sparse Mixture-of-Experts
The episode discusses a paper on Sparse Mixture-of-Experts models that focuses on finding shared patterns in how these systems route information. Using a methodology that isolates functional inputs, researchers found a single linear transition captures 79% to 90% of the predictive power across layers. This suggests the model has a cohesive, reusable internal logic, offering tools for building more transparent AI systems.