Has anyone tested whether this spreading-activation effect varies across model architectures (for example... dense vs. mixture-of-experts)? Since MoE models route different tokens through different expert subnetworks, I'd guess the.. 'write a fact, lower the retrieval threshold for the next' chain could behave differently than in a dense model where everything passes through the same weights every step.