Equifinality in Mixture of Experts: Routing Topology Does Not Determine Language Modeling Quality

Sparse Mixture-of-Experts (MoE) architectures employ increasingly sophisticated routing mechanisms – learned routers, multi-hop trajectories, token-dependent gating. We ask: does routing topology actually determine language modeling quality? We build a geometric MoE (ST-MoE) using cosine-similarity routing against learned centroids in a low-dimensional space ($d_{space}=64$), requiring 80% fewer routing parameters than standard linear routers. Through 62 controlled experiments on WikiText-103 at 76–84M parameters trained to convergence (50K steps, 1.64B tokens), we find that routing topology does not determine asymptotic perplexity (PPL): five cosine-routing variants are statistically equivalent within a 1-PPL margin (Two One-Sided Tests [TOST], $p<0.05$ for all 10 pairwise comparisons; 15 runs across 3 seeds, observed range 33.93–34.72). The finding extends to hash, random-fixed, and top-1 routing (single-seed; graceful 1.1–2.2 PPL degradation) and replicates on OpenWebText (0.03 PPL gap, 6 runs, 3 seeds each). A standard linear router with 5.3$\times$ more routing parameters reaches PPL 32.76, but iso-parameter cosine routing closes 67% of this gap – the true mechanism advantage is $\sim$1.2%. The mechanistic explanation is convergent redundancy: multi-hop updates are collinear ($\cos (\mathrm{\Delta }{h}_0,\mathrm{\Delta }{h}_1)=0.805$), implementing magnitude amplification rather than compositional reasoning; a single learnable scalar replicates multi-hop performance. As a practical payoff, zero-shot relative-norm halting saves 25% of MoE FLOPs at +0.12% PPL. Expert-level specialization and causal controllability – which coexist with topology-level equifinality – are explored in a companion paper.