How Modular Is a Frontier Mixture-of-Experts? A Pre-registered Causal Test in Which Apparent Expert Modularity Mostly Dissolves
arXiv:2606.25092v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models route each token to a few of many experts, inviting the hypothesis that experts form functional modules tied to capabilities or languages. We test this causally on Command A+, a frontier open-weights MoE (218B total / 25B active; 128 experts, 8 active, +1 shared). We build a routing-mass atlas, pre-register six family-to-axis hypotheses before any intervention, and ablate each family at inference time against a size-matched random-expert null, measuring whether it selectively breaks its own axis (worst off-t
The proliferation of Mixture-of-Experts (MoE) models as a frontier architecture necessitates deeper analytical understanding of their internal mechanisms.
This research critically challenges the prevailing assumption of modularity in MoE models, suggesting that their expert components may not be as functionally specialized as hypothesized, impacting future AI model design and interpretability.
The understanding of MoE models shifts from a naive modularity perspective to one requiring more nuanced interpretations of expert roles, potentially guiding new architectural innovations or diagnostic approaches.
- · AI researchers focusing on interpretability and model architecture
- · Developers of custom MoE architectures
- · Organisations investing in AI safety and alignment
- · Naively designed MoE models assuming clear functional modularity
- · Interpretability frameworks based solely on expert activation
- · Efforts to easily disentangle model capabilities via expert identification
The findings will prompt reassessments of expert definitions and functions within large MoE models.
New architectural paradigms or fine-tuning methods might emerge to achieve genuine modularity or leverage the observed non-modularity.
This could influence the training costs and efficiency of future frontier models if architectural assumptions are fundamentally revised.
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Read at arXiv cs.LG