Mechanisms determining expert feature combinations

Determine why Mixture-of-Experts experts converge to particular combinations of features rather than other combinations, and whether load-balancing losses or model-capacity constraints force redundancy across experts.

Background

The experiments establish that the studied experts are highly polysemantic, with routing associated with disjoint collections of micro-domains. The paper does not explain why training produces these particular feature combinations.

The conclusion identifies the effects of load-balancing objectives and model-capacity constraints as unresolved potential causes of redundancy across experts. Understanding these mechanisms could reveal principles governing the self-organisation of modular neural architectures.

References

Several directions remain open for future investigation. First, the mechanisms driving expert specialisation deserve deeper study: why do experts converge to their particular feature combinations rather than others, and do load-balancing losses or model capacity constraints force redundancy across experts?

— RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing  (2610.11775 - Lasy et al., 8 Oct 2026) in Section Conclusion

Second, tracking how routing evolves across training checkpoints could reveal when and how expert specialisation emerges and how early training shapes routing.

— RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing  (2610.11775 - Lasy et al., 8 Oct 2026) in Section Conclusion