Beyond Routing: Characterising Expert Tuning and Representation in Vision Mixture-of-Experts
arXiv:2605.20610v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models are often interpreted by analysing which categories are routed to which experts. However, routing alone does not reveal what each expert actually encodes. We train sparsely-gated convolutional MoE models with a contrastive objective on natural images and characterise expert specialisation using tools from visual neuroscience. Extending from gating-level to expert-level analyses, we measure per-expert category separability, and per-expert tuning using the most exciting inputs. Extending from category-level to feat
The increasing complexity and adoption of Mixture-of-Experts models necessitate deeper understanding of their internal workings beyond simple routing mechanisms.
A more granular understanding of expert specialization in MoE models can lead to significant improvements in AI efficiency, interpretability, and capability, impacting various AI applications.
The focus shifts from merely observing routing behavior to intrinsically understanding and characterising what individual experts within an MoE model learn and represent.
- · AI researchers
- · Deep learning practitioners
- · Companies developing MoE-based AI systems
- · Developers reliant on black-box AI models
- · Inefficient AI training methods
Improved interpretability and efficiency of large-scale AI models become more attainable.
This foundational understanding could accelerate the development of more robust, specialized, and resource-efficient AI agents and systems.
Advances in understanding expert specialization might enable more precise control and debugging of complex AI, reducing unexpected behaviors and bias, paving the way for wider deployment in critical applications.
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Read at arXiv cs.AI