Dynamic expert management to prevent modular explosion

Develop algorithms to dynamically merge or compress redundant expert subnetworks within the sparsely activated, expert-gated components of the Tri-Memory architecture to prevent long-term modular explosion during continual learning.

Background

The architecture leverages sparse activation and expert gating to compartmentalize knowledge, improving efficiency and reducing interference. Over extended operation, the number of experts may proliferate, increasing management complexity and memory footprint.

The authors identify an explicit open need for methods that detect redundancy across experts and perform merging or compression to maintain scalability without degrading performance.