Localized LoRA-MoE introduces block-wise low-rank experts with adaptive routing

The authors introduce Localized LoRA-MoE, a unified framework that fuses localized spatial blocking with dynamic, context-conditioned routing to address the limitations of standard parameter-efficient fine-tuning methods.

• Block-Wise LoRA-MoE modulates the entire structural grid via a monolithic context signal.
• Cell-Wise LoRA-MoE empowers every coordinate cell in the matrix grid with autonomous, localized expert gating.
• Both architectures resolve optimization deadlocks inherent in static baselines across high-dimensional SVD simulations and spatial vision perception tasks.
• Decentralized cell-level gating achieves complete statistical parity with an omniscient global coordinator.

The framework provides a robust "gradient firewall" that protects surviving pathways from fault-propagated corruption, offering a scalable solution for dynamic model adaptation.