Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
2026-07-06 • Machine Learning
Machine LearningArtificial IntelligenceComputation and Language
AI summaryⓘ
The authors focus on improving how language and perception models adapt to different tasks without retraining everything. They introduce Localized LoRA-MoE, a new system that splits model adjustments into smaller parts with smart routing that changes depending on the context. This approach avoids problems from older methods where adjustments could interfere destructively, especially when tasks switch or sensors fail. Their tests show that their method better protects important model pathways and adapts more flexibly than previous static designs.
Large Language ModelsParameter-Efficient Fine-Tuning (PEFT)LoRAMixture of Experts (MoE)Gradient OptimizationSpatial PartitioningContext-Conditioned RoutingMatrix FactorizationSensor DegradationDynamic Adaptation
Authors
Babak Barazandeh, Subhabrata Majumdar, Vinay Prithyani, George Michailidis
Abstract
Large Language Models (LLMs) and high-dimensional perception networks increasingly rely on parameter-efficient fine-tuning (PEFT) to adapt to diverse operational contexts. However, standard methods like LoRA are structurally limited by a monolithic bottleneck, making them highly susceptible to gradient warfare. Interleaved multi-task streams may trigger destructive optimization feedback, collapsing adapter weights into unspecialized averages. While recent spatial partitioning methods have introduced block-wise isolation, they remain trapped in static topologies, unable to adapt to dynamic task-switching or environmental sensor failure. In this work, we introduce Localized LoRA-MoE, a unified framework that fuses localized spatial blocking with dynamic, context-conditioned routing. We propose and evaluate two novel architectural paradigms: Block-Wise LoRA-MoE (Centralized Macro-Routing), which modulates the entire structural grid via a monolithic context signal, and Cell-Wise LoRA-MoE (Decentralized Micro-Routing), which empowers every coordinate cell in the matrix grid with autonomous, localized expert gating. Through a comprehensive suite of benchmarks, ranging from high-dimensional SVD matrix simulations and real-world tabular transformations to spatial vision perception under sensor degradation, we demonstrate that both architectures resolve optimization deadlocks inherent in static baselines. Our empirical results establish that decentralized cell-level gating achieves complete statistical parity with an omniscient global coordinator, providing a robust "gradient firewall" that protects surviving pathways from fault-propagated corruption. Our proposals consistently outperform static baselines, offering a scalable and parameter-efficient solution for dynamic model adaptation across granular coordinate fields and shifting operational regimes.