DoRA-RBAC experiments on LLaMA-3.1-8B and Mistral-7B across QA benchmarks show geometry-aware merging offers no advantage over Euclidean averaging, indicating adapter interference stems from nonlinear representation interactions rather than parameter-space geometry.
AdapterSwap : Continuous training of LLMs with data removal and access-control guarantees
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PermDoRA -- Understanding Adapter Interference in Language Models: Limits of Parameter-Space Geometry
DoRA-RBAC experiments on LLaMA-3.1-8B and Mistral-7B across QA benchmarks show geometry-aware merging offers no advantage over Euclidean averaging, indicating adapter interference stems from nonlinear representation interactions rather than parameter-space geometry.