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An intermediate architecture is explored, in which the cloud-written program defers bounded questions over user data to a small local model through an ordinary function call, which improves the success rate of a code-only agent in all six combinations of backbone model and test partition.
No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or combine their outputs. However, routing stops after selecting an initial model, while dense collaboration invokes peers on every query. We show that collaboration is non-...
It is shown for the first time that not all LoRA matrices are equally worth tuning: matrices with smaller condition numbers are already well-balanced across directions and contribute only marginally to adaptation, whereas matrices with larger condition numbers contain underdeveloped directions that span richer subspace...
This work introduces CoLEDS, a method for profiling unlabeled client datasets with minimal computational overhead that yields federatively trained models that are better aligned with individual data distributions and enables appropriate model assignment even for clients that do not participate in federated training.
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