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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.
CALM-MAS is proposed, a congestion-aware serving framework for LLM applications that treats LLM test-time computation as an elastic resource, dynamically adjusting the compute profile of admitted tasks to tame congestion.
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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