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This paper introduces invalidation contracts as a protocol for LLM agents to manage cached recovery suggestions across episodes, addressing server-side data drift and improving token efficiency by evicting stale entries while maintaining high compliance rates.
This paper introduces NOMADD, a post-hoc method to reduce concept drift in tabular models by fitting base models on labeled periods and extrapolating compressed parameter changes. It achieves competitive performance with Drift-Resilient TabPFN at a fraction of training and inference cost.
This article discusses how AI deployments in businesses often fail not due to model quality but because of the lack of ownership for keeping the model's knowledge current as the world changes, highlighting the challenge of 'silent drift' and the need for ongoing operational maintenance.