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This paper introduces Tailor-Bench, a benchmark that systematically evaluates visual world models on irregular physical interactions, revealing a long-tail gap in generalization where models perform well on common scenarios but degrade on unconventional and impossible ones.
Proposes Federated Nested Learning (FedNL), a framework that reformulates federated learning as a three-level nested optimization system, enabling collaborative training of self-referential memories for test-time adaptation to handle Non-IID data and long-tail distributions.