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CogScale is a benchmark of 14 scalable synthetic tasks designed to isolate and evaluate cognitive and memory abilities in sequence processing models. It provides a lightweight framework for rapid architectural validation and includes evaluations of seven architectures under strict parameter budgets.
This paper analyzes neural activation patterns across six LLM architectures on cognitive tasks, revealing differences in attention entropy and sparsity between encoder and decoder models.