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This paper introduces the Entropic Bound, a spectral measure of task-intrinsic capacity for transformers, proving that the intrinsic rank of the token-mixing operator provides a tight lower bound on required model capacity. It shows that while a naive transfer from linear attention fails for real attention, an attention-native intrinsic rank restores the full theoretical structure.
This paper models a question-answering forum staffed by expert knowledge workers, studying optimal scheduling to maximize system capacity and stability.