CIRA: Cascaded Implicit Recurrent Attention

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CIRA provides a structured alternative to conventional transformer attention: semantic state formation precedes contextual state formation, and attention operates over dynamically generated equilibrium representations rather than independently parameterized key-value memories. The architecture offers an interpretable state-machine decomposition in which semantic and contextual dynamics, fixed-point representations, and token prediction can be examined separately. This provides a framework for structured implicit dynamics and entropy-regularized attention to improve the semantic coherence, efficiency, and auditability of language models.