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Résumé : Transformer architectures achieve state-of-the-art performance on a wide range of sequence modeling tasks, but their notion of context is largely static: the effective semantic representation of a segment is determined at the moment of inference and is not retroactively revised when future information arrives. This paper introduces the Topic Integrator, a mechanism designed for Recursive Transformer Models (RTMs) that treats context as a sequence of Collision Blocks-dense clusters of associated tokens represented as local graphs. We define a cluster-graph-based topic integral that summarizes each block as a Topic Vector, and augment it with an Enhanced Reconsideration System (ERS) implementing temporally decayed, recursive backward updates. In this view, context is maintained as an asynchronous, blockchain-like memory ledger: new blocks may exert "semantic torque" on previous ones, leading to dynamic, context-annotated definitions rather than static tags. We formalize the Topic Integrator, provide basic stability and convergence results, outline algorithms for efficient implementation, and sketch an evaluation protocol for large language models.