From simulated empathy to structural attunement: Realtime Editable Memory Topology and the evolution of emotionally grounded AI

Large language models (LLMs) and retrieval-augmented generation (RAG) systems have achieved remarkable linguistic fluency, and many now implement persistent cross-session memory at the application layer. However, these mechanisms typically rely on external storage and reinjection of stored content rather than structural reorganization of memory relationships. As a result, they remain limited in their ability to integrate affective salience into a dynamically evolving internal memory topology capable of supporting coherent long-term behavior. To address this gap, we introduce Realtime Editable Memory Topology (REMT), an architectural framework for imbuing conversational agents with persistent autobiographical memory organized as an evolving graph of emotionally valenced nodes. REMT formalizes synthetic neuroplasticity through explicit update rules governing edge reinforcement, decay, and pruning, and introduces a bounded Mood Index that modulates retrieval bias and response generation as a function of accumulated affective experience. In this Perspective, we argue that memory-grounded architectures integrating insights from cognitive science, affective computing, and memory-augmented neural systems are necessary for building adaptive conversational agents with stable long-term interactional tendencies. We conclude by outlining a roadmap for empirical validation using an internally developed evaluation framework, with results to be reported in a future Original Research article.