Back to Basics: Let Conversational Agents Remember with Just Retrieval and Generation

ArXi:2604.11628v1 Announce Type: new Existing conversational memory systems rely on complex hierarchical summarization or reinforcement learning to manage long-term dialogue history, yet remain vulnerable to context dilution as conversations grow. In this work, we offer a different perspective: the primary bottleneck may lie not in memory architecture, but in the \textit{Signal Sparsity Effect} within the latent knowledge manifold.