Memory Architectures Beyond Attention: Disambiguating Four Memory Concepts and the Long-Context Reasoning Frontier

Abstract

State-space models did not merely "open a door" beyond attention; they walked through it. Mamba, Mamba-2, Samba, Jamba, Granite 4, Hymba, and a growing family of hybrid attention-SSM architectures are now in production language models, demonstrating that linear-time alternatives to attention can match transformers on language modelling at competitive scales. The simultaneous expansion of pure-attention context windows — Gemini 1.5 and successors handling up to 10 million tokens with near-perfect needle-in-haystack recall — has changed the empirical landscape that motivated SSM research in the first place. The popular framing of "memory architectures beyond attention" has not kept up with this. This paper makes three claims. First, the word "memory" in the long-context discussion conflates four distinct concepts — architectural state, context window, external retrieval, and persistent agent memory — each with different scaling properties and different research questions. Second, the empirical picture is more nuanced than either the SSM-replaces-attention or the attention-is-enough framings: SSMs win on very long passive recall and inference efficiency, transformers win on complex reasoning, hybrids win in deployment, and the choice between them is task-dependent. Third, the genuine open frontier is reasoning at long context — not retrieval, which is largely solved — and benchmarks like MathHay (51% accuracy at 128K tokens for Gemini-1.5-Pro) make the gap quantitatively visible. We propose a research agenda focused on architecture-task fit, hierarchical multi-scale memory, and reasoning-aware long-context evaluation.