Title: MANAR: Memory-augmented Attention with Navigational Abstract conceptual Representation
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: brain inspired, memory-augmented architecture, attention, out-of-the-box thinking, imagenet, librispeech, quadratic complexity

Abstract: Transformers - and their multi-head attention (MHA) core - power today's leading models across a broad application spectrum. Yet MHA contextualizes each token through explicit, pair-wise interactions with every other token, yielding quadratic time/space cost and an unbounded, linearly-growing context. This \textit{direct all-to-all} modeling is both the source of attention's expressiveness and a barrier to scaling. We address this bottleneck by augmenting attention with a trainable external memory that stores both conceptual and relational general representations learned during training. For every input, lightweight scalable retrieval produces a fixed-size set of memory retrieved concepts whose values are fused into a compact Abstract Conceptual Representation (ACR). Tokens then attend jointly to (i) the global, concept-level ACR and (ii) a short local context, completely sidestepping all-to-all token interactions. The result is a non-convex pathway that provide the model with an \textit{out-of-the-box thinking} contextualization - i.e., beyond the convex hull spanned by the input values - while reducing complexity to linear time and memory. Integrated as a drop-in replacement for MHA, our layer (MANAR) preserves accuracy on ImageNet-1K (82.3\% top-1 with a DeIT-B backbone) and LibriSpeech (2.9/6.8\% WER on test-clean/other) yet cuts inference latency by up to 14.8x and peak GPU memory by up to 9.3x as sequence length grows to 4K. A simple weight-copy knowledge-transfer procedure trims training cost by $\approx$99\% versus training from scratch. Finally, Convex Hull Membership (CHM) tests show that $>$50\% of MANAR’s outputs lie outside the convex span of the input values, quantitatively confirming its out-of-the-box contextualization.