MANAR: Memory-augmented Attention with Navigational Abstract Conceptual Representation
arXiv Preprint Archive March 19, 2026 Zuher Jahshan, Ben Ben Ishay, Leonid Yavits
A new neural network layer called MANAR generalizes standard multi-head attention by implementing principles from Global Workspace Theory, a cognitive model of consciousness. MANAR uses a trainable memory of abstract concepts to create a central workspace that integrates information and then broadcasts it to all tokens. This design achieves linear instead of quadratic computational complexity while enabling creative synthesis beyond simple combinations of inputs. MANAR can replace standard attention in pretrained models by copying weights, and it matches or exceeds strong baselines across language (GLUE score 85.1), vision (83.9% ImageNet-1K accuracy), and speech (2.7% word error rate on LibriSpeech).