Verace V1: A Multimodal LLM
We present Verace V1, a reference architecture that replaces four standard transformer subsystems with continuous, manifold-constrained alternatives, and adds a fifth mechanism for per-token adaptive compute. Sequence mixing is handled by Spectral State-Space Differential Attention (SSSD), whose per-step state update is an exactly orthogonal Cayley transform, giving Frobenius-norm conservation by construction rather than by regularization. The KV cache is replaced by a Continuous Holographic Associative Memory (CHAM), a fixed-size complex matrix updated in place and re-projected onto the unitary manifold by Newton-Schulz retraction after every step, giving O(1) memory and per-token compute in sequence length. Discrete routed mixture-of-experts is replaced by a Manifold Continuous MoE (M-CMoE), which assembles each token's expert contribution locally from a shared low-rank basis, removing the all-to-all dispatch step that discrete MoE requires at scale. An Adaptive Cognitive Depth Engine (ACDE) gives each token its own early-exit depth via per-example active-token gathering, which we show is exactly batch-independent. Parameter updates use a Unitary Muon optimizer that projects every 2D parameter's momentum onto the Stiefel manifold via exact SVD-based polar decomposition; we report a negative result for the iterative Newton-Schulz alternative, which fails to converge for the rectangular, small-singular-value matrices typical of real gradients. Every manifold-constraint claim in this paper is checked by an executable test, not just derived on paper: we report the actual measured deviations from those constraints, all of which hold to within 1e-3 or better. This is a reference implementation, not a trained model: it ships with no pretrained weights, and none of the results here should be read as evidence about downstream task performance.