Verace V1

Verace V1 presents a groundbreaking architecture that redefines foundational models by replacing conventional transformer subsystems with continuous, manifold-constrained alternatives. Key innovations include: • Linear-cost attention through orthogonal state updates • O(1) memory footprint without a KV cache • Elimination of cross-device expert dispatch • Real per-token adaptive depth compute This reference architecture prioritizes efficiency and scalability, moving beyond the limitations of current large language models. Sequence mixing is managed by Spectral State-Space Differential Attention, ensuring Frobenius-norm conservation. The Continuous Holographic Associative Memory replaces the KV cache, maintaining a fixed-size complex matrix that updates in place, leading to consistent memory usage regardless of sequence length. Furthermore, the system replaces discrete routed mixture-of-experts with a Manifold Continuous MoE, allowing local assembly of expert contributions from a shared low-rank basis, thereby removing the all-to-all dispatch step common in scaled discrete MoEs. An Adaptive Cognitive Depth Engine provides per-token early-exit depth, which is shown to be batch-independent. All manifold and conservation claims are rigorously backed by executable tests, demonstrating measured deviations within strict tolerances. Designed for researchers and developers pushing the boundaries of large-scale sequence processing, Verace V1 offers a robust and verifiable framework for building next-generation models with enhanced performance characteristics. While currently a reference implementation without pretrained weights, its architectural innovations lay a strong foundation for future development and scaling.