HAMBAZ AI
Phase-Space Intelligence
Standard Transformers hit a quadratic O(N²) wall—requiring hundreds of gigabytes of KV-cache and forgetting everything outside a fixed window. Hambaz AI replaces discrete token attention with coupled physical harmonic oscillators, achieving strict O(1) constant state memory and continuous lifelong reasoning on the path to true AGI.
True General Intelligence requires continuous, lifelong state evolution without memory explosion. Here is why legacy Softmax Self-Attention cannot scale to continuous AGI—and how Hambaz solves it from physical first principles.
Biological brains do not store a growing matrix of every millisecond of past sensory input. Instead, cortical networks encode experiences as continuous multi-frequency neural oscillations. Hambaz models sequence semantics in a continuous position-momentum phase space that evolves indefinitely without resetting.
Transformers attend to every noisy token equally until billions of parameters force sparsity. Hambaz employs a proprietary non-linear energy differential gate that balances instantaneous potential and kinetic phase energy—filtering out entropy spikes and hallucination cascades automatically.
Whether processing token #100 or token #1,000,000, Hambaz Brain v4 updates its 18-layer recurrent state in-place inside a fixed 0.84 MB buffer. It runs natively on cloud GPUs, TPUs, and even low-power edge CPUs with zero thermal throttling.
Drag the context length slider below to compare active recurrent state memory between a standard Transformer and Hambaz Phase-Space Architecture.
Inspect verified real-time generations captured from our 452M-parameter Hambaz Brain v4 foundation checkpoint across English Science, Classical & Egyptian Arabic, Python Algorithms, and Mathematics.
Because Hambaz combines $O(T \log T)$ spectral training with Surprisal-Gated Backpropagation and Net2Net Manifold Expansion, it reaches convergence up to 2.7×–3.5× faster than standard Transformers on identical distilled datasets.
18-layer, 72-oscillator bilingual (English + Arabic) & coding foundation model. Runs locally on consumer CPUs or cloud GPUs with a 0.84 MB recurrent state.
32-layer continuous phase-space model engineered via Net2Net manifold expansion for enterprise coding, sovereign MENA deployment, and autonomous robotics.
80-layer, 640-oscillator frontier model maintaining a ~40 MB recurrent wave state at 1,000,000+ token context—eliminating the 320 GB Transformer KV-cache bottleneck.
We are onboarding select research partners, enterprise developers, and cloud ecosystem reviewers to test Hambaz Chat and our O(1) Phase-Space Inference API.