🌌 QORVEX v8: The Hyperspace Kernel

🏗️ Total Architecture

1. The Core: Hyperspace Transformer

• Attention: Uses the HyperspaceQPU to project attention weights into a quantum state.
• Mechanism: Instead of simple dot-product attention, it calculates expectation values of a Matrix Product State (MPS), allowing for non-linear, high-dimensional structural resonance.
• Config: Target 1024d / 24L / 32H (Qorvex-class).

2. The Nervous System: Calibration-Aware Routing

• CalibrationMap: A real-time map of hardware "noise" and "dead zones."
• Router: Uses a Dijkstra-based pathfinding algorithm to route tensor contractions through the most stable hardware paths.

3. The Cognitive Layer: PUP & SLA²

• PUP: Quantifies uncertainty at every layer, preventing confident hallucinations.
• SLA²: Implements a biologically plausible depth profile (L2 peak, L4 pump) to optimize information flow.

🚶 Walkthrough

1. Input $\rightarrow$ Tokenized BPE $\rightarrow$ Embedding.
2. Hyperspace Block $\rightarrow$ Quantum Attention (MPS) $\rightarrow$ MLP $\rightarrow$ LayerNorm.
3. Routing $\rightarrow$ The CalibrationRouter optimizes the contraction on the fly.
4. Output $\rightarrow$ PUP-weighted prediction $\rightarrow$ Final Token.

📈 Benchmarks

• Iron Gap: Quantifies the gate-cost of quantum emulation vs. native torch.
• Stability: Verified via CPU-fallback for driver-agnostic deployment.