Manifold Compression Thesis
Information Has Geometric Structure
Traditional compression treats data as linear streams. We treat it as a geometric manifold. Every token sequence maps to a coordinate in a high-dimensional space defined by its Entropy ($h$), Coherence ($q$), and Hazard ($\lambda$).
Regions of high curvature (high $\lambda$) represent "Hazard Signatures"—points where the system must branch or rupture. By storing only these critical topological features, we achieve 41x compression while retaining the structural integrity of the original context.
Token Embedding Manifold
Visualization: 3D projection of token embeddings. Color = Hazard Intensity.
Structural Metrics
Entropy Reduction ($h$)
As context is compressed, raw entropy decreases. We track the rate of information loss versus structural retention.
Local Curvature ($\lambda$)
High curvature indicates a "Hazard" or "Rupture Point"—a regime change in the data stream that requires immediate attention.
Coherence ($q$)
Coherence measures the self-similarity of the manifold. High $q$ implies a stable, predictable regime.
Hazard Cancellation & Verification
Interference Mechanism
*False positives (noise) destructively interfere over recursive passes, while true structural signals reinforce.
Gated Verification
In the Structural Manifold engine, verification is Hazard-Gated. We do not verify every token. We only trigger expensive verification when the local curvature ($\lambda$) exceeds a critical threshold.
- [!] High $\lambda$: Hazard detected. Full verification triggered.
- [✓] High $q$: Coherent regime. Skip verification (Compression).
- [~] Interference: Ambiguous signals self-cancel via recursive pass.
Structural Manifold Core
Real-Time Kernel State
Stream Analysis
Throughput
Live visualization of the C++17 Structural Manifold Core processing a random data stream.
Sub-millisecond latency. Lock-free ring buffer.