Docs - HoloHDC
HoloHDC Technology
Meaning-preserving compression using hyperdimensional vectors and NSM primitives.
What is HoloHDC?
Holographic Hyperdimensional Computing (HDC) is our research track for encoding meaning into 10k-dimension bipolar vectors. Unlike token truncation, HoloHDC aims to preserve intent, allow lossy reconstruction, and keep similarity relationships stable across modalities (text, code, audio, images).
Key Ideas
1) NSM Decomposition: 64 semantic primitives (Natural Semantic Metalanguage) provide a stable basis to encode meaning.
2) Hypervectors: Each primitive maps to a 10k-dim bipolar vector; binding and bundling allow compositional meaning with resilience to noise.
3) Chi Score: A telemetry metric that estimates how well intent is preserved after compression or transformation.
Why It Matters
- Compression without losing the point: compress context while keeping intent traceable.
- Cross-modal: primitives work for code, prose, image captions, and audio transcripts.
- Swarm-friendly: hypervectors allow lightweight similarity search and routing inside the Hive.
- Evidence-aware: Chi tracks fidelity so humans know when to trust or re-run.
How to Use It Today
- Use the mock API in
/api/holohdcto prototype integrations. Responses are stubbed for now. - Watch Chi scores in responses; treat them as guidance for fidelity.
- Do not store production secrets in compressed blobs until the audited release ships.
What Comes Next
We are moving from mock responses toward real encoders/decoders with formal evals, reproducible seeds, and verified Chi measurements. Expect breaking changes while the research pipeline lands.