Sequence Compression
11.4x
Propositions vs BPE Subword Tokens
Attention Compute Saved
130.0x fewer ops
O(C²) Concept vs O(T²) Token Matrix
KV-Cache Memory Saved
91.2%
Continuous 256-D State Retention
Engine Latency
-- ms
CPU Flow-Matching Inference
1. Input Concept Stream
SVD / PCA Projection of S²⁵⁵
Live 256-D Concept Trajectory Map
Input z₁..t
Predicted ẑₜ₊₁..k
Stage 1 • ConceptSegmenter + ConceptSpaceEncoder
Segmented Input Concept Spans (Z₁:ₜ)
Stage 2 • Causal Concept Attention + Flow-Matching
Continuous Latent Prediction
Autoregressive Next-Concept Rollout (Ẑₜ₊₁:ₜ₊ₖ)
Decoded Surface Realization from Predicted Concept Trajectory:
Stage 3 • Spherical Linear Interpolation (SLERP)
Geodesic Concept Bridge Lab
Persistent SQLite Concept Memory Bank
Concept Atlas & Zero-Shot Proposition Ingestion
Add custom domain concepts below — the NCM Engine immediately projects them into the 256-D manifold for future rollouts.
| Concept ID & Title | Domain | Intent / Modality | Semantic Proposition | Next Transition |
|---|---|---|---|---|
| ncm-001 Semantic Concept Segmentation | neural_ai | synthesis quantitative_compact | Decompose raw token streams into atomic semantic propositions to reduce sequence length by 10x. | ncm-002 |
| ncm-002 Continuous Manifold Projection | neural_ai | transformation imperative_goal | Embed each proposition into an L2-normalized 256-dimensional continuous concept vector space. | ncm-003 |
| ncm-003 Causal Concept-Level Self-Attention | algorithms_math | transformation quantitative_high | Compute causal attention directly over concept embeddings, cutting O(N^2) token complexity by two orders of magnitude. | ncm-004 |
| ncm-004 Iterative Flow-Matching Refinement | neural_ai | prediction temporal_future | Refine predicted next-concept vectors via continuous denoising flow steps onto the high-density semantic manifold. | ncm-005 |
| ncm-005 Hierarchical Concept Memory Retrieval | data_knowledge | transformation negation_contrast | Anchor refined latent vectors against a persistent Concept Atlas to decode coherent surface realizations without hallucination. | ncm-006 |
| ncm-006 Sub-Quadratic Inference Scaling | systems_compute | causal_trigger high_certainty | Execute long-horizon multi-step reasoning trajectories in milliseconds on standard CPU or GPU hardware. | ncm-007 |
| ncm-007 Goal-Conditioned Trajectory Planning | autonomous_control | transformation imperative_goal | Steer autoregressive concept generation toward a target goal embedding using lookahead cosine-gradient guidance. | ncm-008 |
| ncm-008 Epistemic Uncertainty & Manifold Distance Audit | security_trust | prediction hypothesis | Quantify prediction confidence via geodesic cosine similarity and manifold energy before emitting surface text. | ncm-001 |
| sys-101 Zero-Dependency Modular Service Architecture | software_eng | architecture high_certainty | Structure backend pipelines into stateless deterministic compute kernels backed by transactional SQLite storage. | sys-102 |
| sys-102 Cryptographic Integrity & Deterministic Execution | security_trust | constraint high_certainty | Enforce strict schema validation, deterministic seeding, and air-gapped local execution across all inference endpoints. | sys-103 |
| sys-103 10x Compute & Bandwidth Cost Reduction | economics_value | prediction negation_contrast | Eliminate redundant token-by-token KV-cache overhead to lower inference memory bandwidth and operational cost by 90%. | ncm-006 |
| math-201 Orthogonal Subspace Decomposition | algorithms_math | architecture hypothesis | Partition latent concept representations into disentangled intent, ontology, epistemic modality, and lexical subspaces. | math-202 |
| math-202 Geodesic Latent Interpolation (SLERP) | algorithms_math | synthesis imperative_goal | Synthesize intermediate conceptual bridges between distant domains using spherical linear interpolation on the unit hypersphere. | ncm-005 |