Structural visibility
See redundancy, coherence, hidden correlations, and manifold shape across massive embedding datasets. Know your data the way no cosine index can tell you.
Spectral Intelligence Platform
AI-native data operations — at structural depth.
Genefold transforms embeddings into compact spectral artifacts that power search, drift detection, OOD monitoring, and data valuation across ML and LLM workflows.
Spectral Intelligence
A new category — structure-aware AI operations.
Cosine similarity tells you what is close. Spectral intelligence tells you why — revealing the manifold geometry, redundancy, and topology that cosine-only systems permanently discard. Genefold is the platform layer that makes that geometry a first-class operational asset.
Retrieval with topology
Recover coherent long-tail items aligned with global geometry — not just nearest neighbours.
Early drift detection
Reusable spectral signatures as an early warning system for domain shift and OOD behaviour.
A platform, not a point solution
Search, anomaly detection, diffusion workflows, dimensionality reduction, quality diagnostics, and data valuation — one Laplacian artifact, many operations.
Live Demo
Try spectral search on real vulnerability data.
~360k
CVE records
<100ms
Search latency
NVD
Data source
The CVE Search Engine indexes the National Vulnerability Database with ArrowSpace spectral retrieval. Search across ~360k CVE records and see how manifold-aware ranking surfaces relevant vulnerabilities that cosine-only baselines miss.
- ~360k CVE records from NVD
- Live tau modulation: spectral, hybrid, or cosine-like
- Compare spectral and baseline results side by side
CVE-2014-0160
Heartbleed — OpenSSL information disclosure
Spectral match
CVE-2021-3449
OpenSSL CA certificate denial of service
Baseline
CVE-2022-4304
OpenSSL BN_mod_sqrt timing side-channel
Spectral match
Engineering
How we build spectral intelligence.
Notes on systems, algorithms, and the engineering choices behind ArrowSpace. Written for the teams shipping embedding-heavy software in production.
002 — Foundations
Read all engineering notes
Genefold First Principles: Depletion of Uncertainty
Entropy is the irreducible uncertainty of the source. Cross-entropy is the mathematical structure that measures how close the model's distribution is to the truth. Training depletes the gap — the KL divergence.
001 — AlgorithmsDesigning ArrowSpace and graph wiring
How graph wiring in the feature-space Laplacian powers spectral indexing for vector retrieval, and what the experimental evidence shows on CVE and TREC-COVID.
Open source
Built in the open. Production-ready today.
pyarrowspace
arrowspace for Python — graph analysis and vector search. pip install arrowspace for spectral indexing, bounded scores, and manifold-coherent retrieval.
Vector analytics and search using dispersion models. cargo add arrowspace for graph analysis, search, and energy-distribution statistics.
arrowspace_tunerHyperparameter optimisation and benchmark tooling for ArrowSpace spectral pipelines. Tune tau-modulation, graph-wiring parameters, and retrieval profiles.
arro-serverArrow Flight RPC server for high-throughput spectral artifact exchange. Serves structured embedding operations over a binary columnar protocol — zero serialisation overhead.
arro-memoryPersistent spectral artifact store with versioning and lineage tracking. The structural memory layer for embedding pipelines — durable, queryable, replayable.
arrowspace-skills
A set of skills to use arrowspace and pyarrowspace — prompt-driven workflows for spectral operations.
Model Context Protocol server for ArrowSpace — enabling LLMs and agents to perform spectral search and embedding operations natively.
Building with embeddings?
Try ArrowSpace, open an issue, star the repo, or tell us how spectral search fits your stack. We read every message.
genefold-ai
Our company AI — trained on spectral intelligence principles and embedding workflows to assist with research, tooling, and data operations.
Found a bug? Open an issue · Want to contribute? Read the contributing guide
View all repositories at github.com/GenefoldEarly access
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Science & explainers
Core paper
2025
ArrowSpace: Spectral Search for Embeddings and Graph Analysis
Introduces spectral indexing with graph-Laplacian structure and bounded spectral scores for vector search.
Open paper page
Core paper
2026
Epiplexity And Graph Wiring: An Empirical Study for the Design of a Generic Algorithm
Every dataset generates information, every manifold draws a unique surface.
Open paper page
Software
Ready
ArrowSpace: a generic algorithm for data operations
Hundreds of downloads per week: pip install arrowspace
Core paper
2026
From Embedding Geometry to Spectral Search: Energy Dispersion Networks For Vector Retrieval
Lorenzo Moriondo, Ilias Azizi — coupling geometric similarity with spectral information for improved retrieval and adaptive tau-modulation in RAG pipelines.
Open paper