NeuronScope
ArchitectureStablesince v0.4.0

Canonical Fingerprinting Architecture

The end-to-end architecture of deterministic fingerprinting — input adapters, canonicalization rules, 32-byte BLAKE3 hashing, and namespace tagging.

Audience: System Architects & ML EngineersRead: 8 minEdit on GitHub Source

The NeuronScope Fingerprint Pipeline computes deterministic, content-addressed identity digests across models, vector database collections, structured prompts, and hardware environments.

Interactive pipelinehover · focus · click
ModuleInput AdapterModuleCanonicalizerCoreBLAKE3 HasherModuleNamespace TaggerOutputFingerprint
Module
Input Adapter

Resolves Python models, SLMs, vector indexes, or prompts into canonical objects.

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Layer & Module Boundaries

  • Input Adapter: Inspects user objects and exposes standardized structural protocols.
  • Canonicalizer: Sorts model tensors in parameter-name order, validates prompt variable bindings, and normalizes vector index schemas.
  • BLAKE3 Hasher: Streams raw byte representations into the BLAKE3 digest engine with fixed chunking rules.
  • Namespace Tagger: Stamps the scheme, canonicalizer version, and namespace tag (fp:model, fp:prompt, fp:dataset, fp:env).

Platform Hashing Invariants

Drift Prevention & Failure Modes

Fingerprinting prevents silent drift by detecting un-seeded random initializations, mutated prompt templates, modified vector store schemas, or un-pinned driver updates before execution begins.

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