Semantic Substrate

Make offer

The canonical coordinate for tracing what a neural system knew, used, and decided.

A cross-cutting position naming the discipline of attributing neural-network outputs back to their training data, feature weights, and reasoning paths.

Coordinated sets this position belongs to — the coverage it extends. Counts are the live cluster size in the graph.

Attribution cluster357Neural cluster42

Architectural context

Neural · Cross-Vertical · 2 compound moats. Architectural surface: Neural, Attribution.

Layer position: Cross-cutting

AttributionNeural

Why this is canonical

'Brain' anchors the neural computing metaphor; 'attribution' names one of the most consequential open problems in AI — understanding why a model produced a given output. The compound is legible to both technical and regulatory audiences, and occupies a position that grows in strategic value as explainability requirements tighten.

Where it fits

A few directions this coordinate opens —

Explainability and XAI tooling
A home for platforms that surface attribution maps, SHAP values, or saliency traces to explain neural-model decisions to developers, auditors, and end users.
XAI platform builders, enterprise ML teams, compliance-tool founders
Data-lineage and IP provenance
A namespace for tooling that traces which training data contributed to a given model output — essential for copyright, licensing, and data-governance disputes.
LegalTech, data-licensing platforms, foundation-model teams

Illustrative, not exhaustive — held as a transferable canonical position, open to the buyer's own use.

← More in Neural