Align
Equivalent meanings should converge across languages without erasing useful linguistic metadata.
Registry-backed multilingual meaning
Meaning, independent of language.
Embedded Semantics maps natural-language expressions into a shared concept space, then anchors probabilistic retrieval to stable, inspectable semantic identities.
- Language
- Many expressions
- Identity
- One stable concept
- Vectors
- Evidence, not authority
I am hungry EN
Tengo hambre ES
お腹が空いた JA
أنا جائع AR
CONCEPT
PERSON_HUNGRY
stable identity
Resolve
Embeddings retrieve candidates; a versioned registry determines which semantic identity is authoritative.
Explain
Definitions, examples, hard negatives, provenance, and uncertainty remain inspectable.
Architecture
Separate the probability from the meaning.
A vector is a coordinate produced by a model version. A concept identity should survive model upgrades, language changes, and retraining. Embedded Semantics treats those as separate layers.
- ExpressionNatural languageAny supported language or locale
- RetrievalEmbedding + rerankingCandidate generation with calibrated confidence
- IdentityConcept registryStable ID, definition, evidence, version
- OutputSemantic packetConcepts, relations, residue, rendering
Starter registry
Concepts are first-class records.
Direct answer
What is a registry-backed semantic embedding system?
It is a system where embeddings locate likely meanings, but persistent concept records define those meanings. This lets multilingual expressions converge in vector space without making a model's temporary coordinates the semantic source of truth.
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