What Is ECAI? Representation, Retrieval and Reasoning

ECAI explores structured knowledge representation using cryptographic identities, including elliptic-curve constructions. In the documented platform, this sits alongside indexing, retrieval, explicit relation rules and an LLM bridge. Read the implementation overview before choosing a deployment.

The goal is inspectable knowledge work: retain the record, identify its source, make transformations explicit and attach evidence to an answer or action.

For a suitable elliptic curve over a finite field, valid points and the point at infinity form a group. Point addition and scalar multiplication are defined operations. Their results can be repeated and checked.

It does not follow that every possible idea has a unique point, that an on-curve statement is true, or that point negation means logical negation. Those interpretations require a representation scheme with stated semantics and tests. A false statement can be encoded just as consistently as a true one.

Likewise, shared-key computation establishes a cryptographic relationship; it does not produce agreement about the truth of a claim. Computing N * Q does not by itself retrieve the answer to a question encoded as Q.

1. Select a corpus and preserve source identifiers and processing permissions.
2. Canonicalise fields under a versioned schema and retain the original records.
3. Build the supported index and record its version and artifact identities.
4. Run a query; return the retrieved records with their source references.
5. If a model composes an answer, evaluate it against those records.

Retrieval depends on the corpus and retrieval algorithm. Hashing a record can help identify it, but cannot replace storing it or checking its meaning. The Wikipedia guide and search reference describe the concrete paths.

The relation layer defines typed identities and composition rules for structural relationships. A reproducible result from those rules is useful within their domain. Broader semantic reasoning requires a benchmark with known answers and a meaningful baseline. The available article inspects benchmark code; it does not report an executed score.

Geometric representations may offer useful ways to organise or compute over knowledge. Demonstrating that value requires a specified task, comparable baseline, accuracy measurements, resource costs and failure analysis. Claims of universal truth retrieval, reversible hashing or cryptographic consciousness are not established by the documented implementation.

See research and earlier drafts, encoding checks and the platform vision.