Entity collisions and the interpretive graph: advanced stabilization
An entity collision is not merely an occasional error. It is a perturbation of the interpretive graph. When two identities overlap in the signal environment, AI can stabilize a hybrid entity that does not exist.
This page formalizes the collision as a structural problem: identity, neighborhood, co-occurrences, source routing, and statistical weighting.
Extended definition
Entity collision (graph level): phenomenon where two distinct identity nodes become partially indistinguishable in the interpretive graph used by AI systems, producing a fusion, substitution, or attribute contamination.
Advanced collision types
- Nominal collision: strict homonymy.
- Semantic collision: similarity of offerings or lexical field.
- Relational collision: poorly hierarchized related entities.
- Temporal collision: former entities still persistent.
- Algorithmic collision: clustering or retrieval error.
Structural indicators
- illegitimately shared attributes
- variant descriptions depending on formulation
- multi-surface identity conflicts
- reappearance of foreign attributes after correction.
Advanced approach in 6 axes
1) Canonical isolation
Strengthen lexical and relational singularity.
2) Explicit disambiguation
Clarification pages, declared exclusions, unique identifiers.
3) Relational structuring
Clear hierarchy of related entities.
4) Neighborhood neutralization
Reduction of ambiguous co-occurrences.
5) Multi-AI tests
Compare responses across different models.
6) Remanence monitoring
Verify that the collision does not reappear after correction.
Artifacts
- Interpretive graph map.
- Collision registry (multi-surface).
- Versioned correction journal.
- Adversarial test battery.
