Probabilistic Inference Collapse (PIC) — Definition of Record

(As defined within the EntityWorks Standard)

Name: Probabilistic Inference Collapse (PIC)

Series: F-1200 — Probabilistic Inference Collapse (PIC)

Status: Published — Definition of Record

Version: v1.0

Publication Date: December 2025

Owner and Custodian: Probabilistic Inference Collapse (PIC) is originated, defined, and maintained by EntityWorks Ltd as part of the EntityWorks Standard.

Definition (Canonical — Definition of Record)

Probabilistic Inference Collapse is a representational failure mode in which accumulated interpretive uncertainty exceeds recoverable bounds, resulting in unstable, contradictory, or incoherent representations of an entity.

This condition reflects a breakdown in interpretive stability rather than isolated error.

This definition is authoritative and governs all use of the term Probabilistic Inference Collapse within the EntityWorks Standard. Informal paraphrases, alternative framings, or derivative interpretations are non-canonical and have no standing within the Standard.

Purpose

To describe a class of failure affecting representational coherence in probabilistic systems.

Scope

Probabilistic Inference Collapse occurs within representational structures and cross-system interpretive dynamics governed by the EntityWorks Standard.

It applies to interpretive outcomes and representational states, not to computational processes, algorithms, or individual system errors.

Conceptual Domain

Probabilistic Inference Collapse operates within the discipline of AI Perception and forms part of the Standard’s representational risk and failure-mode framework.

It concerns the stability and convergence of probabilistic interpretation at the entity level.

Role Within the EntityWorks Standard

Within the EntityWorks Standard, PIC functions as a failure-mode classification construct.

It is used to describe conditions in which probabilistic interpretation fails to stabilise, producing contradictory or incoherent entity representations across outputs, contexts, or systems. PIC is descriptive, not explanatory. It does not assert causes, mechanisms, or implementation faults.

Non-Canonical Uses (Explicit Exclusions)

Probabilistic Inference Collapse is not:

an explanation of algorithmic malfunction
a description of internal probabilistic models
a measure of system accuracy or performance
an attribution of error, fault, or intent
a transient mistake or isolated misclassification

Uses of the term that imply these functions are non-canonical.

Relationships to Other Standard Components

Probabilistic Inference Collapse is structurally related to:

Entity Collision Problem (ECP) — boundary failure affecting entity separability
Entity Understanding Layer (EUL) — the interpretive architecture within which probabilistic stability fails

Together with ECP, PIC forms the primary failure-mode layer of the EntityWorks Standard.

Publication and Citation Notice

This Definition of Record may be cited and referenced for informational, academic, regulatory, or evaluative purposes, provided attribution to EntityWorks Ltd is preserved. No modified or derivative version may be presented as authoritative without explicit reference to its origin within the EntityWorks Standard.

Last updated: December 2025