Multimodal Semantic Continuity Layer

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Architecture Definition Document (ADD)

Domain: Enterprise AI Platform Architecture
Initiative: Multimodal Semantic Continuity Layer
Review Status: Proposed for Peer Review
Version: 1.0

1. Architecture Vision & Business Alignment

Current decoupled generative architectures suffer from Cross-Model Epistemic Discontinuity, wherein the semantic state and provenance of generated artefacts are lost across model interfaces. This leads to non-monotonic capability degradation, policy failures, and the inability to distinguish synthetic provenance from visual realism.

Business Objective: Enable deterministic policy enforcement, auditable synthetic media generation, and persistent character/entity continuity across heterogeneous generative models (Language, Vision, Audio) without restricting the underlying model capabilities.

Target Capability: A provenance-driven orchestration layer—the Continuity Broker—that maintains canonical semantic state, resolves conversational intent to persistent entities, and automatically reconciles generative drift.

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2. Application & Component Architecture (ArchiMate Logical View)

The architecture introduces a central control plane to orchestrate probabilistic generation models.

2.1 Core Application Components

• Continuity Broker (Core Orchestrator): The central state-management application service. It exposes APIs for model orchestration and state retrieval.
• Entity Registry Service: Maintains stable, unique identifiers for persistent semantic objects (e.g., PERSON:FICTIONAL_0042).
• Canonical State Store: The authoritative repository for the requested, non-transient attributes of an entity.
• Provenance Ledger: A directed acyclic graph (DAG) service tracking the parent-child lineage and transformation history of all generated outputs.
• Intent Resolver: NLP component that maps natural language references (“her”, “the previous scene”) to explicit Entity IDs within an active session.
• Reconciliation Engine: A controller that compares the Canonical State against the Observed State (via Vision/Analysis models) to calculate a continuity score and enforce automatic regeneration if the score falls below threshold $\tau$.
• Policy Context Service: A decision-engine wrapper that evaluates moderation rules using the full provenance context (origin + intent + history), rather than relying solely on pixel-level artefact analysis.

2.2 Component Interaction Flow

1. Workflow Engine requests an entity transformation.
2. Intent Resolver queries the Entity Registry to bind the request to a specific Entity ID.
3. Continuity Broker retrieves the baseline from the Canonical State Store.
4. Policy Context Service pre-evaluates the request against organizational safety rules.
5. Generation Service (External/Internal API) produces a candidate artefact.
6. Analysis Service evaluates the candidate, producing an Observed State.
7. Reconciliation Engine compares Canonical vs. Observed. If continuity $\geq \tau$, the artefact is approved, written to storage, and recorded in the Provenance Ledger.

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3. Data & Information Architecture

This domain formalizes the separation between what the system wants to generate and what it actually generated.

3.1 State Definitions

The semantic state $S$ of any entity is formally defined as:

$$S = {P, E, I, T, C}$$

Where $P$ is provenance, $E$ is entity identity, $I$ is user intent, $T$ is transformation history, and $C$ is policy context.

3.2 Canonical vs. Observed Schema (JSON Data Model)

The canonical state is treated as an immutable master record for a session, while observed states are candidate evaluations.

{
“entity_id”: “PERSON:FICTIONAL_0042”,
“trust_classification”: “SYSTEM_VERIFIED”,
“canonical_state”: {
“identity”: “synthetic_human”,
“invariants”: [“hair_texture”, “facial_morphology”, “eye_spacing”]
},
“provenance_lineage”: {
“artifact_id”: “IMG:88227”,
“parent_artifact”: “IMG:88213”,
“operation_type”: “environment_transformation”
}
}

3.3 The Provenance DAG (Directed Acyclic Graph)

Every artefact is a node in a DAG. Edges represent transformation events. This structure allows the system to trace any derivative image back to its original generation event to prove it is synthetic, bypassing the need for probabilistic “fake detection” on internal assets.

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4. Technology & Integration Architecture (API Contracts)

Integration between the probabilistic generative models and the deterministic control plane is managed via strict API contracts.

Interface Name	Protocol	Source	Destination	Primary Payload
IdentifyEntity	gRPC	Orchestrator	Intent Resolver	Conversation context $\rightarrow$ Entity ID array
GetCanonicalState	REST	Orchestrator	Continuity Broker	GET /entity/{id}/canonical
ExecuteGeneration	REST	Orchestrator	Model Gateway	Envelope: { operation, invariants, mutable_params, provenance_id }
EvaluateArtefact	gRPC	Model Gateway	Analysis Service	Base64 Artefact $\rightarrow$ { observed_attributes, confidence }
ReconcileState	REST	Analysis Service	Reconciliation Engine	POST /reconcile (Observed metrics vs. Canonical state)
EvaluatePolicy	REST	Orchestrator	Policy Engine	POST /policy/evaluate (Artefact + Lineage DAG)

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5. Security & Trust Architecture

The architecture introduces strict trust boundaries to prevent adversarial manipulation of the semantic state.

5.1 Trust Boundaries

• Zone A: Deterministic Control Plane (High Trust): Houses the Continuity Broker, State Store, and Provenance Ledger. Only internal microservices can write to this zone via authenticated, role-based service accounts.
• Zone B: Generative Execution (Low Trust): Houses the LLMs and Image Generators. Treated as untrusted endpoints. Output from Zone B is never written directly to the State Store without passing through Zone C.
• Zone C: Observation & Reconciliation (Validation Trust): Houses the Vision/Analysis models and Reconciliation Engine. Responsible for validating Zone B outputs before committing them to Zone A.

5.2 Threat Mitigations

1. Provenance Spoofing (Attacker injects external image claiming it is synthetic): Mitigated by cryptographically signing generation events. The DAG only accepts parent hashes that exist within the internal ledger.
2. Entity Poisoning (Malicious prompt attempts to overwrite invariants): Mitigated by strict separation of $ASSERTED$ (Canonical) and $OBSERVED$ attributes. Canonical state mutations require explicit UPDATE operations via the intent resolver, which rejects conflicting invariant modifications.
3. Adversarial Similarity (Prompt attempts to recreate a protected identity incrementally): Mitigated by the Policy Context Service evaluating the entire historical DAG, identifying malicious convergence across multiple transformation steps, not just a single prompt.

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6. Architecture Review Criteria (ARB Checklist)

For the architecture to be approved for Minimum Viable Product (MVP) implementation, it must pass the following architectural gates:

• [ ] State Isolation Check: Does the architecture prevent generative models from directly overwriting the Canonical State Store?
• [ ] Provenance Integrity Check: Are cryptographic signatures or immutable hash chains applied to the artifact DAG to prevent spoofing?
• [ ] Policy Independence Check: Can the Policy Context Service block an action based on historical DAG lineage, even if the current prompt and candidate artefact appear benign in isolation?
• [ ] Reconciliation Determinism Check: Are the thresholds ($\tau$) for automated regeneration defined as configurable business logic rather than hardcoded into the models?
• [ ] Scalability Check: Is the DAG storage optimized (e.g., using PostgreSQL recursive CTEs or a dedicated graph database) to prevent latency bottlenecks during policy evaluation?