ADR-013: Observer Effect as Invariant Selection

Status: Accepted Version: 1.0 Date: 2025-12-21 Supersedes: N/A Related ADRs: ADR-003, ADR-004, ADR-005 Related PRDs: N/A

Context

As SEA-Forge™ operates with AI agents making proposals within a governed semantic space, we need a principled model for how observation, decision, and commitment interact with invariant enforcement. The traditional understanding of “reasoning” as “thinking harder” is insufficient—what actually makes reasoning valid is invariant preservation across transformation steps.

Decision

Adopt the Observer Effect as Invariant Selection principle: observation (whether by human, agent, or automated process) is an invariant-selection process that collapses a possibility space into a constrained trajectory, making cognition and governance possible.

Core Insight

The observer effect is an invariant-selection process that collapses possibility space into a stable trajectory, enabling cognition by stabilizing identity, order, and persistence.

This applies isomorphically across:

• Quantum mechanics: Measurement collapses state space
• Cognition: Attention collapses interpretive space
• Architecture: Design decisions collapse solution space
• Governance: Commits collapse possibility into auditable history

Implications for SEA-Forge™

1. Before observation: Many possible invariant-consistent evolutions exist
2. After observation: One invariant-consistent trajectory is bound
3. Observation does not create invariants: It selects among pre-existing valid paths

The Cognition-Invariant Link

Cognition requires three fundamental invariants:

1. Persistence — Something stays long enough to be reasoned about
2. Identity — It remains “the same thing” across moments
3. Order — Relations between moments are preserved

Without these invariants, there is no concept formation, reasoning, memory, or learning.

Time vs. Temporal Order

A critical distinction: reasoning depends on invariant temporal order, not invariant time itself.

• Time flows, moments change, durations vary (time is not invariant)
• What is invariant: temporal ordering and continuity
  • A happens before B
  • A remains identifiable while B is evaluated
  • Transformations are ordered

This aligns with process ontology and prevents reification of static entities.

Rationale

LLMs operate in semantic continuity space, not invariant space:

• They preserve local plausibility and pattern resemblance
• They do not track invariant obligations across steps
• They do not refuse to proceed when invariants would break

Reasoning is not thinking harder. Reasoning is refusing to proceed unless invariants remain intact.

SEA-Forge™ operationalizes this by turning decisions into:

• Rules (SBVR, CALM controls)
• Validations (schema checks, graph shapes)
• Pipeline gates (CI/CD enforcement)
• Violation events (audit trail)

So observation doesn’t just constrain—it enables cognition by making the space navigable, and enables automation by making the space checkable.

Alternatives Considered

“LLMs Should Reason Better” (Internal Improvement)

Rejected — The gap is architectural, not model-capability. LLMs lack externalized invariant state; no amount of scale fixes this.

Unconstrained Agent Autonomy

Rejected — Without invariant enforcement, agents produce confident nonsense that sounds right but violates domain rules.

Document-Based Governance

Rejected — Documents drift immediately. Invariants must be machine-checkable to be trustworthy.

Constraints

• MUST treat observation as invariant-selection, not invariant-creation
• MUST enforce invariants through machine-checkable rules (SBVR, CALM, schemas)
• MUST preserve persistence, identity, and order as fundamental invariants
• MUST externalize invariant state outside LLM token streams
• MUST NOT rely on “LLMs reasoning harder” as a governance strategy

Quality Attributes

• Principled foundation for safe agent automation
• Clear model for agent-governance interaction
• Unified cognitive and architectural concerns
• Auditability through invariant violation tracking

Bounded Contexts Impacted

• Semantic Core
• Architectural Governance (CALM)
• AI Agent Runtime
• Invariant Regime

Consequences

Positive

• Clear model for how agent proposals interact with governance
• Principled foundation for “safe agent automation”
• Explains why regeneration (not mutation) is the correct update pattern
• Unifies cognitive and architectural concerns under one model

Negative

• Requires explicit invariant declaration (upfront modeling overhead)
• May feel philosophically abstract to developers not versed in foundations
• Demands tooling to make invariant violations visible and actionable

Additional Notes

Related Specifications

• REF-012: Invariant Regime Specification

⏳ Post-MVP (foundational theory; implementation via Invariant Regime)