Semantic primacy asserts that semantic structure precedes model behavior as the primary determinant of executable meaning.
Learning in this framework is not treated as automatic parameter adaptation, but as a governed semantic process subject to meta-cognitive constraints.
This is not a claim about intelligence or cognition. It is a technical premise about where control, accountability, and stability must reside in executable language systems.
Models do not define semantics
Models generate outputs. They do not define the semantics under which those outputs are interpreted, validated, or executed.
Treating model behavior as the source of semantics conflates statistical correlation with operational meaning.
This work rejects that conflation.
Semantics as a pre-model constraint
Semantics is treated here as a pre-model constraint that determines:
- What constitutes a valid intent
- What counts as acceptable execution
- What conditions must be satisfied before action is taken
- How responsibility and completion are evaluated
Models operate within these constraints. They do not generate them.
Why model-centric systems collapse under execution
Model-centric systems rely on emergent behavior to infer intent, correctness, or completion.
Such systems may perform well in interactive settings, but they degrade when language is used to drive execution, especially across organizational or institutional boundaries.
Failure modes include:
- Ambiguous authority
- Non-reproducible actions
- Irrecoverable side effects
- Inability to assign responsibility
These are not model failures. They are semantic failures.
Semantic structures as control surfaces
By asserting semantic primacy, control is relocated from model internals to explicit, inspectable structures.
These structures define:
- Execution boundaries
- Delegation scope
- Completion conditions
- Permissible side effects
Models become components, not arbiters.
Relation to intermediate representations
Intermediate semantic representations exist because semantics cannot be safely inferred from outputs alone.
They externalize meaning into a form that can be reasoned about independently of any model.
Semantic primacy therefore necessitates an intermediate layer between language and execution.
Semantic primacy and accountability
Accountability requires that meaning be stable across time, execution context, and implementation.
If semantics are embedded in model behavior, they cannot be inspected, signed, or disputed.
By treating semantics as primary, responsibility can be assigned and evaluated without reference to opaque model states.
Implications for system design
Semantic primacy implies that:
- Models are replaceable
- Execution logic is auditable
- Governance is enforceable
- Standards are meaningful
Systems designed under this premise remain coherent as models evolve.
Boundary clarification
Semantic primacy does not deny the importance of models. It denies their authority over meaning.
Models are engines. Semantics defines the road.
Reading note
This premise underlies all subsequent discussions of:
- Semantic ISA
- Completion semantics
- Delegation and responsibility
- Institutional AI compatibility
It should be read as a technical constraint, not a philosophical assertion.