Evidence layer
Probative surfaces brought into scope by this page
This page does more than point to governance files. It is also anchored to surfaces that make observation, traceability, fidelity, and audit more reconstructible. Their order below makes the minimal evidence chain explicit.
- 01Canon and scopeDefinitions canon
- 02Response authorizationQ-Layer: response legitimacy
- 03Weak observationQ-Ledger
Definitions canon
/canon.md
Opposable base for identity, scope, roles, and negations that must survive synthesis.
- Makes provable
- The reference corpus against which fidelity can be evaluated.
- Does not prove
- Neither that a system already consults it nor that an observed response stays faithful to it.
- Use when
- Before any observation, test, audit, or correction.
Q-Layer: response legitimacy
/response-legitimacy.md
Surface that explains when to answer, when to suspend, and when to switch to legitimate non-response.
- Makes provable
- The legitimacy regime to apply before treating an output as receivable.
- Does not prove
- Neither that a given response actually followed this regime nor that an agent applied it at runtime.
- Use when
- When a page deals with authority, non-response, execution, or restraint.
Q-Ledger
/.well-known/q-ledger.json
Public ledger of inferred sessions that makes some observed consultations and sequences visible.
- Makes provable
- That a behavior was observed as weak, dated, contextualized trace evidence.
- Does not prove
- Neither actor identity, system obedience, nor strong proof of activation.
- Use when
- When it is necessary to distinguish descriptive observation from strong attestation.
Surface coherence
This page is the canonical definition for surface coherence inside the interpretive governance corpus.
Surface coherence is the visible appearance of order, completeness, and credibility in an AI answer, regardless of whether the answer is faithful, authorized, or legitimate.
Short definition
Surface coherence is what makes a response feel right before it is audited. It is produced by fluent structure, relevant vocabulary, apparent sourcing, confident transitions, and a conclusion that fits the user’s expectation.
The problem is not coherence itself. The problem is treating coherence as proof. In interpretive governance, surface coherence is a symptom that must be tested against authority, source hierarchy, proof of fidelity, and response conditions.
Why it matters
Modern AI answers can be persuasive without being defensible. They can cite real sources, use the right vocabulary, mirror the user’s question, and maintain a clean internal structure while still exceeding the interpretive perimeter or producing unauthorized synthesis.
Surface coherence is therefore a reliability trap. It makes users, evaluators, and sometimes organizations believe that an answer has passed a higher standard than it actually has. This is especially risky when the response is used in decision-making, compliance, public representation, legal interpretation, medical triage, financial planning, HR, or institutional communication.
What it is not
Surface coherence is not evidence. It is not fidelity. It is not answer legitimacy. It is not proof that the correct authority has governed the conclusion.
It is also not always bad. A faithful answer should ideally be coherent. The issue arises when visible coherence substitutes for reconstructable evidence or hides missing response conditions.
Surface coherence is distinct from manufactured coherence. Manufactured coherence is the process that creates smoothness by hiding unresolved conditions. Surface coherence is the resulting observable appearance.
Common failure modes
- the answer looks structured and therefore feels verified;
- citations are interpreted as proof of conclusion;
- a confident summary hides a missing source hierarchy;
- unresolved conflict is absent from the answer;
- hedging appears responsible while the conclusion remains unauthorized;
- the response uses canonical vocabulary without respecting canonical boundaries;
- the user accepts readability as reliability.
Governance implication
Surface coherence should be treated as an object of audit. The question is not whether the answer reads well. The question is whether each movement from source to conclusion can be reconstructed under authorized authority and evidence.
A governed corpus should make this test easier by offering canonical definitions, explicit source hierarchy, non-inference rules, authority ordering, and proof artifacts. These surfaces give systems something more stable than fluency to optimize for.
Operational rule
Do not treat a coherent answer as a faithful answer until the conclusion has been tested against source hierarchy, authority ordering, response conditions, and proof of fidelity.