The Cross-Primitive Coherence Engine

Mechanism

The cross-primitive coherence engine operates as a synchronous arbiter between the agent's cognitive domain fields and its mutation lifecycle. At each lifecycle stage, the engine queries the active fields for evaluations of the pending mutation, assembles the evaluation tuple, and applies a configured set of consistency rules. The rules specify which field combinations must agree, which may disagree within bounded tolerances, and which combinations constitute structurally prohibited contradictions that block lifecycle advancement.

The engine is composed around what this disclosure terms the coherence trifecta: affect, integrity, and forecast, supplemented by the lineage record that anchors all three to a verifiable history. Affect contributes the agent's subjective valuation of the proposed mutation, including urgency, confidence, and empathy weighting. Integrity contributes the agent's structural self-assessment, including capability bounds and policy compliance. Forecast contributes the projected outcome distribution and the confidence intervals around that projection. Lineage contributes the historical record that the engine consults to detect drift, regression, or contradiction with prior commitments.

When the consistency rules detect an inconsistency, the engine does not silently override any field. Instead, it initiates a deterministic reconciliation procedure. Reconciliation may take three forms: input adjustment, in which the engine requests that one or more fields re-evaluate against refined inputs; supplementary evaluation, in which a dormant field is activated to provide additional evidence; and escalation, in which the inconsistency is flagged to a higher-level resolution path that may invoke policy review or human oversight. Each reconciliation event is recorded in the lineage, producing an auditable trace of how the contradiction was resolved.

The engine's structural commitment is that the mutation lifecycle cannot advance past a stage at which an unresolved inconsistency exists. This is the mechanism by which coherence is enforced rather than merely encouraged. An agent governed by the coherence engine cannot, as a structural matter, proceed with execution while believing the execution is unsafe; the contradiction blocks the lifecycle until reconciliation completes.

The engine is positioned at the boundary between the agent's cognitive primitives and its mutation lifecycle, and it is invariant under the specific identity of the cognitive primitives so long as those primitives expose the standard evaluation interface. New primitives may be added, existing primitives may be deprecated, and the engine's structural behavior is unchanged. This invariance is what permits the engine to serve as the integration point for the broader cognitive architecture: it is the locus at which the primitives become a coherent system.

Operating Parameters

Consistency rules are expressed as predicates over the evaluation tuple and are configured per deployment. Typical rules include integrity-confidence agreement bounds, in which the agent's confidence in a mutation may not exceed its integrity-derived capability assessment by more than a configured margin; affect-forecast alignment, in which the affective valuation of an outcome must lie within the forecast distribution's plausible range; and lineage-monotonicity, in which the present evaluation must be consistent with the agent's prior commitments unless an explicit revision event is recorded.

The reconciliation procedure operates under a bounded budget. Each reconciliation attempt is limited in cycles, evaluation depth, and policy invocations, and exhaustion of the budget triggers escalation to higher-level resolution. The engine records reconciliation latency, success rates, and escalation frequency as integrity-relevant metrics that feed back into the integrity field's self-assessment of the agent.

Field evaluations carry confidence intervals as well as point estimates, and the consistency rules are evaluated against the intervals. Two fields are considered consistent when their confidence intervals overlap within the configured tolerance, even if their point estimates differ. This permits the engine to tolerate principled disagreement among fields while still detecting structural contradiction.

The engine maintains a per-stage evaluation cache so that fields are not re-queried within a single lifecycle stage unless their inputs have demonstrably changed. Cache invalidation is keyed on the input hashes of each field, ensuring that a field whose inputs are stable contributes a stable evaluation throughout the stage. This caching is essential to the engine's structural guarantee: without it, the same lifecycle stage could observe two different evaluations of the same field and produce a spurious inconsistency report.

Alternative Embodiments

The engine may be embodied as a synchronous in-process arbiter, as an out-of-process service that all field evaluations route through, or as a distributed quorum protocol among independently hosted field evaluators. The consistency rules may be expressed as static configuration, as a learned model, or as a hybrid in which a static core is augmented by learned tolerance bounds.

The set of active fields is configurable. A minimal embodiment activates only affect, integrity, and forecast. Extended embodiments add temporal cognition, empathy, capability projection, and domain-specific evaluators. The engine's structural behavior is invariant under the field set: it enforces consistency over whatever fields are active at the present lifecycle stage.

Reconciliation strategies may be embodied as deterministic rule cascades, as priority-ordered field overrides, or as negotiation protocols in which fields exchange refined evaluations until consensus is reached. The disclosure encompasses all reconciliation strategies that produce a deterministic, lineage-recorded resolution of detected inconsistencies.

Composition with the Coherence Trifecta

The coherence engine is the structural mechanism that makes the trifecta of affect, integrity, and forecast operate as a unified cognitive system rather than as three independent evaluators. Without the engine, the trifecta is merely three fields that happen to coexist on the same agent. With the engine, the trifecta becomes a coherent cognitive substrate in which affective valuation is constrained by integrity-derived capability, integrity is constrained by forecasted outcomes, and forecasts are anchored by affective priors and integrity bounds.

The lineage field plays a distinct compositional role: it is not a participant in moment-to-moment consistency checks but the persistent record against which the trifecta's evaluations are validated for temporal coherence. An agent whose present evaluation contradicts its lineage without an explicit revision event is, by the engine's rules, in an inconsistent state, and the engine will block lifecycle advancement until the contradiction is reconciled or the lineage is explicitly amended.

This composition is what produces human-relatable behavior. A human reasoner does not separately consult independent faculties of feeling, judgment, and prediction; the faculties are continuously reconciled, and the reconciliation is what observers experience as coherent personhood. The engine reproduces that structural property in the artificial agent.

The composition further admits a recursive property: the engine itself participates in the integrity field's self-assessment. An engine that is reconciling frequently, escalating often, or operating near its budget bounds is itself a signal that the agent's cognitive state is under stress, and that signal is reflected back into integrity. An agent whose coherence engine has been quietly absorbing contradictions is structurally distinguishable from an agent whose engine has been idle, even if both agents present the same surface behavior. This recursive observability is what makes the engine an instrument of trust rather than merely an instrument of decision-making.

Finally, the composition enables a principled treatment of revision. When an agent must reverse a prior commitment, the revision is not a silent overwrite of lineage; it is a structurally recorded reconciliation event in which the present trifecta evaluation is made consistent with a corrected lineage entry. The engine ensures that revision proceeds through the same machinery as ordinary mutation, preserving the auditability of the agent's cognitive history even as that history is amended.

Prior-Art Distinction

Multi-criteria decision systems aggregate independent evaluations into a single score, but they do not enforce structural consistency among the evaluators and do not record reconciliation events in an auditable lineage. Ensemble methods in machine learning combine model outputs by voting or averaging, treating disagreement as noise rather than as a structural signal that must be reconciled. Constraint-satisfaction systems enforce hard rules over decision variables but do not operate over confidence-bearing evaluations from cognitively distinct fields. The coherence engine is distinguished by the combination of cross-primitive scope, structural blocking of lifecycle advancement under unresolved inconsistency, and lineage-recorded reconciliation.

Disclosure Scope

This disclosure covers the synchronous arbitration of cognitive domain field evaluations at each mutation lifecycle stage; the consistency rules and their evaluation against confidence-bearing intervals; the reconciliation procedure and its three forms of input adjustment, supplementary evaluation, and escalation; the lineage-recorded auditability of reconciliation events; and the composition of the engine with the affect-integrity-forecast trifecta and the lineage record. The scope encompasses any embodiment in which cross-primitive consistency is structurally enforced as a precondition for mutation lifecycle advancement.