Affect-Modulated Inference Integration

Mechanism

Affect-modulated inference integration governs how an agent whose schema includes the affective state field evaluates mutations proposed by a large language model or other probabilistic inference engine. It extends the architecture in which the inference engine acts as a stateless mutator: the engine proposes candidate mutations, and the agent, not the engine, decides whether each proposal is accepted, rejected, or queued for retry. The affective state field is the seventh field of the semantic agent schema, a deterministic, policy-bounded modulation layer of named control fields including uncertainty sensitivity, risk sensitivity, novelty appetite, persistence-under-partial-failure, and escalation-under-time-pressure. Inference integration is the point at which those control fields shape the agent's response to model output.

The integration operates through four stages: mutation acceptance threshold modulation, retry strategy modulation, inference context conditioning, and multi-engine arbitration modulation. Each stage reads the agent's current affective state and adjusts a defined parameter of the mutation evaluation pipeline within policy bounds. The affective state does not authorize new actions or bypass governance; it modulates the quantitative parameters that shape how the existing, authorized mutation pipeline executes.

Mutation Acceptance Threshold Modulation

The mutation acceptance threshold is the minimum validation score a proposed mutation must achieve to be accepted. The agent's risk sensitivity and uncertainty sensitivity fields modulate this threshold. When these fields are elevated, the acceptance threshold is higher, so that only high-confidence, well-validated mutations are accepted. When these fields are suppressed, the threshold is lower, permitting the agent to incorporate a broader range of proposed mutations. This modulation applies uniformly to all proposed mutations regardless of which inference engine produced them.

The modulation is bounded. As described for the affective modulation layer generally, the affective state cannot relax policy bounds: even when the agent's modulation state would favor more permissive incorporation, policy-imposed ceilings on the threshold remain in force. Affect adjusts the stringency of the validation gate within the policy-defined operating envelope; it does not replace the gate or override the governance determination of whether the mutation is admissible.

Retry Strategy Modulation

When a proposed mutation is rejected by the validation engine, the agent must decide among three responses: request a new proposal from the inference engine (retry), accept the rejection and proceed without the mutation (abandon), or escalate the need to a human operator or higher-authority agent (escalate). The agent's persistence-under-partial-failure and escalation-under-time-pressure fields modulate this determination.

Elevated persistence causes the agent to favor retry, issuing a refined prompt to the inference engine that incorporates the rejection reason. Elevated escalation tendency causes the agent to favor escalation. Low persistence combined with low escalation tendency causes the agent to favor abandonment. The retry path is the mechanism by which the rejection reason re-enters inference: rather than discarding a rejected proposal silently, a persistent agent feeds the reason back to the stateless engine to obtain a corrected proposal.

Inference Context Conditioning

When the agent issues a mutation request to the inference engine, the agent's current affective state summary is included as a conditioning parameter in the request. The affective state summary is a compact representation of the agent's current modulation configuration that informs the engine of the agent's operational disposition. This conditioning enables the engine to tailor its proposals to the agent's state, for example producing more conservative proposals when risk sensitivity is elevated, or more exploratory proposals when novelty appetite is elevated.

The inference engine remains stateless. The affective conditioning is supplied as input with each request and is not retained by the engine between requests. The agent, which holds the persistent affective state, is the sole locus of memory across requests; the engine receives a snapshot of disposition and returns proposals conditioned on it, without accumulating state of its own.

Multi-Engine Arbitration Modulation

When multiple inference engines provide competing mutation proposals, an arbitration engine selects among them, and the agent's affective state modulates the arbitration weights. Elevated risk sensitivity increases the weight assigned to safety-oriented inference engines relative to creativity-oriented engines. Elevated novelty appetite increases the weight assigned to engines that produce novel or unconventional proposals.

This modulation is policy-bounded. The minimum and maximum weights for each inference engine are specified by the policy configuration and cannot be exceeded regardless of the agent's affective state. Affect shifts the arbitration toward engines whose output character matches the agent's current disposition, but it operates only within the policy-defined band of permissible weights for each engine.

Separation From Governance

Across all four stages, a strict separation of concerns holds between affective modulation and governance. The affective state field cannot create authority the agent does not possess, cannot bypass policy constraints, and cannot validate truth claims. It modulates how the agent evaluates proposed mutations, not whether the agent is permitted to act on an admitted mutation. Even when the agent's modulation state produces maximal confidence disposition and minimal risk sensitivity, the governance gate independently determines whether the resulting action satisfies all policy requirements, and the affective state is not an input to that gate.

This is what permits affect to act as a trustworthy modulator of the inference pipeline. Affective updates are produced by the agent's structured observations of execution outcomes and environment, recorded in lineage as policy-governed mutations, and bounded by the range limits, rate limits, and admissible-trigger constraints of the update mechanism. The inference engine receives a conditioning summary but has no path to write the agent's affective state, which keeps disposition outside the engine's representational space.

Lineage and Auditability

Because every affective mutation is recorded in lineage as a policy-bounded state change, and because the affective update function is deterministic, the modulation configuration that governed any given inference decision is reconstructable after the fact. Replaying the deterministic update function over the recorded sequence of observations reproduces the exact affective state vector that existed when a mutation was accepted, rejected, or queued for retry. This supports post-hoc review of how disposition shaped the agent's response to model output, without requiring persistent storage of moment-to-moment affective values, and it preserves the governance properties that apply to all other agent fields.

Disclosure Scope

Affect-modulated inference integration, comprising mutation acceptance threshold modulation by risk sensitivity and uncertainty sensitivity, retry strategy modulation among retry, abandon, and escalate by persistence-under-partial-failure and escalation-under-time-pressure, inference context conditioning that supplies the agent's affective state summary to a stateless inference engine with each request, and multi-engine arbitration weight modulation bounded by policy, is disclosed in the cognition filing (U.S. Application No. 19/647,395 and its international counterpart) at Chapter 2, Section 2.15, building on the mutation acceptance threshold target of Section 2.3, the policy-bounded update mechanism of Section 2.6, and the stateless-mutator inference architecture cross-referenced from Chapter 7. This article describes that disclosed mechanism. The scope is independent of the underlying inference engine class and extends to single-engine and multi-engine embodiments, provided the affective state modulates mutation evaluation while remaining outside the inference call and subordinate to the governance gate.