Affect-Modulated Inference Integration

Mechanism

Inference integration sits between the inference adapter that returns LLM-generated candidate mutations and the validator that converts admitted candidates into committed state changes. Each candidate carries a proposal-type tag, a target scope, and a confidence or score returned by the model. Before the validator is invoked, the affect-modulation stage retrieves the current affective field from the agent's state container and computes a per-proposal threshold modifier as a weighted combination of relevant dimensions.

The weighting is policy-defined per proposal type. For mutations classified as risk-bearing, such as those that touch external systems, mutate persistent state, or invoke privileged tools, the weighting elevates the contribution of the agent's risk-sensitivity dimension and any frustration or distress dimensions, raising the acceptance threshold so that a more cautious agent demands a higher proposal score. For mutations classified as exploratory, such as hypothesis generation or speculative branch creation, the weighting elevates the contribution of the agent's novelty-appetite dimension, lowering the threshold so that a curious agent admits more diverse candidates. The threshold modifier is bounded by the policy reference to prevent any single affective state from gating the agent into total paralysis or total permissiveness.

Candidates whose model-reported score falls below the modulated threshold are not silently dropped. They are routed into one of three terminal states. Rejected candidates are written to the lineage with a rejection rationale that names the affective dimensions and weights that contributed to rejection, supporting downstream interpretability. Queued candidates are deferred for re-evaluation when the affective field has changed by more than a configurable delta or when a configurable time has elapsed, modeling the realistic case in which a proposal that is wrong now may be right when the agent has stabilized. Accepted candidates pass to the validator unchanged in their content, with an annotation recording the affective state at the moment of acceptance.

The affective state is read-only to the inference adapter. The adapter receives the candidate and the modulated threshold's effect on it, but never the affective field itself, and the adapter has no path by which to write affective updates. This isolation is enforced architecturally by the state container's access control, not by convention, and it is what permits affect to act as a trustworthy gate on model output rather than as a parameter that the model can learn to optimize against.

Operating Parameters

Threshold modifiers are expressed as additive offsets to a baseline acceptance score in the interval from negative one half to positive one half of the score range. Per-dimension weights are typically between zero and 0.3 in absolute value, with the sum of absolute weights for any single proposal type bounded by 0.5 to ensure that affective modulation never exceeds half the available threshold range. Queue residency limits are configurable, with typical values of thirty seconds to ten minutes for short-cycle agents and hours to days for long-running deliberative agents.

Re-evaluation triggers fire when any weighted dimension changes by more than a delta of 0.1 of its full range, when the queue residency timer expires, or when an explicit re-eval signal is delivered through the agent's control plane. The number of re-evaluations per candidate is capped, typically at three, after which the candidate is finalized as rejected with an exhausted-retries rationale.

Mechanism, Continued

The modulator is implemented as a pure function of the affective field, the proposal-type tag, and the policy reference, with no side effects on the affective state itself. This purity is essential: were the modulator to write back to affect, the inference path would acquire a hidden control loop in which proposal volume could amplify or dampen the very state that gates it. Affective updates are produced only by the agent's experience pipeline, in response to outcomes, environmental signals, and biological coupling, never as a byproduct of inference admission. The result is a clean separation between the experiential layer that maintains affect and the inferential layer that produces candidate mutations.

The proposal-type taxonomy is itself a versioned policy artifact. Each candidate emitted by an inference adapter carries a type tag drawn from the active taxonomy, and the modulator's per-type weight table is keyed on that taxonomy. When the taxonomy is revised, for example to subdivide a previously coarse type into finer-grained categories, the policy publisher must supply a migration mapping that preserves continuity of behavior across the version boundary. This discipline prevents silent behavioral regressions when proposal classification is refactored.

The queue that holds deferred candidates is bounded in size and ordered by the difference between the candidate's score and the threshold at the time of deferral. When the queue is full and a new candidate is to be deferred, the lowest-priority entry is finalized as rejected with an admission-pressure rationale. This admission-pressure rationale is itself a structured lineage event that operators can use to detect when queue capacity is the binding constraint, indicating that the affective regime is too restrictive for the proposal volume.

Alternative Embodiments

In a single-threshold embodiment, all proposal types share a common modulator and the policy distinguishes types only through differing weights. In a per-tool embodiment, each tool the agent can invoke carries its own modulation profile, so that a database-write tool receives heavier risk-sensitivity weighting than a read-only search tool. In a multi-stage pipeline embodiment, modulation is applied at multiple points: a first pass against a coarse threshold filters obvious mismatches before validation expense is incurred, and a second pass against a fine-grained threshold operates on validated candidates to control commit rate.

In an ensemble-inference embodiment, several inference adapters return candidates concurrently and modulation acts as a cross-adapter selector, preferring candidates from the adapter whose historical acceptance pattern best matches the current affective regime. In a streaming embodiment, modulation is applied incrementally as tokens arrive, allowing a high-risk-sensitivity state to terminate generation early when a candidate's developing structure indicates likely rejection.

Parameter Tuning and Calibration

Calibration of the per-dimension weights proceeds through a combination of policy-author intent and empirical observation. The policy author begins with a declarative statement of behavioral intent, expressed as a set of canonical scenarios with desired admission outcomes. These canonical scenarios are converted into weight constraints by a calibration tool that solves for the weight vector minimizing the deviation between desired and predicted outcomes across the scenario set. The resulting weights become the starting point for the deployed policy.

Once deployed, the lineage subsystem accumulates a record of actual modulation decisions that operators can compare against the canonical scenarios to detect drift. When drift exceeds a configurable tolerance, a re-calibration cycle is initiated, producing a new weight vector that is published as a successor policy version. This continuous-calibration discipline prevents the modulator from accumulating subtle behavioral mismatches that would otherwise erode trust in the gating function.

Composition

Inference integration composes with the inference-control mechanism, which performs pre-execution policy resolution to determine which inference adapters, tools, and proposal types are admissible at all in the current context. Inference-control runs before affect modulation: it filters the universe of possible proposals down to a policy-admissible subset, and affect modulation then gates that subset against the agent's experiential state. The two together implement a layered admissions process in which structural policy and experiential policy operate at different timescales but through compatible interfaces.

Inference integration also composes with the lineage and audit subsystems, since every modulated decision writes a structured record naming the proposal, the affective field at decision time, the weights applied, and the resulting outcome. This record supports post-hoc analysis of how affect shaped agent behavior across a workflow and supports policy iteration by exposing the empirical distribution of modulator effects.

Prior-Art Distinction

Prior LLM-integration architectures have introduced confidence thresholds, risk-aware filters, and rejection cascades, but these mechanisms are typically static or are tuned through offline reinforcement learning rather than driven by an online, structured affective state. Some prior work conditions LLM prompts on an affect token, but this places affect inside the inference call, exposing it to manipulation by the model and entangling it with the model's representational space. The disclosed mechanism keeps affect outside inference and uses it as a downstream gate, which is structurally distinct.

The disclosed mechanism is also distinct in routing rejected candidates through a queue with explicit re-evaluation triggers, in attaching affective rationale to each rejection in lineage, and in enforcing a one-way information flow from affect to inference admission rather than a bidirectional coupling.

Failure Modes and Mitigations

A first failure mode is over-restriction, in which an agent in a sustained high-risk-sensitivity regime rejects so many proposals that progress halts. The mitigation is the policy-bounded threshold modifier, which caps the maximum upward shift, combined with a minimum-admission floor that guarantees a configurable fraction of high-confidence proposals will be accepted regardless of affective state. A second failure mode is calibration drift, in which the empirical distribution of proposal scores shifts away from the distribution against which the modulator was calibrated, causing the threshold modifier to act in unintended ways. The continuous-calibration monitor described above detects drift and triggers re-calibration before user-visible behavior changes substantially.

A third failure mode is queue exhaustion under proposal flood, in which a high-volume inference adapter generates more candidates than the queue can hold, leading to admission-pressure rejections. The mitigation combines back-pressure to the inference adapter, so that the adapter slows its generation rate when the queue saturates, with rate-shaping at the inference-control stage so that flood conditions are detected before they reach the modulator. A fourth failure mode is adapter compromise, in which a malicious or malfunctioning adapter emits proposals whose model-reported scores are unreliable. The mitigation here is independent score validation by the validator stage, which can downweight or reject candidates from adapters whose historical score-to-outcome correlation has degraded below a configurable floor.

Disclosure Scope

This disclosure covers any agent architecture in which a structured affective state, maintained outside the inference call, modulates the acceptance, rejection, or queueing of candidate mutations produced by an inference adapter, where the modulation is governed by a versioned policy reference and the resulting decisions are recorded in lineage. The scope is independent of the underlying model class, the proposal grammar, the validation framework, and the deployment domain, and it includes both single-agent and multi-agent embodiments.