Mechanism

The affective state field is the seventh structural field of the semantic agent schema, sitting alongside the intent field, context block, memory field, policy reference field, mutation descriptor field, and lineage field. It is a deterministic, policy-bounded data structure that encodes valence-weighted feedback derived from prior execution outcomes and environmental observations. It does not encode emotion in the phenomenological sense. It encodes a structured modulation vector that shapes how the agent weighs alternatives, tolerates ambiguity, persists under partial failure, and escalates under constraint pressure. Within that field, each named control field is governed by an emotional decay curve that determines how the field value returns toward its baseline in the absence of reinforcing stimuli.

The decay curve is a deterministic function of three inputs: the time elapsed since the most recent update, the magnitude of the current deviation from baseline, and the decay parameters specified by the agent's policy configuration. Because the function is deterministic, the affective trajectory of any agent is reproducible: given the recorded sequence of observations and the policy configuration, the field value at any past timestamp can be reconstructed by replaying the update and decay functions. The decay curve operates as one stage of the affective update pipeline, which proceeds from structured observations through the update function, policy bound enforcement, the decay curve, semantic hysteresis, entropy-governed stabilization, and lineage recording.

The Named Control Fields That Decay

The affective state field is organized as a structured modulation layer comprising a plurality of named control fields, each encoding a distinct dimension of the agent's dispositional orientation. The named control fields disclosed include an uncertainty sensitivity field, an ambiguity tolerance field, a novelty appetite field, a persistence-under-partial-failure field, an escalation-under-time-pressure field, a risk sensitivity field, a cooperation disposition field, and an attention sensitivity field. Each field governs a specific aspect of the agent's deliberation behavior with defined semantics, value ranges, update rules, and governance bounds.

Each named control field is represented as a tuple comprising a current magnitude value within a defined range, a decay rate governing how rapidly the field value returns toward a baseline in the absence of reinforcing stimuli, a policy-defined ceiling and floor bounding the permissible range of the field, and a timestamp recording the most recent update. The decay curve described in this article operates on that tuple: it reads the current magnitude, the deviation from baseline, the field-specific decay parameter, and the elapsed time since the timestamp, and returns the field's effective value at the present moment.

Exponential Decay Toward Baseline

In an embodiment, the decay function is implemented as an exponential decay with a configurable time constant. The field value at time t after the most recent reinforcing update is computed as the baseline value plus the difference between the value immediately after the most recent update and the baseline, multiplied by the exponential of the negative elapsed time divided by the decay time constant. The baseline is the policy-defined resting value for the field, and the time constant is specified by the policy configuration. In the absence of new reinforcing stimuli, the field relaxes back toward its resting value along this curve.

Different named control fields may have different decay time constants, reflecting the architectural principle that some modulation dimensions are more persistent than others. The disclosure gives uncertainty sensitivity as an example of a field that may decay rapidly, because epistemic conditions change frequently, and persistence-under-partial-failure as an example of a field that may decay slowly, because learned persistence reflects deeper accumulated experience. The specific time constants are not fixed by the architecture: they are policy configuration. The disclosure does not assign numeric values to any time constant, and the relationship between fields is one the policy author selects within governance bounds.

Semantic Hysteresis and Asymmetric Update

The affective modulation layer exhibits semantic hysteresis: the agent's current affective state depends not only on the current structured observations but also on the trajectory of prior states. Hysteresis is implemented through asymmetric update rules. The rate at which a named control field increases in response to a triggering observation may differ from the rate at which it decreases when the triggering condition is removed.

In an embodiment, negative valence updates, meaning updates driven by failure, uncertainty, or threat, apply at a higher rate than positive valence updates driven by success or stability. This asymmetry reflects the architectural principle that an agent should respond more rapidly to deteriorating conditions than it recovers from them, producing a built-in caution bias. The asymmetry is in the update rates, not a separate enter-and-exit threshold pair: the field rises quickly under adverse observation and relaxes more gradually toward baseline once the adverse condition is removed.

Entropy-Governed Valence Stabilization

Entropy-governed valence stabilization is applied to prevent oscillatory affective behavior. When the agent's affective state field exhibits rapid alternation between elevated and suppressed values on any named control field, detected by monitoring the frequency and direction of recent updates, the stabilization mechanism progressively increases the effective decay time constant, damping the oscillation. The stabilization threshold and damping factor are policy-configurable. This mechanism prevents affective instability that could arise from noisy environmental inputs or from rapid alternation between success and failure conditions, so that the decay curve does not become a vehicle for jitter when observations themselves are unstable.

Policy-Bounded Decay

The decay parameters are themselves governed. The policy reference field specifies, for each named control field, constraints that govern how the field may be updated and how it decays. These include range bounds (a minimum and maximum permissible value, beyond which computed values are clamped), rate limits (a maximum magnitude of change per update cycle), admissible triggers (the observation types permitted to drive updates to a given field), update authority (which entities may initiate updates), and decay governance.

Decay governance comprises policy constraints on the decay parameters for each named control field, including minimum and maximum decay rates and whether decay is permitted to proceed below the field's baseline value. The disclosure notes that this prevents adversarial suppression of affective response through artificially accelerated decay: an attacker cannot simply drive a field's time constant toward zero to erase a caution signal, because the minimum decay rate is a policy floor. Every decay parameter, like every value, sits inside the policy-defined operating envelope, and every mutation to the field is recorded in the agent's lineage.

What the Decay Curve Feeds

The decayed field values are consumed downstream. The affective state field modulates enumerated targets in the agent's deliberation and execution pipeline, including promotion thresholds, search breadth, branch growth rates, decay rates for unpromoted candidates, escalation thresholds, persistence parameters, delegation routing preferences, and mutation acceptance thresholds. As the named control fields relax toward baseline, the parameters they modulate relax with them: an agent whose risk sensitivity has decayed since an adverse event gradually lowers its promotion and mutation acceptance thresholds back toward their resting values.

The decay curve also participates in cross-primitive integration. The affective state modulates the rate at which confidence decays and recovers, as described in the confidence chapter, and modulates planning graph construction parameters in the forecasting engine. The disclosure maintains a strict separation of concerns throughout: the affective state field modulates how the agent deliberates, but it is not an input to the governance gate and cannot create authority, validate truth claims, relax policy bounds, or alter trust slope validation. The decay curve shapes disposition, not admissibility.

Disclosure Scope

The emotional decay curve, comprising the exponential decay of each named control field toward its policy-defined baseline with a field-specific time constant, the semantic hysteresis implemented through asymmetric update rules under which negative valence updates apply at a higher rate than positive valence updates to produce a built-in caution bias, the entropy-governed valence stabilization that increases the effective time constant to damp oscillation, and the decay governance that bounds the decay parameters within policy, is disclosed in the cognition filing (U.S. Application No. 19/647,395 and its international counterpart). This article describes that disclosed mechanism. The scope extends to embodiments in which different named control fields carry different time constants, to alternative decay parameterizations selected by policy configuration, and to the consumption of decayed field values by the promotion, search, escalation, confidence, and forecasting targets the modulation layer governs, provided the decay remains deterministic, policy-bounded, asymmetric in its update rates, and recorded in the agent's lineage.