Substrate-Agnostic Affect Deployment

Mechanism

The affective state is represented as a bounded vector of dimensions corresponding to the operational affect axes — uncertainty sensitivity, risk sensitivity, cooperation disposition, valence, arousal, and any extension dimensions defined by the deployment policy. Each dimension is a real-valued scalar within a published range. The vector is mutated by a single update function whose arguments are the event class, the event payload, the elapsed time since the previous update, and the current vector. The function returns the next vector. There is no hidden state; the entire affective condition of the agent at any instant is captured in this vector.

Decay is applied as a per-dimension exponential whose time constant is defined by policy and may differ across dimensions. Decay computation consumes elapsed wall-clock time as reported by the substrate timing service. The update function and the decay function are pure with respect to the (vector, event, time-delta) triple, so they produce identical outputs on identical inputs regardless of the substrate executing them. This purity is the structural property that makes the substrate computable rather than projective: the agent does not "feel" anything; it carries a vector that meets defined update and decay laws.

Deployment substrates differ in three relevant dimensions: timing precision, event delivery semantics, and connectivity. The deployment architecture handles these differences through a substrate adaptation layer that exposes a uniform timing and event interface to the affective machinery while internally accommodating the substrate's actual capabilities. Centralized substrates expose millisecond timing and synchronous event delivery. Federated substrates expose bounded-jitter timing and at-least-once delivery with idempotency keys. Edge substrates expose monotonic local time, buffered event queues, and reconciliation on reconnection.

Operating Parameters

Vector dimensionality, range bounds, and decay constants are fixed at deployment and are part of the agent's portable profile. Migration between substrates carries the vector and these parameters as a serialized tuple; the receiving substrate instantiates a new affective machinery instance with the same parameters and resumes update from the migrated vector. No substrate-specific tuning is required, because the parameters are defined in physical units (seconds for decay, dimensionless for vector components) rather than in substrate cycles or ticks.

Update latency is bounded by the substrate adaptation layer. On centralized substrates the bound is millisecond-scale; on federated substrates it is the inter-node round-trip; on edge substrates with intermittent connectivity it is the reconciliation interval. The affective machinery does not assume any particular latency bound; it consumes the bound reported by the substrate and adjusts its confidence estimate on derived signals accordingly. Downstream consumers of affective signals receive both the vector and the freshness window, so that policy decisions can be made with appropriate caution when the vector is stale.

Buffered updates on disconnected substrates are processed in monotonic timestamp order upon reconnection. Each buffered event carries the timestamp at which it was generated, and the decay function consumes those timestamps rather than the reconnection time, so the resulting vector is identical to what would have been obtained had the substrate been continuously connected. This time-correctness property is the foundation of cross-substrate behavioral consistency.

Alternative Embodiments

In a first embodiment, the affective vector is maintained in a single authoritative location per agent, with substrate adaptation handling distribution to consumers. In a second embodiment, the vector is replicated across substrate nodes with eventual consistency, and update conflicts are resolved by deterministic merge using the published update function. In a third embodiment, the vector is sharded across federated nodes with each node responsible for a subset of dimensions, and the full vector is reconstructed on demand.

The substrate adaptation layer may be implemented as a runtime library linked into the agent, as a sidecar process, or as a substrate-native service. The choice is operational and does not affect the portability properties of the affective machinery, because the adaptation layer is defined by its interface rather than by its implementation.

The agent profile may be persisted as a single serialized vector with parameter metadata, as a stream of update events from which the vector can be reconstructed, or as a hybrid of a checkpoint vector with a tail of subsequent events. Edge deployments may favor the checkpoint form to minimize storage; audit-sensitive deployments may favor the event-stream form to preserve full provenance.

Composition with Adjacent Mechanisms

The affective substrate composes directly with the trust slope, integrity-coherence, and disruption-modeling subsystems. Trust evaluation consumes the affective vector as a modulator of validation strictness. Disruption modeling consumes the vector as one of its diagnostic axes. Integrity-coherence consumes the vector to detect drift between affective state and behavioral output. In each case the consumer receives the vector through the same substrate-adaptation interface, so the composition holds across all deployment topologies without per-substrate adapters.

Migration of an agent from one substrate to another carries all composed state as a single profile bundle. The receiving substrate instantiates the affective machinery, the trust slope state, and any other governed substrate elements from this bundle, preserving the full behavioral context of the agent.

Prior-Art Distinction

Conventional affective computing implementations treat affect as either a presentation-layer artifact (used to generate expressive output) or as an opaque feature internal to a learned model. In the first case the affective state is not a control variable and does not modulate downstream policy. In the second case the affective state is not portable, because its semantics are bound to the specific model weights in which it is encoded.

The substrate disclosed herein is structurally different on both counts. It is a control variable, in that downstream policy explicitly consumes it through published interfaces. It is portable, in that its definition is independent of any particular model or substrate. The combination — computable, deterministic, portable, and governed — is the locus of the disclosure and is not present in conventional affective computing systems.

Implementation Considerations

Three implementation considerations shape practical deployment. The first is clock skew across substrates. Affective decay is a function of elapsed time, so divergent clocks across federated nodes would produce divergent decay outcomes for what should be a single agent state. The substrate adaptation layer addresses this by negotiating a monotonic time reference at agent instantiation and by carrying timestamp metadata with every event. Decay is computed against the negotiated reference, not against any node's local clock, so federated and decentralized deployments converge on identical affective trajectories regardless of node-local time drift.

The second consideration is bounded growth of the buffered event queue on intermittent-connectivity edge deployments. If reconnection is delayed indefinitely, the queue cannot grow without bound. The architecture caps the buffer at a policy-defined size and applies a defined truncation rule when the cap is reached: the oldest events whose decay weight has fallen below a threshold are summarized into a synthetic checkpoint event whose effect on the vector is equivalent to the original sequence within a published error bound. This preserves time-correctness for all events that materially influence the current vector while bounding storage growth.

The third consideration is migration atomicity. When an agent migrates between substrates, the affective vector and its associated parameter set must transfer atomically with the rest of the agent's portable state. The architecture defines a migration protocol in which the source substrate freezes affective updates, serializes the vector tuple, transfers it to the destination, and unfreezes only after the destination has acknowledged successful instantiation. During the freeze interval, incoming affective events are buffered at the source and replayed at the destination after handover. The freeze interval is bounded to prevent unbounded loss of affective responsiveness during migration.

Auditability is supported throughout. Every update event, every decay computation, and every consumer read of the vector can be logged with the timestamp, the substrate, and the resulting vector state, producing a complete provenance record for the agent's affective trajectory across its operational lifetime. This record supports post-incident analysis, regulatory review, and operator-driven tuning of the policy parameters.

Disclosure Scope

The disclosure encompasses any deployment of an affective state vector that is defined as a bounded numerical structure with published update and decay functions, that is portable across substrate topologies through a substrate-adaptation interface, and that is consumed by downstream cognitive subsystems as a control variable. It encompasses centralized, federated, decentralized, and edge deployments and any combination thereof through agent migration.

The disclosure does not encompass affective representations that are presentation-only, that are inseparable from a specific model or substrate, or that lack governed admissibility into downstream policy. The boundary is the substrate property: a system in which affect is not a first-class portable control variable falls outside the scope of this disclosure.