Attention Field

Mechanism

The attention field is implemented as a bounded scalar-and-vector composite maintained within the agent's cognitive substrate. The scalar component represents total available attention capacity for the current cycle, and the vector component represents the distribution of that capacity across candidate input domains, mutation classes, and operator surfaces. At each evaluation tick, the field is recomputed from a deterministic update function whose inputs include the prior-cycle attention vector, the integrity deviation signal, the affective stress estimate, the resource-budget envelope, and the operator-state descriptor.

The update function applies a structured decay term to every dimension of the prior attention vector. Decay is not uniform: dimensions associated with high integrity deviation decay more slowly, preserving attention on domains where coherence risk is elevated; dimensions associated with low salience decay more quickly, freeing capacity for newly arriving inputs. Following decay, the function applies a governance-adjusted reallocation step, in which policy-declared weights modulate how freed capacity is redistributed. The reallocation step is bounded by capacity invariants that prevent any single dimension from monopolizing the field.

Inputs that arrive during a cycle are evaluated against the current attention vector. An input whose dimension exceeds the active threshold is admitted to elevated processing, meaning that downstream cognition primitives — confidence governance, mutation generation, integrity evaluation — consult it at full resolution. An input whose dimension falls below the threshold is enqueued in the deferred-consultation buffer, tagged with the cycle index, the threshold value at admission decision, and the policy clause that determined the threshold. The buffer is bounded, governed by an explicit eviction policy, and its contents are visible to audit.

Crucially, queued inputs are not silently dropped. Each cycle, the deferred-consultation buffer is re-evaluated against the updated attention vector. Inputs whose relevance has risen — because the field has shifted toward their dimension, or because integrity deviation has surfaced their domain — are promoted to elevated processing in subsequent cycles. The promotion event is recorded in lineage, including the cycle index at which the input was first observed and the cycle index at which it was admitted, providing a complete temporal audit of attention allocation.

Operating Parameters

The attention field exposes several policy-governed parameters. The capacity scalar sets the maximum total attention available per cycle and is typically tuned to match the substrate's per-cycle compute budget. The decay coefficient governs how rapidly attention dimensions relax toward zero in the absence of reinforcing signals; values near unity produce sticky attention, while smaller values produce rapidly refreshing attention. The integrity-coupling weight controls the degree to which integrity deviation accelerates or retards decay on coupled dimensions, and is typically set higher in safety-critical deployments.

The affective-stress modulation parameter determines how operator or system stress estimates compress the attention vector. Under elevated stress, the field narrows, concentrating capacity on a smaller number of dimensions deemed most relevant by policy. Under low stress, the field broadens, permitting exploratory consultation of additional domains. The deferred-buffer depth and eviction policy bound the long-term memory of the queue, with eviction recorded under provenance so that no input is removed without trace.

Cycle period — the interval at which the field is recomputed — is itself a policy parameter, and may be governed by either wall-clock cadence or event-driven triggers. The choice between cadences is recorded, and a single agent may operate under hybrid cadence in which baseline ticks are interleaved with event-driven recomputations. All thresholds are hysteretic: admission and demotion thresholds differ by a configurable margin to prevent oscillatory churn at the boundary.

Alternative Embodiments

In a first embodiment, the attention field is realized as a dense vector across a fixed schema of cognitive domains, suitable for agents whose input space is known in advance. In a second embodiment, the field is realized as a sparse map over a dynamic key space, suitable for agents that encounter previously unseen domains and must extend the field at runtime. In both embodiments the update function, decay behavior, and governance coupling are structurally identical; only the representation of the vector differs.

A third embodiment couples the attention field to an external operator interface, exposing the current vector as a read-only telemetry stream. Operators may inspect, but not directly mutate, the field; mutation is reserved to policy. A fourth embodiment supports federated attention, in which multiple agents share a coordinated attention vector through a consensus protocol, useful in multi-agent deployments where overlapping consultation must be avoided. A fifth embodiment substitutes a stochastic decay term for the deterministic decay term, in deployments where deliberate attention diversification is required; the stochastic term remains seeded and reproducible under lineage.

Composition

The attention field composes with confidence governance through a defined coupling: the active attention vector determines which inputs feed the confidence computation, and the resulting confidence value modulates subsequent attention reallocation. Low confidence narrows the field, concentrating consultation on the dimensions most likely to resolve uncertainty; high confidence broadens it, permitting exploratory cognition. The coupling is bidirectional but bounded, governed by explicit policy clauses that prevent runaway feedback.

The field also composes with the integrity evaluator, which can elevate attention on dimensions exhibiting coherence drift, and with the discovery traversal primitive, which consults the field to determine which branches of the search frontier receive elevated expansion. Composition with the audit subsystem is structural: every attention vector, every admission decision, every deferred-buffer promotion is written to lineage with cycle index, threshold values, and governing policy clause, producing a complete reconstruction of attention dynamics across the agent's operational history.

Implementation Considerations

A reference implementation maintains the attention field as an immutable record per cycle, with each new cycle producing a successor record linked to its predecessor through the cycle index. Immutability of historical records is enforced at the storage layer, ensuring that audits performed weeks or months after the fact reconstruct the exact field that governed each prior decision. The successor relation is a directed acyclic chain, and may be exported as a self-contained provenance document for regulatory submission.

Numeric reproducibility deserves explicit attention. The decay term, the reallocation step, and the threshold comparisons are all specified to operate under a declared floating-point format with ordered accumulation. Implementations that would otherwise rely on parallel or vectorized arithmetic must serialize accumulation order or use deterministic reduction primitives, so that a re-run on a different substrate produces identical attention vectors. Where reproducibility cannot be guaranteed at single-bit granularity — for instance, in mixed-precision accelerators — the implementation declares a tolerance bound, and that bound is itself recorded in lineage.

Performance considerations bound the practical capacity scalar. In the dense-vector embodiment, update cost scales linearly with schema size, and the cycle period must accommodate this cost within the deployment's latency envelope. In the sparse-map embodiment, update cost scales with the number of active dimensions, permitting much larger nominal schemas at the cost of variable per-cycle latency. The choice between embodiments is a deployment decision, recorded in policy and in the bootstrap lineage of each agent instance.

Failure handling is also structural. If a cycle's update function cannot complete within its allotted window, the prior cycle's attention vector persists, and the missed-cycle event is recorded under provenance. Downstream primitives consulting the field are notified of staleness through a freshness flag carried alongside the vector. Repeated staleness is itself an integrity-deviation signal and may trigger predictive alerting in deployments that compose with that primitive.

Distinction Over Prior Art

Prior approaches to selective processing in agent systems generally fall into three categories: hard input filtering, attention as a learned weighting in neural architectures, and priority-queue scheduling. Hard input filtering discards inputs that do not match a static rule, providing no mechanism for re-evaluation as conditions change. Learned-attention weightings within neural architectures are not policy-governed; their weights cannot be inspected, certified, or modulated by external governance, and they offer no audit trail. Priority-queue scheduling provides ordering but not bounded capacity, and does not couple to integrity, stress, or operator state.

The attention field disclosed here is structurally distinct: it is bounded, decaying, governance-adjusted, and auditable. Inputs are not discarded but queued, and queued inputs are continuously re-evaluated against an evolving field. Every parameter is declared in policy, every state transition is recorded in lineage, and every coupling to other cognition primitives is explicit. The result is a selective-processing mechanism that is amenable to formal analysis and regulatory certification, properties that prior approaches do not provide.

Deployment Illustrations

In an autonomous-control deployment, the attention field is dimensioned over sensor modalities, control surfaces, and operator-state estimators. Under nominal conditions, attention is broadly distributed; under integrity deviation — for example, a sensor whose readings diverge from the forecasted envelope — the field narrows onto the deviating modality, ensuring that subsequent cycles consult that modality at full resolution. Inputs from non-deviating modalities are queued for deferred consultation rather than discarded, and their re-evaluation in subsequent cycles preserves the agent's full sensory awareness even while attention is concentrated.

In a decision-support deployment, the field is dimensioned over evidence sources, normative frameworks, and operator-supplied context. Stress estimates derived from operator interaction patterns modulate field width, narrowing consultation under high stress and broadening it under low stress. The deferred-consultation buffer ensures that evidence sources not consulted in the current cycle are not forgotten; their re-evaluation under subsequent cycles preserves the completeness of the deliberative record. Auditors reviewing a decision can reconstruct exactly which sources were consulted, which were deferred, and why.

In a federated multi-agent deployment, attention vectors are coordinated through a consensus protocol that prevents redundant consultation while preserving the independence of each agent's decision authority. Each agent's vector is independently audited, and the consensus protocol's transcript is itself recorded under provenance. Operators reviewing the federation can reconstruct the full attention dynamics across the agent population, including which agent attended to which inputs at which cycles, and how the federation as a whole allocated its collective attention budget.

Disclosure Scope

This disclosure describes the attention field as a structural primitive of the cognition patent, including its update function, decay behavior, governance coupling, deferred-consultation buffer, and audit semantics. The disclosure encompasses dense and sparse representations, deterministic and stochastic decay variants, single-agent and federated embodiments, and the full set of policy-governed parameters that tune field dynamics. It also encompasses the bidirectional couplings to confidence governance, integrity evaluation, discovery traversal, and the audit subsystem.

The scope extends to deployments in which the field operates over arbitrary input spaces, including but not limited to autonomous control, decision-support, therapeutic agents, and enterprise cognition systems. The disclosure is not limited by the specific schema of cognitive domains employed by any particular deployment; the structural mechanism — bounded capacity, governed decay, queued deferral, recorded promotion — is invariant across deployments and constitutes the claimed subject matter.