Mechanism

The distribution mechanism replaces the central distribution server with mesh-distributed propagation: skill-adapter updates per Chapter 12 are distributed to devices participating in the governed mesh protocol through the governed mesh itself, without reliance on an out-of-band distribution channel. An artifact in this context is an adaptation artifact per Chapter 8, a deployable unit of skill carrying its governance credentials, its training-data provenance, and the governance scopes under which it may be admitted.

Propagation proceeds as a governed observation. A deploying authority publishes the update as an observation carrying the version, the content, the deploying authority's signature, and an applicability-scope specification identifying the devices eligible to receive it. That observation propagates through the governed mesh by Section 2.6 multi-hop relay and by Section 2.7 mobile store-and-forward carriage. The mechanism does not require each receiving device to establish a connection to a centralized distribution server, to carry an out-of-band credential for such a server, or to depend on out-of-band connectivity infrastructure.

Each receiving device admits the update independently of which peer relayed it. The governed observation is evaluated through the composite admissibility evaluator of Chapter 4, verifying the authority credential of the deploying authority, the evidential weight assigned to that authority under the receiving device's authority taxonomy, and the consistency of the update with the device's prior policy state. Skill-adapter updates additionally pass through the adapter-sandboxing mechanism of Chapter 8, a sandbox evaluation per Section 8.5 performed prior to adapter activation; an artifact failing sandbox evaluation is not activated and the outcome is recorded in the device's lineage field.

Each admission, and each transition between versions, is recorded in the receiving device's lineage field. A rollback mechanism enables reversion to the prior state upon detection of admission failure or upon receipt of a subsequent revocation governed observation. Because every cache event, availability broadcast, and peer-loading event is recorded in lineage, the propagation of an artifact across the mesh is reconstructible after the fact through the deterministic lineage of the architecture.

Operating Parameters

Operating parameters of the distribution layer are governance-policy-defined rather than implementation-fixed. The set of authorities recognized as valid deploying authorities, the applicability scope that bounds which devices are eligible to receive an update, and the admissibility constraints applied at each receiving device are expressed as governance policy bound to the receiving unit's authority taxonomy. A policy update may itself specify a geographic scope, a temporal scope, a device-class scope, or a combination thereof, limiting application to receiving devices within the specified scope. Hierarchical propagation applies supersession semantics, so that an update issued by a higher-authority deploying authority supersedes a conflicting state established by a lower-authority deploying authority.

Distribution accommodates deployments where approaching operating units lack continuous connectivity to the governed marketplace of Chapter 22. Under infrastructure-mediated skill distribution per Section 12.7, cognitive infrastructure agents cache adaptation artifacts and emit governed adaptation-artifact-availability observations through the governed mesh to approaching operating units, producing skill-distribution coverage in regions of sparse or intermittent connectivity through the local infrastructure presence. The mechanism composes with mobile store-and-forward carriage per Section 2.7, wherein governance-credentialed operating units carry availability observations across regions of sparse connectivity and propagate them to receiving infrastructure agents upon entry into the agents' signaling volumes.

Admission policy is evaluated locally at each receiving device through the composite admissibility evaluator of Chapter 4. The evaluator verifies the deploying authority's credential, assigns evidential weight under the receiving device's authority taxonomy, and checks consistency with prior state; no external arbiter is consulted at distribution time. Need detection at an infrastructure agent identifies skill gaps in approaching units through repeated composite admissibility degradation events, repeated solicitation emissions per Section 5.8, repeated confidence-governor graduated-mode de-escalations per Section 6.4, or any governance-policy-defined skill-gap indicator, and selects high-trust adaptation artifacts appropriate for the detected gap.

Alternative Embodiments

Several embodiments of the distribution layer are contemplated. In one embodiment, propagation runs over multi-hop relay per Section 2.6, in which a governed observation carrying the update is relayed device to device through the governed mesh. In a second embodiment, suitable for regions of sparse or intermittent connectivity, distribution proceeds through mobile store-and-forward carriage per Section 2.7, in which a governance-credentialed operating unit buffers an update emitted at an ingress point, carries it across the sparse-connectivity region, and rebroadcasts it upon entry into a receiving device's signaling volume. In a third embodiment, infrastructure-mediated skill distribution per Section 12.7 caches artifacts at cognitive infrastructure agents and peer-loads them into approaching operating units through sandbox evaluation per Section 8.5.

Embodiments differ also in their handling of revocation. Revocation is carried as a subsequent governed observation propagated through the same mesh mechanism as the artifacts. Upon receipt of a revocation governed observation, the receiving device's rollback mechanism enables reversion to the prior state. Skill retirement and condition-based deprecation per Section 12.11 subject adaptation artifacts produced through the training governance primitive to automatic retirement upon detection of governance-policy-defined conditions, with each retirement recorded in lineage.

Composition with cross-model portability is also an embodiment dimension. Under the cross-model portability mechanism of Section 8.7, an adaptation artifact authored for one base cognitive substrate may be transferred for consumption by a second base cognitive substrate of structurally compatible architecture, with a portability-lineage recorder recording each cross-model portability event. The distribution layer carries such artifacts across heterogeneous fleets while preserving the governance chain and the artifact's training-data provenance through lineage.

Composition With the Broader Architecture

The distribution primitive composes structurally with cross-model portability, with the composite admissibility gate, and with the settlement mechanism. Cross-model portability ensures that an artifact authored for one base cognitive substrate can be transferred to a structurally compatible substrate, with the distribution layer carrying the artifact and its portability-lineage records. The composite admissibility gate ensures that an artifact, once distributed, is activated into downstream operation only if it passes the composite admissibility evaluation of Chapter 4, consistent with the five-property governance chain imposed on every governed mutation in the architecture.

The settlement mechanism composes by treating distribution events as governance-chain-preserving records. Distribution and consumption events recorded in lineage support the reputation track record maintained per Chapter 28, producing per-transfer performance feedback for a consuming agent operating under a transferred artifact. Where a distribution or transfer event constitutes a bilateral exchange, it admits matched-pair settlement per Chapter 20, producing a governance-chain-preserving bilateral settlement record that itself enters lineage.

Distinction From Prior Art

The distribution layer is structurally distinguished from prior centralized firmware-update mechanisms and prior centralized policy-distribution mechanisms. The mechanism disclosed here does not require each receiving device to establish a connection to a centralized distribution server, does not require each receiving device to carry an out-of-band credential for such a server, and does not depend on out-of-band connectivity infrastructure for the propagation of updates.

The present mechanism propagates through the governed mesh itself. Trust flows from the deploying authority's credential and the receiving device's authority taxonomy, evaluated locally through the composite admissibility evaluator of Chapter 4, rather than from a distribution server's identity. Audit is a property of the artifact's lineage, recorded deterministically at each cache, broadcast, and peer-loading event. Revocation is carried as a governed observation and propagated by the same mesh mechanism as the artifacts. The mechanism additionally supports propagation into regions of sparse or intermittent connectivity through mobile store-and-forward carriage per Section 2.7, where a centralized distribution server would have no reach. It operates independently of and complementary to the governed marketplace of Chapter 22, providing distribution coverage where approaching units lack continuous marketplace connectivity.

Operational Considerations

In practice the distribution layer must accommodate the asymmetries of real deployment topologies. Field units may have intermittent connectivity and episodic high-bandwidth windows; cognitive infrastructure agents near data-rich operating centers may have continuous links. The mechanism does not assume uniform connectivity. A cognitive infrastructure agent maintains a governance-credentialed adaptation-artifact cache and emits availability observations through the governed mesh to approaching units, while a governance-credentialed operating unit may buffer updates under store-and-forward carriage and rebroadcast them on entry into a receiving device's signaling volume. On reconnection, a unit serves both as consumer of newer artifacts and, through the infrastructure caching and store-and-forward mechanisms, as a carrier contributing updates back into the mesh without dependency on any central server.

Failure modes admit governed handling. A receiving device may evaluate an update and find it inconsistent with prior state or below the evidential weight its authority taxonomy assigns the deploying authority; the composite admissibility evaluator refuses admission and the outcome is recorded in lineage. A skill-adapter update may fail sandbox evaluation per Section 8.5; it is not activated and the sandbox-evaluation output is recorded in the device's lineage field. Upon admission failure or receipt of a revocation governed observation, the rollback mechanism reverts the device to its prior state. None of these failure modes require central intervention, and each is observable in the lineage record after the fact.

Disclosure Scope

This disclosure, supported by U.S. Provisional Application No. 64/049,409, covers the mesh-distributed propagation of skill-adapter updates through the governed mesh, infrastructure-mediated skill distribution with caching, availability broadcast, and peer-loading, mobile store-and-forward carriage for sparse-connectivity regions, the composite admissibility evaluation and sandbox pre-activation that gate downstream activation, the lineage recording of cache, broadcast, and peer-loading events, and the composition of the distribution primitive with cross-model portability, with the five-property governance chain, and with matched-pair settlement. Equivalents, including alternative multi-hop relay and store-and-forward strategies and alternative revocation propagation through the governed mesh, are within scope. Implementations that rely on a single centralized distribution server, or that do not bind activation to composite admissibility evaluation and lineage recording, fall outside the disclosed mechanism.