Cognitive Disruption as Architectural Phase-Shift

Mechanism

Each agent in the cohort maintains a vector of stimulus-regime estimators. The estimators consume observation events and produce, at a fixed cadence, a tuple summarizing the central tendency, dispersion, and rate-of-change of the ambient stimulus distribution along each monitored dimension. A phase-shift is declared when one or more estimators report a sustained excursion beyond a configured boundary, where sustained means that the excursion persists across a configured number of consecutive cadences. The boundary is not a threshold on raw stimulus magnitude; it is a threshold on the divergence between the current regime estimate and the regime estimate that the cohort's prevailing policy was selected to fit.

When a phase-shift is declared, the detecting agent emits a regime-change advisory. The advisory carries the identity of the detector, the dimensions on which the excursion was observed, the magnitude and direction of the divergence, and a credentialed timestamp. The advisory is broadcast to the cohort through the governed propagation fabric. Receiving agents do not unilaterally adopt a new policy on receipt; they incorporate the advisory into a cohort-level corroboration count and wait for the count to reach a configured quorum before triggering a policy update.

The policy update, once triggered, is itself a structural action subject to admissibility gating. The update specifies the new policy parameters, the cohort scope to which it applies, and the credentials authorizing the change. Each receiving agent verifies the credentials, applies the update locally, and emits a confirmation. A cohort-level audit accumulates confirmations and produces a record of which agents adopted the update at what time, providing a basis for subsequent reasoning about partial adoption.

Bounded propagation is enforced at two layers. At the fabric layer, the advisory carries a propagation horizon that limits the number of hops or the cohort-membership scope across which it is forwarded. At the agent layer, each forwarder applies a rate limiter that caps the number of advisories it will retransmit within a sliding window, preventing advisory storms in which correlated detectors flood the fabric. The rate limiter is keyed on the dimension of the reported excursion, so that an agent rate-limited on one dimension remains responsive to advisories on other dimensions. The combination of horizon and rate limit is essential: a horizon alone permits bursty saturation within the bounded scope, and a rate limit alone permits unbounded geographic spread of a sustained but localized regime change. Together they bound both the spatial and temporal cost of a single phase-shift event.

The phase-shift event itself is not erased by the policy update it triggers. The advisory, the corroboration tally, the credentials presented at update time, and the per-agent confirmation records are retained as a credentialed trace of the event. A subsequent investigator can reconstruct, from the trace alone, which estimators detected the regime change first, how the corroboration count grew over time, when quorum was reached, and which agents adopted the resulting policy update at what cadence. This forensic affordance distinguishes governed phase-shift handling from autonomous drift adaptation, in which the absence of an event record obscures the chain of causation that produced behavioral change.

Operating Parameters

The cadence at which estimators report regime tuples is configurable per dimension and per agent class. High-bandwidth sensory dimensions may require sub-second cadences; slow-varying dimensions may report on the order of minutes. The persistence requirement, expressed as the number of consecutive over-boundary cadences required to declare a phase-shift, controls the tradeoff between detection latency and false-positive rate. A short persistence requirement detects regime changes quickly but is sensitive to transient noise; a long persistence requirement is robust but lags behind genuine regime changes.

The corroboration quorum for cohort policy update is configurable per policy class. A safety-critical policy class may require corroboration from a supermajority of the cohort before any agent acts on an advisory; a low-stakes policy class may allow individual adoption with after-the-fact cohort review. The quorum threshold is enforced by the receiving agents, each of whom maintains a tally of distinct credentialed advisories received within a configured aggregation window.

Rate limiting at the forwarder is parameterized by the maximum advisory count per window, the window length, and a backpressure policy that determines what happens to advisories arriving while the limiter is saturated. Available backpressure policies include drop-with-counter, queue-and-delay, and merge-and-summarize, the last of which combines multiple within-window advisories into a single summary advisory carrying the union of detector identities and the aggregated divergence statistics.

Propagation horizons are configured per advisory class. An advisory that pertains only to a local subset of the cohort carries a small horizon; an advisory pertaining to the full cohort carries a horizon sufficient to span the cohort's diameter under the prevailing fabric topology. The horizon is enforced by hop counters and by cohort-membership predicates that forwarders evaluate before retransmission. Misconfigured horizons fail safely: an undersized horizon delays corroboration but does not produce a spurious update, and an oversized horizon spends fabric capacity but does not by itself cause an unintended policy change because the corroboration quorum remains the operative gating threshold.

Aggregation window length governs how recent advisories are considered when tallying corroboration. A short aggregation window requires temporally tight corroboration and is appropriate for fast-changing regimes; a long aggregation window tolerates spread arrival times and is appropriate for slow regime drift. The aggregation window is decoupled from the broadcast cadence of the underlying estimator: a fast estimator may emit advisories that are aggregated under a long window, and a slow estimator may emit advisories that are aggregated under a short window, depending on how the policy class characterizes the regime change of interest.

Confirmation collection parameters determine how long the cohort waits for adoption confirmations before declaring partial adoption. Partial adoption is itself a first-class state: the cohort's policy registry records that some agents updated and others did not, and downstream reasoning can take the partial state into account rather than assuming uniform adoption. The cohort may schedule a re-broadcast of the update to non-confirming agents, escalate to a manual review, or continue operating in mixed-policy mode according to the policy class's tolerance for heterogeneity.

Alternative Embodiments

In one embodiment, regime estimators are implemented as exponentially weighted moving statistics over the observation event stream, with boundaries defined as multiples of the running standard deviation. In an alternative embodiment, estimators are implemented as Bayesian change-point detectors that produce posterior probabilities of regime change, with boundaries defined as posterior thresholds. The two embodiments are interchangeable at the advisory layer; the advisory format is invariant to the underlying detection technique.

In another embodiment, the policy update is parameterized as a delta against the prior policy, allowing receiving agents to apply only the changes rather than reloading the full policy. This embodiment reduces propagation payload size and supports incremental policy refinement across multiple successive advisories. A symmetric embodiment carries the full new policy in the update, prioritizing simplicity of receiver logic over propagation cost.

In a further embodiment, the cohort is partitioned into tiers, with high-tier agents serving as advisory aggregators that consume raw advisories from low-tier agents and emit corroborated regime-change declarations to the rest of the cohort. This embodiment is appropriate for cohorts whose membership is too large for direct peer-to-peer corroboration. In an alternative flat embodiment, all agents participate equally in advisory generation, forwarding, and corroboration.

In an embodiment supporting heterogeneous agent populations, the boundary thresholds and persistence requirements are specialized per agent class, allowing agents with differing sensor modalities or differing operational risk profiles to apply different detection sensitivities to the same underlying environment. Cross-class corroboration is supported by a calibration map that translates each class's advisory dimensions into a common cohort-level dimension space, so that quorum can be tallied across classes without requiring unit-level commensurability of raw measurements.

In a still further embodiment, the policy library is itself a cohort-shared structure that participates in the same advisory fabric, so that additions of new policies and retirements of obsolete policies are propagated as governed advisories distinct from regime-change advisories. This embodiment unifies policy lifecycle management with regime adaptation, allowing a single audit trail to capture both why the cohort decided a regime change had occurred and why a particular successor policy was selected.

In an embodiment optimized for ephemeral cohorts, agents joining the cohort receive a cohort-state digest summarizing the current regime estimate, the active policy, and the recent advisory history within a configurable look-back window. The digest enables a newly joined agent to participate in corroboration and confirmation immediately, without requiring a cold replay of cohort history. The digest is itself credentialed and is regenerated on a configurable cadence by a designated cohort coordinator role.

Composition

Phase-shift detection composes with the cohort's policy library. The library catalogs the policies available for selection, each annotated with the regime conditions under which it is appropriate. When a phase-shift is corroborated, the cohort's policy selection logic queries the library for the policy whose regime annotation best matches the new regime estimate, and emits the corresponding policy update advisory. The library itself is governed: additions, modifications, and retirements of policies require credentialed lineage entries.

Phase-shift advisories compose with the lineage substrate by being emitted as credentialed observation events. Downstream consumers of lineage can therefore reconstruct, after the fact, which regime declarations were in effect at any given time, which agents corroborated them, and which policy updates were triggered as a result. This supports incident review and regulatory audit without requiring a separate audit channel.

Phase-shift handling composes with the rate-limiting and bounded-propagation primitives provided by the governed fabric. The fabric enforces global invariants on advisory traffic, such as fairness across cohorts and isolation between unrelated cohorts, while phase-shift logic enforces local invariants on advisory generation and corroboration. The two layers are orthogonal: phase-shift logic does not need to know the fabric topology, and the fabric does not need to know the semantics of any particular advisory.

Phase-shift detection composes with downstream actuation by serving as a structural justification for shifts in actuation policy. An actuation supervisor that observes a corroborated cohort-wide policy update can adjust its actuator gating in concert with the regime change, with the audit trail linking the actuation policy adjustment back to the underlying regime evidence. This linkage is what permits regulators and downstream reviewers to reconstruct, after the fact, why a particular regime of actuation was in effect at a particular time.

Phase-shift handling composes with cohort identity management by treating cohort joins and departures as events that affect the corroboration tally denominator. When a cohort's membership changes, in-flight corroboration tallies are recomputed against the updated membership before quorum is evaluated, so that a regime change cannot be adopted on the strength of corroboration from an obsolete cohort composition.

Prior-Art Distinctions

Conventional concept-drift detection systems operate on individual model performance metrics and trigger model retraining when drift is detected. The retraining is a per-instance action; there is no notion of cohort-wide policy update gated by cross-instance corroboration, no rate limiting at the advisory layer, and no bounded propagation horizon. The present system treats regime change as a cohort-coordinated event rather than as an instance-local trigger.

Conventional alarm correlation systems aggregate alerts from multiple sources and present a deduplicated view to operators. The aggregation is descriptive and does not gate any structural action. The present system uses cross-source corroboration as a precondition for cohort policy update, making the corroboration causally upstream of the action rather than informational about already-taken actions.

Conventional rate limiting in distributed systems is applied per request type or per source identity. The present system rate-limits at the level of advisory dimension and applies bounded propagation horizons keyed to advisory class, neither of which is a property of conventional rate limiters.

Conventional federated learning systems coordinate parameter updates across distributed model instances under a centralized aggregator, with no separation between the regime-change signal and the parameter delivery. The present system separates regime detection from policy delivery, treats them as distinct credentialed events with their own corroboration semantics, and supports policy classes other than parameter updates including symbolic rule sets, gating thresholds, and structural reconfiguration directives.

Conventional anomaly detection systems flag individual events as anomalous and route them to operator queues. The present system does not produce an anomaly queue; it produces a structural regime classification whose corroboration is a precondition for a regulated cohort-wide action. The corroborated regime change is a state of the cohort, not a flag on an event.

Disclosure Scope

The disclosed subject matter encompasses any system in which a detected change in ambient stimulus regime, declared by a credentialed detector and corroborated by a configured quorum of cohort members, triggers a cohort-wide policy update whose propagation is bounded by horizon and whose generation is rate-limited at the advisory source. The disclosed scope is not limited to any particular regime estimator implementation, advisory transport, policy representation, or cohort topology. The disclosed scope includes embodiments in which corroboration is performed peer-to-peer, through tiered aggregators, or via an external corroboration service, provided the structural properties of credentialed advisory, bounded propagation, and rate-limited emission are preserved.