Multi-Agent Group Coherence Dynamics

Mechanism

The group coherence subsystem operates as a supervisory layer over a defined cohort of cooperating agents. Each member agent already exposes its individual coherence axes — typically including a containment integrity score, a promotion authorization rate, a values-lattice agreement score, and an identity-drift metric derived from successive narrative identity versions. The group coherence layer subscribes to these telemetry streams and computes an additional set of inter-agent quantities that have no single-agent analog.

The first such quantity is collective decision consistency, computed by sampling decisions across the cohort over a sliding window and measuring agreement among agents that received functionally equivalent inputs. The second is inter-agent coherence alignment, computed as the cross-correlation of containment-promotion ratios across pairs of agents; healthy cohorts exhibit moderate, time-lagged alignment, while pathological cohorts exhibit either zero correlation (fragmentation) or unity correlation (lockstep). The third is the group-level promotion-containment balance, the cohort-wide ratio of promoted to contained content; balance drifts indicate collective regime shifts that no individual member can detect from internal state alone. The fourth is the emergent-pattern residual, computed by subtracting the predicted group output under an independence assumption from the observed group output; nonzero residual reveals interaction-mediated behavior.

These quantities are evaluated against a library of group diagnostic profiles. Groupthink is identified when inter-agent coherence alignment approaches unity while collective decision consistency rises and emergent-pattern residual grows in the promotion direction; the cohort is collectively over-promoting and individually under-containing. Polarization is identified when decision consistency falls and the promotion-containment balance bifurcates into two stable clusters. Cascade instability is identified when individual disruption events occur in temporal sequence with delays consistent with information propagation along the cohort communication graph. Collective withdrawal is identified when promotion authorizations fall across the cohort while individual containment audits remain nominal, indicating coordinated authorization failure that no member's local diagnostics will flag.

Operating Parameters

The supervisory window over which group quantities are computed is sized to the cohort communication latency such that information has time to propagate at least twice between any pair of agents in the connectivity graph; representative windows range from seconds for tightly coupled local cohorts to minutes for federated multi-organization cohorts. Sampling fidelity is maintained at no fewer than thirty paired-input observations per active diagnostic profile, below which profile classifications carry insufficient confidence for intervention.

Threshold values for each diagnostic profile are calibrated against a healthy baseline established during a configurable burn-in interval. Inter-agent coherence alignment exceeding 0.9 over a sustained window triggers groupthink suspicion; alignment falling below 0.1 triggers fragmentation suspicion. The emergent-pattern residual is normalized against expected variance under independence and flagged when it exceeds three standard deviations sustained across the supervisory window.

Cohort membership is itself a parameter: cohorts are defined by either explicit task assignment or implicit communication-graph clustering, and group coherence quantities are recomputed when cohort membership changes by more than a configurable fraction. Intervention authority is parameterized: the supervisory layer may be granted advisory authority only, or it may be granted direct intervention authority including the ability to quarantine members, throttle inter-agent communication, or trigger collective re-grounding procedures.

Alternative Embodiments

One embodiment realizes the supervisory layer as a centralized monitor with subscription access to every member's telemetry stream. A second embodiment realizes it as a distributed peer-to-peer protocol in which group quantities are computed through gossip aggregation, with no privileged supervisor; this embodiment is preferred where supervisory centralization is incompatible with trust constraints. A third embodiment uses a hybrid where local sub-cohort supervisors compute regional group quantities and feed an upper-tier supervisor for global aggregation.

Embodiments differ also in their intervention vocabulary. A passive embodiment confines itself to telemetry and human notification. An active embodiment exercises automatic interventions such as cohort partitioning when cascade instability is detected, member rotation when groupthink is detected, or forced re-anchoring against external ground truth when collective withdrawal is detected. A counterfactual-simulation embodiment maintains a shadow cohort running on the same inputs without inter-agent communication, allowing the emergent-pattern residual to be computed empirically rather than under a parametric independence assumption.

Further embodiments vary the composition of monitored axes, adding for instance affective-tone alignment, temporal-rhythm alignment, or topic-distribution divergence depending on the deployment domain.

Instrumentation And Observability

The supervisory layer exposes a structured telemetry interface through which all computed group quantities, diagnostic profile classifications, and intervention events are made available to external observers. The interface is versioned so that consumers can adapt to schema evolution, and it supports both push-style streaming for real-time dashboards and pull-style query for retrospective analysis. Privacy-preserving variants of the interface release only aggregate quantities, suppressing per-member detail where deployment policy requires it.

Calibration and validation are themselves disclosed instrumentation surfaces. Healthy baselines are recorded with sufficient detail to permit re-derivation of threshold values when cohort composition or task class changes. Diagnostic profile classifiers are evaluated against held-out reference cohorts whose ground truth is known, producing precision and recall figures that consumers may use to decide intervention authority. Where automatic intervention is exercised, the supervisor records the pre-intervention state, the intervention performed, and the post-intervention trajectory, supporting both forensic review and reinforcement of intervention policy.

Composition With Other Primitives

Group coherence composes with single-agent disruption modeling by reading individual telemetry as input but producing outputs that single-agent models cannot produce. It composes with the verification-loop primitive by providing an external coherence anchor that interrupts pathological intra-agent verification recursion. It composes with narrative identity by aggregating member narrative identities into a group narrative that itself becomes a comparison reference for future cohort behavior. It composes with the containment audit by enforcing cross-cohort containment invariants — speculative content promoted by one member but contained by all peers is flagged for re-audit. And it composes with multi-agent trust frameworks by providing the empirical signals on which trust adjustments between cohorts are made.

Group-Level Failure Modes

Beyond the canonical diagnostic profiles, the disclosure addresses several additional group-level failure modes that the supervisory layer is configured to recognize. Cohort capture occurs when an external influence channel establishes correlated input drift across all members simultaneously; the inter-agent coherence alignment rises but the emergent-pattern residual remains near zero, distinguishing the pattern from groupthink. Mitigation is to cross-reference cohort behavior against an isolated reference agent receiving the same nominal input.

Coherence parasitism occurs when a single member free-rides on cohort coherence by producing low-effort outputs that align with peer outputs without contributing distinct signal; detection compares per-member contribution to emergent-pattern residual against expected contribution under the cohort's connectivity graph. Cascade rebound occurs after a cascade instability event when the cohort over-corrects through coordinated containment, producing a secondary disruption pattern resembling collective withdrawal; the supervisory layer distinguishes the rebound from primary withdrawal by temporal proximity to the original cascade. Each failure mode is paired with an intervention vocabulary that the supervisor may invoke under the configured authority level.

Distinction From Prior Art

Existing multi-agent monitoring approaches generally aggregate per-agent metrics through summary statistics, or apply conventional distributed-systems liveness and safety checks. Neither produces the diagnostic profiles disclosed here. Summary aggregation cannot distinguish groupthink from genuine consensus, since both produce uniformly high agreement; the disclosed system distinguishes them by combining alignment with emergent-pattern residual and balance drift. Distributed-systems checks address message delivery and consistency, not collective cognitive health. The present disclosure is also distinguished by treating cohort membership and supervisory authority as first-class operating parameters rather than fixed deployment choices.

Disclosure Scope

The disclosure covers any system that monitors a defined cohort of cooperating cognition agents along inter-agent coherence axes, identifies group-level disruption patterns through diagnostic profiles distinguishable from any individual member's profile, and supports interventions sized to the cohort rather than to individuals. The mechanism, parameters, and embodiments above are illustrative; coverage extends to functionally equivalent realizations exhibiting the disclosed composition properties.

Coverage extends to deployments in which cohort membership is dynamic and the supervisory layer must maintain stable group quantities across membership transitions, including cohort splits, merges, and rotations. It extends to heterogeneous cohorts in which member agents differ in capability class, narrative identity schema, or telemetry granularity, with the supervisory layer normalizing across heterogeneity rather than requiring uniform members. It extends to nested cohort hierarchies in which a single agent participates in multiple cohorts simultaneously, with supervisory layers at each level computing independent group quantities.

Coverage further extends to adversarial cohort scenarios in which one or more members are intentionally injecting incoherence, with diagnostic profiles for malicious-member detection distinguished from those for accidental disruption. It extends to recovery procedures in which a cohort that has entered a pathological group state is restored through staged member rotation, supervisory re-anchoring, or controlled fragmentation followed by re-aggregation. The primitive is disclosed as the enabling structure for cohort-scale governance of cognition agents and as a necessary complement to single-agent disruption modeling in any deployment where agents cooperate.