Confidence-Integrity Feedback Loop

Mechanism

The integrity-feedback mechanism is defined in Chapter 5 of the cognition patent as a structural coupling between the integrity-violation detector and the confidence-governance state. When the detector emits a violation record — for example, a contradiction between asserted and observed canonical fields, a lineage gap, or a constraint failure during validation — the governance state machine consumes that record and applies a deterministic transformation to its own parameters.

The transformation is not heuristic. It is a policy-defined function from the violation record (containing the violation type, severity, agent identifier, and lineage chain) to a delta on the governance parameters (sufficiency threshold, capability tier, decay rate, hysteresis margin). The function is pure and reproducible: identical violation records produce identical parameter deltas, and the prior governance state plus the delta yields the next governance state.

Threshold elevation is the most direct effect. When a violation is admitted, the sufficiency threshold for subsequent recomputation cycles is raised by a policy-defined increment scaled by violation severity. The agent now requires more evidence to act on the same kind of decision; the system has structurally encoded "I have just been shown to be wrong, so I should require more before claiming to be right."

Capability narrowing is the second effect. Each agent operates within a tier that defines which skills it may invoke and which actions it may take without escalation. A violation narrows the tier — typically by demoting the agent one step on a discrete tier ladder — for a policy-defined window. During the narrowed window, skill invocations that would previously have been auto-authorized now require explicit confirmation, an additional inquiry pass, or a higher trust score on inputs.

Downstream propagation is the third effect, and the most consequential for multi-agent systems. The governance runtime maintains a directed graph of trust relationships among agents. When agent A is restricted, agents downstream of A in the trust chain — those that consumed A's outputs as inputs to their own decisions — receive a corresponding propagated restriction. The propagated restriction is attenuated by distance and by the freshness of the consumed outputs, so that agents who consumed A's recent outputs are restricted more than agents who consumed A's outputs long ago and have since revalidated.

Recovery is structural, not implicit. The narrowed tier and elevated threshold persist for a policy-defined window or until a defined recovery condition is satisfied: a passing audit, a restorative validation pass, an external attestation, or simple time-based decay back toward the baseline. Recovery is recorded in lineage with the same fidelity as restriction so that auditors can reconstruct the full trajectory of a session's governance state.

Operating Parameters

Severity coefficients map violation classes to numeric multipliers on threshold elevation and tier-narrowing depth. Minor violations (a single field discrepancy resolved by re-retrieval) use a small coefficient; major violations (a constraint failure on a safety-critical field) use a large coefficient. Coefficients are configurable per deployment so that regulated domains can encode regulator-mandated severity weights.

Recovery half-life governs how quickly the governance state decays back toward baseline absent further violations. In high-trust deployments the half-life is short, allowing the agent to resume normal operation quickly after isolated incidents. In safety-critical deployments the half-life is long, requiring sustained clean operation before authority is restored.

Propagation depth and attenuation factor govern downstream effect. Depth caps the number of trust-graph hops a restriction may traverse; attenuation reduces the magnitude at each hop. Both are policy-resident and recorded in lineage so that the propagation pattern is reconstructible. Hysteresis margins around tier boundaries prevent rapid oscillation: once an agent has been demoted, it cannot be promoted back to the prior tier without exceeding the demotion threshold by a configurable margin.

Audit cadence parameters govern how often the governance runtime emits a structured snapshot of current threshold values, current tier assignments, and the recent violation log. In regulated deployments the cadence is high, producing a near-continuous audit stream; in unregulated deployments the cadence may be event-driven only.

Violation deduplication parameters control whether repeated emissions of the same underlying integrity issue produce repeated parameter deltas or a single cumulative delta. Naive accumulation can over-tighten the governance posture in response to a single root cause that surfaces multiple times; aggressive deduplication can under-respond to genuinely repeating distinct violations. The deduplication window and the equivalence relation that determines whether two violations are considered duplicates are both policy-resident and recorded in lineage so that the deduplication decisions themselves are auditable.

Governance-state floor and ceiling parameters bound the cumulative effect of feedback. The threshold cannot be raised indefinitely by repeated violations; a configurable ceiling caps the maximum threshold value, beyond which further violations trigger an escalation event (typically a notification to a supervisory operator) rather than a further parameter delta. Symmetrically, a floor ensures that a long history of clean operation does not relax the threshold below a minimum required by policy. Both bounds are deployment-specific and recorded with each emitted governance snapshot.

Alternative Embodiments

In a multi-agent autonomous-driving fleet embodiment, an integrity violation in one vehicle's perception stack — for example, a confirmed misclassification — propagates as elevated thresholds across vehicles that consumed the same upstream model output. Vehicles that consumed the output recently are restricted to a more conservative driving policy until a fleet-wide revalidation pass clears the restriction.

In a regulated-financial-trading embodiment, a violation in a pricing-model agent narrows the trading tier of any execution agent downstream that relied on that pricing in the last N minutes. The narrowed execution agent may continue to operate but only on smaller order sizes and with higher confidence requirements until the upstream pricing model is revalidated by an audit pass.

In a clinical-decision-support embodiment, a violation in a diagnostic agent — a contradiction between asserted differential and confirmed pathology — feeds back to raise the threshold for subsequent diagnostic confidence claims by that agent during the same session, and propagates to downstream treatment-recommendation agents that consumed the diagnostic.

In a single-agent embodiment, the mechanism still applies: detected self-violations (the agent's own output contradicting its own subsequent observation) feed back to elevate the agent's own thresholds for the remainder of the session, producing a within-session learning effect without any change to the underlying model weights.

Composition With Other Mechanisms

Integrity feedback composes with inquiry mode: an elevated threshold makes inquiry mode more likely to be triggered, so the agent enters structured clarification more often after a violation. It composes with skill gating: a narrowed tier removes specific skills from the agent's authorized set, so even if confidence rises, the agent cannot invoke the restricted skills until tier is restored. It composes with lineage: each parameter delta is recorded with the violation record that caused it, so the governance trajectory is fully reconstructible.

Critically, the feedback loop does not require model retraining or any change to underlying weights. The mechanism operates entirely on policy-resident state and lineage records, so it is compatible with any underlying inference substrate — symbolic, neural, hybrid, or rule-based.

Composition with biological-binding gating produces an additional safety property: when an integrity violation is detected, not only are thresholds raised and tier narrowed, but the binding record may be invalidated, requiring a fresh operator attestation before the agent resumes any gated skill. This converts an integrity event into an explicit human-in-the-loop checkpoint without requiring the system architect to wire one in by hand.

Composition with the inquiry-mode mechanism is symmetric. An unresolved inquiry that exhausts its budget is itself a low-grade integrity signal — the agent has been unable to reach sufficiency despite legitimate effort — and may, depending on policy, contribute a small parameter delta of its own. The result is that persistent inability to reach confidence sufficiency progressively narrows authority in the same way that explicit violations do.

Distinction From Prior Art

Prior systems treat integrity violations as logging events or as triggers for human review. They do not feed violations back into a deterministic governance state machine that adjusts threshold and tier in a reproducible manner. Adaptive trust systems in network security adjust trust scores but operate at the network rather than cognitive layer; they do not gate skill invocations on a per-agent canonical-field basis.

Online-learning systems update model weights in response to errors, but weight updates are non-deterministic, opaque, and difficult to audit. The claimed mechanism differs by leaving model weights untouched and adjusting only declarative governance state, producing a fully auditable and reproducible feedback loop. Multi-agent reputation systems adjust peer-trust scores but lack the structural coupling to a confidence scalar and a capability tier; they cannot prevent a low-reputation agent from invoking skills that should be tier-gated.

Disclosure Scope

The disclosure encompasses any agent runtime in which an integrity-violation record deterministically transforms governance parameters — sufficiency threshold, capability tier, decay rate, hysteresis margin — for the offending agent and, via a trust graph, for downstream agents. The scope is independent of the violation-detection mechanism and independent of the underlying inference substrate.

Implementations may be single-agent or multi-agent, single-process or distributed, regulated or unregulated. In every embodiment the structural elements are the same: a violation record schema, a deterministic parameter-transformation function, a tier ladder with hysteresis, a trust graph with attenuation, and a recovery condition. Implementations omitting any of these elements are outside the claimed scope; implementations including them are within scope regardless of language, topology, or application domain.

The disclosure further encompasses embodiments in which the trust graph is constructed dynamically from observed data flow rather than configured statically. In dynamic-graph embodiments, the runtime infers downstream consumers from lineage records of which agents read which outputs within a window, and applies the propagation function over the inferred graph. The propagation behavior is identical to the static-graph case; only the source of the graph topology differs. Both are within scope.

Likewise within scope are embodiments in which the parameter-transformation function is itself parameterized by external context — for example, a regulatory mode flag that selects between a permissive transformation and a strict transformation depending on whether the agent is operating under a clinical-trial protocol, a production protocol, or a research protocol. Mode-dependent transformations remain deterministic given the mode flag, and the mode flag itself is recorded in lineage so that violation responses are reproducible from the lineage record alone.