Redemption Engine

Mechanism

The redemption engine is invoked by the integrity subsystem when an integrity observation crosses a declared threshold or when an explicit violation is reported. Invocation is deterministic: the same integrity observation, evaluated against the same policy, produces the same redemption decision. The engine is not a probabilistic recovery heuristic; it is a structured procedure with declared inputs, declared phases, and declared termination conditions.

Upon invocation, the engine performs deviation analysis. It reads the present state object, the active integrity descriptor, and the lineage hash chain identifying the admitted transitions that preceded the violation. Deviation analysis identifies the smallest contiguous lineage segment whose retraction would restore integrity below the violation threshold. This segment is the redemption scope: redemption operates on it and only on it.

With scope identified, the engine generates candidate restorative mutations. Candidates fall into bounded classes: rollback (retract the offending transitions and resume from a prior admitted state), scope-narrow (preserve the lineage but constrict the active capability set so the offending behavior cannot recur), evidence-gather (request additional witness observations that would either confirm or refute the violation before further action), and audit-emit (produce a credentialed observation surfacing the violation to an external authority). Candidates may compose: a typical procedure scope-narrows immediately, gathers evidence, and either rolls back or audit-emits based on the evidence outcome.

Each candidate carries a restoration impact projection: a structured estimate of which downstream consumers, dependent skills, and bound observations will be affected by the candidate's application. The projection is consumed by the admissibility gate, which evaluates the candidate against policy bounds before any mutation is committed. Candidates whose projected impact exceeds policy-declared redemption budgets are rejected in favor of more conservative alternatives, or escalated to operator authority if no admissible candidate exists.

Once a candidate is admitted, the engine commits the restorative mutation through the same admissibility gate that governs ordinary inference transitions. The commit produces a new lineage hash and an integrity-restored descriptor; the redemption procedure itself is recorded as a credentialed observation, identifying the triggering violation, the chosen candidate, the projected impact, and the actual post-commit integrity value.

Operating Parameters

Redemption is bounded along four axes. Scope bound: the segment of lineage that redemption may retract is limited by policy; deep rollbacks across long-lived commitments require operator authority. Budget bound: the cumulative restorative impact within a policy-declared window is capped, preventing redemption thrash where repeated violations trigger repeated rollbacks. Depth bound: redemption invocations may not recursively invoke redemption beyond a declared depth; a redemption procedure that itself produces an integrity violation must escalate rather than self-redeem. Time bound: each redemption procedure has a wall-clock deadline; procedures that exceed the deadline escalate to operator authority with the partial state preserved.

Threshold parameters govern invocation. The integrity subsystem evaluates each new observation against threshold curves declared in the policy reference; threshold crossings are the canonical redemption trigger. Operators may declare per-context thresholds: a clinical deployment may set tighter thresholds for safety-critical capabilities and looser thresholds for advisory functions, and the engine will invoke accordingly.

Candidate selection is deterministic given the policy reference and the current state. The policy declares candidate ordering — for example, evidence-gather first, scope-narrow second, rollback third, audit-emit as terminal — and the engine evaluates in that order, committing the first admissible candidate. Operators may override ordering for specific violation classes; for instance, safety-critical violations may be configured to scope-narrow before any other action.

Audit emission is credentialed. Every redemption procedure produces at least one audit observation regardless of outcome. The observation carries the violation identifier, the chosen candidate, the projected and actual impacts, the resulting integrity value, and the policy reference under which the procedure ran. Audit observations are signed by the redemption engine's authority and propagate through the standard observation channel to operator dashboards, governance modules, and downstream consumers.

Alternative Embodiments

In a co-located embodiment, the redemption engine runs as an in-process module within the agent and shares direct access to the state object and integrity descriptor. In a remote-authority embodiment, the engine runs in a governance enclave; the agent forwards integrity observations to the enclave, which evaluates and returns admissible candidates that the agent then commits locally through its admissibility gate. The remote-authority embodiment is suited to multi-agent meshes where redemption authority is centralized for consistency.

Candidate class composition may vary. Minimal embodiments support only rollback and audit-emit; richer embodiments add scope-narrow, evidence-gather, capability-quarantine, and substrate-migration as redemption candidates. The patent encompasses any embodiment whose candidate set is bounded, policy-declared, and admissibility-gated.

Embodiments differ in projection fidelity. A simple embodiment computes restoration impact by enumerating directly affected lineage entries; a sophisticated embodiment performs forward simulation of dependent skills and bound observations to estimate downstream propagation. The choice is a fidelity parameter, not an architectural one — both modes satisfy the structural requirement that candidates carry projected impact and that the admissibility gate evaluate the projection.

Some embodiments integrate redemption with cascade deactivation: a redemption procedure that scope-narrows a capability triggers cascade deactivation of dependent skills as a structural consequence rather than a separate event. Other embodiments treat the two as independent subsystems coordinating through the state object. Both compositions are supported.

Composition With Adjacent Primitives

The redemption engine reads from and writes to the semantic state object. Deviation analysis consumes the state object's lineage descriptor; restorative commits update the integrity descriptor and capability descriptor. The state object's invariants enforce that redemption operates only within its declared scope; out-of-scope writes are rejected by the state object regardless of the engine's intent.

Redemption composes with the admissibility gate. Every restorative candidate flows through the gate; redemption does not have a privileged commit path. This ensures that redemption itself cannot violate policy, because the gate evaluates redemption commits with the same rigor it applies to ordinary inference transitions.

Redemption composes with the cascade-deactivation subsystem. When a redemption candidate constricts the active capability set, the capability descriptor update triggers cascade evaluation in the dependency graph; dependent skills deactivate transitively, and the cascade is recorded as part of the same redemption observation. Operators see one credentialed event covering the violation, the redemption choice, and the structural consequences.

Redemption composes with audit and governance. Audit observations from redemption procedures feed the same operator dashboards, attestation streams, and regulatory feeds that consume ordinary admissibility events. There is no separate redemption audit channel; redemption is observable through the canonical audit surface.

Failure Modes and Their Containment

A redemption engine that is itself broken or compromised must not be able to amplify the violation it is supposed to remediate. The patent addresses this through three structural containments. First, the engine commits through the same admissibility gate as ordinary inference; a corrupted engine producing pathological candidates is rejected by the gate just as a corrupted reasoner producing pathological transitions would be. Second, the depth bound prevents recursive redemption from masking a deepening violation; an engine that cannot redeem within the depth bound must escalate. Third, the audit-emit terminal candidate guarantees that even when no admissible restorative candidate exists, the violation is surfaced through a credentialed observation rather than silently dropped.

Adversarial scenarios include malicious integrity observations crafted to provoke unnecessary rollback, and malicious revocation observations crafted to expand the redemption scope. Both are mitigated by authority scoping: the integrity subsystem accepts observations only from authorities within scope, and the engine's deviation analysis treats out-of-scope observations as suspect inputs that themselves trigger evidence-gather rather than rollback. The structural pattern is consistent: untrusted inputs do not directly drive remediation; they enter the same admissibility-gated evaluation that governs every other state mutation.

Prior-Art Distinction

Existing AI systems handle integrity violations through one of several inadequate mechanisms. Hard-halt approaches terminate the inference on any violation, sacrificing operational continuity for safety; they cannot scope-narrow or evidence-gather, only stop. Probabilistic self-correction approaches prompt the model to "reconsider" and produce revised output; these approaches lack determinism, lack lineage binding, and provide no guarantee that the revision actually addresses the violation. Rule-based filtering approaches reject the offending output and retry generation; they cannot reason about lineage scope, cannot bound their own activity, and cannot produce credentialed audit records.

The redemption engine differs structurally. Redemption is deterministic given policy and state; it operates on declared lineage scope rather than on outputs alone; it produces credentialed observations consumable by external authorities; it is bounded along scope, budget, depth, and time axes; and it commits through the same admissibility gate as ordinary transitions, eliminating any privileged self-modifying pathway.

No prior framework provides the combination of bounded structured remediation, lineage-scoped retraction, projected-impact admissibility, and credentialed audit emission as a single integrated subsystem. The redemption engine is structurally distinct from filtering, retry, and halt-based approaches.

Deployment Implications

Long-running autonomous deployments — companion AI, therapeutic agents, industrial-control systems, multi-day research assistants — accumulate integrity perturbations at a rate that hard-halt approaches cannot tolerate operationally. Halting on every threshold crossing produces unusable systems; ignoring threshold crossings produces unsafe ones. The redemption engine is the structural answer that makes long-running operation safe by allowing recoverable, audit-visible remediation rather than forcing a binary halt-or-continue choice.

Regulatory compliance regimes increasingly require not only that violations be prevented but that detected violations be remediated through documented procedures with auditable outcomes. The redemption engine satisfies this requirement structurally: every invocation produces a credentialed audit observation identifying the violation, the chosen candidate, the projected and actual impact, and the policy under which the procedure ran. Compliance reviews consume these observations directly rather than reconstructing remediation history from execution logs.

Disclosure Scope

The Cognition Patent discloses the redemption engine as a bounded, deterministic, credentialed remediation procedure invoked by integrity threshold crossing, operating on declared lineage scope, generating policy-declared candidate classes, projecting restoration impact, committing through the admissibility gate, and emitting credentialed audit observations. Any subsystem that exhibits this structural pattern — regardless of candidate class naming, projection fidelity, or co-located versus remote authority — falls within the disclosure.

The disclosure encompasses embodiments where redemption is tightly integrated with cascade deactivation, where it is integrated with substrate migration, where projection is shallow or deep, and where candidate ordering is policy-declared or operator-overridden. The structural property that distinguishes the disclosure is the bounded, lineage-scoped, admissibility-gated treatment of integrity remediation as a first-class subsystem, in contrast to ad-hoc retry, filter, or halt mechanisms.