Mechanism

Predictive mutation verification forecasts expected successor states and bounds acceptable deviation so that drift or compromise can be detected prior to full slope discontinuity. In the disclosed memory-native identity substrate, a device or agent expresses identity as a trust slope: the cumulatively validated sequence of Dynamic Agent Hashes (DAHs) or Dynamic Device Hashes (DDHs) formed by successive verifiable identity mutations, rather than a static credential. Continuity validation checks whether a presented successor is a valid descendant of the last trusted state on that slope. Predictive verification adds a forward-looking layer that anticipates the next step and surfaces behavioral drift early, classifying a claim as predicted-trajectory consistent or as a deviation before the full continuity comparison is performed.

The mechanism is built around a forecasting engine that operates over a history buffer comprising prior validated dynamic identities, mutation classes, and inter-step cadence statistics. From this buffer it produces forecast parameters for near-future epochs: a predicted timing window, a most-likely mutation class with a small set of alternates, and an acceptance envelope for the next step on the trust slope. A presented identity claim is compared against these forecasts. Claims consistent with the predicted trajectory proceed to normal continuity validation; claims outside the predicted bounds are evaluated as behavioral drift and routed to policy.

The Forecasting Engine

The forecasting engine operates over the history buffer of prior validated dynamic identities, the mutation classes recorded for each step, and inter-step cadence statistics. Two components consume this buffer. A cadence estimator maintains an exponentially weighted moving average of inter-step intervals together with a variance term, predicting the expected timing window for the next successor. A role-transition model encodes a finite-state transition matrix implementing a first-order Markov chain, in which each row specifies the conditional probabilities of transitioning from a current mutation class, semantic role, or scope tag to a successor class. The matrix is row-stochastic, with nonnegative entries that sum to one. From these predictions the engine derives a most-likely mutation class and a small set of alternates.

Forecast parameters are continuously updated from the history buffer as new validated steps arrive, allowing the acceptance envelopes to tighten or relax adaptively with observed behavior while preserving sensitivity to genuine role changes indicated by the transition model. The engine relies only on locally retained materials in the history buffer, consistent with the substrate's memory-resolved design, in which validation does not depend on external authorities, persistent keypairs, or synchronized registries.

The Expected-Token Generator and Acceptance Envelope

The predicted attributes drive an expected-token generator that forms a neighborhood envelope for the next step on the trust slope. The generator is compatible with both identity sources. In a local-state embodiment, it projects recent local-state feature vectors into a stability-tuned space and computes a predicted extractor token together with an acceptance envelope defined as a Hamming ball around that predicted token, that is, all tokens whose Hamming distance from the predicted token is within a radius calibrated to observed intra-role variation. In a hardware-anchor embodiment, it predicts salt freshness and cadence bounds for the next per-epoch derivation, yielding an acceptance window over salt reuse and timing. In a hybrid embodiment, both the Hamming-ball envelope and the salt-cadence window are produced, and both must be satisfied during verification.

The forecasting engine emits an expected-identity set that may include one or more predicted successors, expressed as predicted DAHs (or predicted DDHs for devices), together with their acceptance envelopes and expected inter-step timing. This set is the forward forecast against which an incoming claim is evaluated, and it is the artifact that lets the substrate distinguish an expected next step from a deviation before performing the full continuity comparison.

Comparator and Drift Detection

Upon receipt of a presentation bearing a claimed identity, whether the header DAH or the embedded sender DAH described elsewhere in the disclosure, a comparator evaluates whether the claim lies within the predicted envelope and within the cadence window. If it does, the claim is classified as predicted-trajectory consistent and accepted subject to normal continuity checks. The predictive layer does not replace continuity validation; it precedes and informs it.

Deviations are evaluated as behavioral drift. A drift detector classifies out-of-envelope claims by type, including cadence anomalies, where the claim is early or late relative to the cadence estimator; semantic divergence, where the mutation class is unexpected relative to the role-transition model; and token-space deviation, where the claim exceeds the acceptance envelope in the local-state embodiment. Classifying the deviation by type lets policy respond proportionately rather than treating every deviation as an outright break in continuity.

Policy Outcomes

When a claim is classified as drift, the disclosed policy actions are trust-score adjustment, a requirement for supplemental proof such as a short bounded window from the sender, or quarantine pending corroboration from peers or anchors. These are the outcomes the specification enumerates. Drift does not by itself constitute acceptance or rejection of the underlying identity claim; it is a signal that drives one of these policy responses while the claim is held, corroborated, or degraded in trust according to local policy.

Because the mechanism is agnostic to the unpredictability source, the comparator enforces the envelope appropriate to the embodiment in use: salt freshness and cadence windows predicted by the cadence estimator in the hardware-anchor embodiment, neighborhood envelopes computed from stability-tuned projections in the local-state embodiment, and both simultaneously in the hybrid embodiment, where both must hold for the claim to be accepted.

Composition with Delayed and Sparse Validation

Predictive verification composes with delayed or sparse validation. When a verifier lacks an up-to-date anchor, it can still use the expected-identity set to triage incoming traffic: claims far outside the envelopes are rejected or quarantined immediately, while near-boundary claims are held until a checkpoint or short proof window arrives. Once checkpoint material is available, standard replay from the last trusted state confirms or refutes the prediction without reliance on external registries or static credentials.

This composition lets the predictive layer act as an early triage filter under adversarial load or intermittent connectivity, deferring the cost of full replay to the cases that warrant it while still flagging clearly anomalous claims at once. The behavior is consistent with the broader substrate, which is designed to operate using only locally available materials, anchors, and policy-bounded disclosures.

Distinctions

The predictive layer is described in the specification as supplementing continuity checking, not replacing it. A claim that is predicted-trajectory consistent still proceeds to standard continuity validation, and a claim that drifts is not silently rejected but routed to a defined policy outcome of trust-score adjustment, supplemental proof, or quarantine. The forecast is constructed entirely from locally retained history, the cadence estimator's exponentially weighted moving average and the role-transition model's row-stochastic Markov matrix, so any node holding the relevant history buffer can form the same expected-identity set without contacting an external authority.

The mechanism's purpose, as disclosed, is early and local detection of compromise or unauthorized mutation while maintaining interoperability with the trust-slope continuity checks disclosed elsewhere in the specification. It surfaces drift before full slope discontinuity occurs, giving the substrate a window in which to triage, corroborate, or degrade trust rather than forcing a binary accept-or-reject decision at the moment of discontinuity.

Disclosure Scope

Predictive mutation verification and behavioral drift detection, comprising a forecasting engine operating over a history buffer of prior validated identities, mutation classes, and inter-step cadence statistics; a cadence estimator maintaining an exponentially weighted moving average of inter-step intervals and a variance term; a role-transition model encoded as a row-stochastic first-order Markov transition matrix; an expected-token generator that forms a neighborhood acceptance envelope, defined as a Hamming ball around a predicted extractor token in the local-state embodiment or a salt-freshness and cadence window in the hardware-anchor embodiment; an expected-identity set of predicted successors with their envelopes and timing; a comparator that classifies a claim as predicted-trajectory consistent or as drift; and a drift detector whose out-of-envelope classifications drive trust-score adjustment, supplemental-proof requirements, or quarantine, is disclosed in U.S. Application No. 19/388,580. This article describes that disclosed mechanism.

The disclosure is agnostic to the unpredictability source: hardware-anchor, local-state extractor, or a hybrid of both, with both envelopes required to hold under the hybrid embodiment. The predictive layer composes with delayed and sparse validation, allowing triage of incoming claims against expected-identity sets even when a current anchor is unavailable, with near-boundary claims held until checkpoint or bounded-proof material permits confirmation by standard replay from the last trusted state.