Inference Function Evolution Under Aggregated Feedback

Mechanism

The architecture treats inference functions as governance-credentialed adaptation artifacts. Each function instance carries four bound elements: (a) an authoring authority identifier, (b) an operator-intent envelope describing the bounded scope within which the function is permitted to act, (c) a declared verification record summarizing aggregated observation outcomes accumulated during prior runtime, and (d) a credentialing chain — the sequence of parent signatures that authorize the present instantiation. The function is not a free-standing model; it is a node in a directed acyclic graph of mutations rooted at an operator-certified envelope.

A mutation is generated when an authoring authority, an automated optimizer, or a verification-aggregation step proposes a parameter delta. The delta is constructed against an explicit mutation budget — a declared bound on how far parameters may move per evolution step, expressed in the same units as the parent function's scope envelope. The proposed mutation is hashed together with the parent function's signature, the verification record that justified the change, and the mutation budget. The hash is signed by the proposing authority. The result is a candidate child function whose lineage is cryptographically verifiable: a recipient of the candidate can trace from the candidate's signature back through every parent to the operator-intent root.

A candidate child function does not enter production directly. The candidate is admitted to a sandbox under pre-activation certification. The sandbox executes the candidate against a battery of representative inference patterns — some drawn from the parent's verification record, some drawn from authority-supplied test vectors, some drawn from adversarial counter-examples accumulated by peers in the mesh. The sandbox emits per-pattern outcome observations that are themselves credentialed (signed by the sandbox executor and bound to the candidate's hash). An admissibility evaluator compares the sandbox observations to a declared admission criterion. If the candidate admits, the consuming deployment's signature certifies the candidate for activation; that signature, appended to the lineage, becomes the credentialing chain element under which the function operates in production.

Once activated, the function continues to emit verification observations under the standard mesh credentialing flow. Each observation is bound to the active function's hash. Aggregation across observations yields the function's running verification record. The aggregation is the input that drives the next round of mutation proposals: functions that consistently match observed behavior accumulate weight in the deployment's policy, functions that consistently miss are demoted out of the active set, and the demotion is itself a credentialed observation that propagates back to the authoring authority.

Promotion and demotion are themselves bounded operations. The deployment's active-function set is a credentialed object whose composition is governed by the same authority signatures that gate function admission. A promotion event is an observation, signed by the deployment, asserting that a candidate function has accumulated sufficient weight under the declared aggregation policy to enter the active set; a demotion event is the symmetric assertion in the opposite direction. Both events carry the function's lineage, the verification-record state at the time of the event, and the policy parameters under which the event was generated. A consumer auditing the active-function set therefore receives not only the present membership but the complete history of how each member arrived at its present state and how each demoted predecessor departed.

The mutation lineage and the verification-record observation flow together constitute an evolutionary record that is both forward-traceable (from any active function, the path of mutations that produced it can be replayed) and backward-traceable (from any operator-intent envelope, the set of presently-active descendants can be enumerated). The forward trace supports incident investigation: if an active function is observed to behave anomalously, the lineage permits identification of the mutation that introduced the anomaly. The backward trace supports compliance: if an operator-intent envelope is revoked or modified, the descendant set defines exactly which production functions must be re-validated or retired.

Operating Parameters

Mutation budgets are expressed as a bounded fraction of the parent function's parameter envelope per evolution step, typically in the range of one to ten percent per step for parameter-vector functions and as a small bounded edit distance for symbolic-form functions. Sandbox admission criteria are typically expressed as a minimum match rate against the parent's verification record (commonly ninety to ninety-five percent) combined with a maximum regression on adversarial counter-example patterns (commonly zero new failures). Lineage depth is bounded by a configured maximum, beyond which a candidate must be re-rooted by a fresh authority signature against the original operator-intent envelope; this prevents unbounded drift from the operator's certified scope.

Verification-record windows are typically measured in observation counts rather than wall-clock time, so that low-traffic functions accumulate sufficient evidence before promotion or demotion decisions are made. Demotion thresholds are typically set conservatively (a function is demoted only after a sustained drop below a declared accuracy floor across a minimum observation count) to prevent transient adversarial input from triggering inappropriate demotion. The aggregation function used to combine per-observation outcomes is itself a declared parameter of the deployment policy, and may be a simple weighted mean, a robust statistic such as a trimmed median, or a Bayesian update against a prior bound to the parent function's verification record.

Sandbox capacity is a configurable property of the consuming deployment: a deployment may run a single sandbox slot, in which case mutation candidates queue for serial admission, or many parallel slots, in which case multiple candidates can be evaluated concurrently against disjoint subsets of the verification battery. Slot allocation policy may prioritize candidates by their declared improvement over the parent's verification record, by their authoring authority's reputation, or by round-robin fairness among authorities. The slot-allocation policy is itself a declared parameter and is auditable through the credentialed observation stream.

Alternative Embodiments

In a first alternative embodiment, mutation proposals are generated entirely by the authoring authority and the consuming deployment performs only sandbox admission; the deployment never proposes mutations of its own. This embodiment is appropriate for high-assurance contexts in which the authority retains full control over the function's evolution trajectory.

In a second alternative embodiment, mutation proposals are generated by an automated optimizer co-located with the consuming deployment; the optimizer observes the local verification record and proposes parameter deltas that improve local accuracy. The proposals are countersigned by the authoring authority before sandbox admission, preserving the lineage property while permitting deployment-local adaptation.

In a third alternative embodiment, mutation proposals are generated collectively by a quorum of peer deployments, each contributing a partial gradient against its own verification record. The quorum-aggregated mutation is signed by a threshold of peer signatures and admitted to sandbox by each consumer independently. This embodiment supports federated evolution across a fleet of deployments operating under a shared operator-intent envelope.

In a fourth alternative embodiment, the inference function is symbolic rather than parametric; mutations are bounded edits to a symbolic form (rule additions, rule deletions, predicate substitutions) and the mutation budget is expressed as a maximum edit distance rather than a parameter-norm bound. Sandbox admission and lineage signing operate identically.

Composition With the Wider Architecture

Inference function evolution composes with the operator-intent credentialing layer that defines the root envelope, with the mesh-broadcast observation layer that distributes mutation proposals and verification records, with the sandbox pre-activation layer used for runtime adaptation artifacts of all kinds, and with the demotion-and-re-credentialing layer that retires functions whose verification records have decayed. The structural property is that no separate evolution-management subsystem is required: function evolution reuses the same credentialing, distribution, and admission machinery that handles all other governance-bound adaptation in the architecture.

The composition extends to adversarial-aware inference. When peer deployments emit adversarial counter-example observations, those observations enter the verification-record aggregation that drives the next round of mutation proposals. The function's evolution trajectory therefore tracks adversarial co-evolution at the tempo of the mesh distribution layer rather than at the tempo of human-led re-deployment cycles.

Distinction From Prior Art

Conventional online-learning systems update inference function parameters continuously against streaming observations, but they do not bind each update to an operator-intent envelope and they do not enforce a signed lineage from a certified root. Conventional model-versioning systems track which model is deployed where, but they do not gate deployment on sandbox admission against an admissibility criterion derived from the parent's verification record. Conventional federated-learning systems aggregate gradients across a fleet, but they do not require each aggregated update to remain inside a per-step mutation budget bounded by the operator-intent envelope, and they do not enforce a maximum lineage depth beyond which re-rooting against the original operator certification is required.

The disclosed architecture combines four properties not jointly present in prior art: bounded per-step mutation budgets expressed in the same units as the parent envelope, signed lineage rooted at an operator-intent certification, sandbox pre-activation against an admissibility criterion derived from the parent's verification record, and lineage-depth bounding that forces periodic re-certification against the original operator scope.

Disclosure Scope

This disclosure covers the mechanism by which an inference function bound to operator intent evolves through bounded, signed mutations under sandbox admission and aggregated verification feedback. The disclosure includes the lineage data structure, the mutation-budget enforcement, the sandbox admission criterion, the verification-record aggregation, the promotion and demotion event structure, the forward and backward traceability properties, and the alternative embodiments described above. The disclosure does not claim any specific machine-learning model architecture, any specific cryptographic signature scheme, or any specific aggregation function; those elements are implementation choices left to the practitioner. The disclosure applies to inference functions in any domain in which an operator-intent envelope can be expressed, including but not limited to perception, classification, prediction, planning, anomaly detection, and decision support; the architectural property is independent of the inference task and depends only on the lineage, budget, and admission machinery described.