Training-Inference Governance Integration

Mechanism

The integrated architecture treats every training event and every inference event as an admissibility evaluation against a common policy substrate. During training, each content class admitted into the corpus is annotated with the depth at which it was integrated, the memorization threshold permitted, and the licensing class under which it was admitted. These annotations are not discarded at the end of training; they are persisted in a governance manifest bound cryptographically to the model artifact.

At inference time, the manifest is loaded alongside the model weights. Each inference request is evaluated against the manifest before generation proceeds. A request that solicits content from an excluded class is rejected at the admissibility gate. A request that would require deep reliance on shallow-trained content is downgraded to a shallower response posture. A request that operates entirely within the certified envelope passes through without intervention.

The critical mechanical property is that training-time admissibility decisions are reified as data, not baked into model weights as implicit behaviors. A model's refusal to discuss an excluded class is not a learned aversion; it is a manifest-driven gate that exists independently of the weights. This separation is what permits inference-time governance to be reconfigured without retraining: the weights remain stable while the governance manifest is rewritten, re-signed, and reloaded.

The reverse property also holds. Skills that were demonstrably exercised during training, with admissibility evidence collected during the training run, do not need to be re-validated at inference time. The training-time certification carries forward as a signed credential. An inference request invoking a certified skill traverses the admissibility gate by presenting that credential rather than re-deriving admissibility from first principles on every call.

Operating Parameters

The governance manifest is structured as a set of admissibility tuples. Each tuple binds a content class identifier, an integration depth, a memorization threshold, a licensing classification, and the training-run identifier under which the admission occurred. Manifest entries are signed by the training authority and counter-signed by the model artifact registry, so any tampering with the manifest after release invalidates the binding.

Inference-time governance reads the manifest at model load and constructs the runtime admissibility envelope. The envelope is parameterized along three axes: the breadth of content classes the deployment is permitted to surface, the maximum depth of reliance permitted on each class, and the response postures available when a request falls outside the envelope. Operators tune these parameters at deployment time without touching the model weights.

The depth parameter is graded rather than binary. A class admitted at shallow depth during training may be referenced briefly during inference but cannot be the load-bearing source of an extended response. A class admitted at deep depth may be reasoned about extensively. The grading allows operators to deploy a single model into environments with very different exposure profiles by tightening or loosening the envelope.

Memorization thresholds enforced during training propagate as inference-time reconstruction guards. If a class was trained with a strict non-memorization threshold, the inference governance layer monitors output for n-gram overlap against representative samples of the class and intervenes when overlap exceeds a configurable bound. The threshold itself is carried in the manifest; the intervention behavior is configured at deployment.

Alternative Embodiments

One embodiment treats the manifest as a static artifact bound to the model at release. A second embodiment permits the manifest to be amended post-release, subject to a constraint that amendments may only narrow the envelope, never broaden it. The narrowing-only constraint preserves the property that inference-time governance is at least as strict as training-time governance.

A third embodiment composes manifests across federated training. When a model is fine-tuned by a downstream party, the downstream training run produces its own manifest, and the runtime envelope is the intersection of the upstream and downstream manifests. This permits delegation of fine-tuning without losing the upstream governance guarantees.

A fourth embodiment integrates the manifest with policy-as-code systems. Rather than encoding admissibility tuples in a bespoke format, the manifest is expressed as a set of policies in a declarative policy language, and the inference-time gate is a standard policy evaluator. This embodiment trades manifest specificity for tooling reuse.

A fifth embodiment uses the manifest to drive watermarking rather than gating. Excluded classes do not block generation but cause generations to be marked with a class identifier that downstream consumers can detect and act upon. This is appropriate in research deployments where strict gating would inhibit legitimate exploration but provenance must be preserved.

Composition

The integration composes with the broader admissibility primitive set. The same evaluator that gates training-data ingestion gates inference-time generation, and the same credential model that authorizes a training run authorizes an inference deployment. Composition with the capability-awareness primitive permits the runtime envelope to be additionally constrained by the deployed substrate's capabilities. Composition with provenance tracking causes every inference output to carry a chain back through the manifest to the training admissibility decisions that authorized the underlying skills.

Because the manifest is data, it composes with audit infrastructure trivially. An auditor querying the deployment receives the manifest, the runtime envelope, and the log of admissibility decisions at the inference gate. Reconstructing the lifecycle from training admission through inference enforcement is a matter of joining records across these three sources.

Prior Art Distinctions

Conventional training-data governance produces filtered datasets but discards the admissibility decisions once filtering completes. The trained model carries no record of why a class was excluded or at what depth it was admitted; the governance state lives in upstream documentation that is structurally disconnected from the deployment artifact. The integration described here makes the admissibility decisions first-class artifacts that travel with the model.

Conventional inference-time safety filtering applies guardrails at the prompt and output boundaries without reference to training-time decisions. A deployment may apply a content filter that is stricter or looser than the training filter, and there is no mechanical guarantee of consistency. The shared-admissibility-framework approach forces the inference envelope to be derivable from the training manifest, making consistency a structural property rather than an operational discipline.

Constitutional and RLHF approaches train aversions into weights. Reconfiguring such aversions requires retraining or fine-tuning. The manifest-driven approach reconfigures by rewriting data, not by perturbing weights, which is faster and produces deterministic governance shifts rather than emergent behavioral changes.

Practical Considerations

Operational deployments must account for the size of the manifest. A manifest covering a foundation-class model trained on hundreds of content classes at multiple depths produces tens of kilobytes of structured admissibility data, which is negligible relative to the model artifact but non-trivial relative to network protocols that exchange model metadata. Compression is straightforward because manifests contain extensive structural redundancy, and incremental update protocols are appropriate when manifests are amended at deployment time.

Latency at the inference gate is bounded by the cost of envelope evaluation, which is independent of model size and dominated by the lookup of the request's content class against the manifest's class table. For typical class-table sizes, the evaluation completes in microseconds and is invisible in the end-to-end inference budget. Reconstruction-guard evaluation is more expensive because it requires output monitoring; deployments tune the monitoring frequency to balance overhead against the strictness of the memorization threshold.

Auditing workflows benefit from the manifest's persistence guarantees. Because the manifest is signed and bound to the model artifact, audits can be conducted against archived model artifacts long after the original training run has concluded, and the admissibility decisions remain reconstructible. Regulatory regimes that mandate retention of training-data governance records are satisfied by archiving the manifest alongside the model.

Adversarial considerations include attempts to bypass the gate by crafting requests that appear to fall within the envelope while soliciting excluded content. The reconstruction-guard component mitigates this class of attack by monitoring outputs rather than relying solely on input classification, and the depth-grading component limits the load-bearing role of any single class in extended outputs.

Disclosure Scope

The disclosure encompasses the manifest format, the binding mechanism between manifests and model artifacts, the runtime envelope construction at model load, the admissibility gate evaluated on each inference call, the credential carry-forward from training-certified skills to inference invocations, and the reconfiguration mechanism that permits inference-time governance shifts without retraining. Variants over manifest representation, signing infrastructure, depth grading, and intersection composition for fine-tuned descendants fall within scope.

The subject matter recited herein is supported by the disclosures of U.S. Provisional Application No. 64/049,409, including the admissibility-envelope primitive, the credentialed manifest format, the carry-forward of training-time certifications into inference-time invocations, and the narrowing-only amendment discipline. The integration described above unifies training-phase and inference-phase governance under a single signed substrate so that an auditor reconstructing a deployment recovers, from the model artifact alone, the content-class admissions, the depth grades, the memorization thresholds, and the authority chain that authorized each, without depending on out-of-band documentation that has historically been the locus of governance drift.