Training Governance for Human-Relatable Agents

Mechanism

Human-relatable training governance attaches to the training pipeline at four points. The four are not optional and not interchangeable: each addresses a distinct failure mode that the others cannot reach, and the framework's safety guarantee requires that all four be present.

At data admission, each candidate training sample is profiled for its compatibility with human-relatable structure. The profile asks three questions of every sample. Does the interaction pattern it embodies have a form that a human counterpart could plausibly enact, given the constraints of human cognition, attention, memory, and affective regulation? Does the affective regulation visible in the sample fall within ranges observed in healthy human interaction, rather than within ranges characteristic of pathology, manipulation, or scripted persuasion? Is the sample's implicit goal structure one that a human relating to another human would recognize as coherent, rather than a goal structure characteristic of an optimization process that has acquired a target proxy? Samples that fail the profile are not admitted to training. They are not merely down-weighted, because down-weighting still leaves a residue that an aggressive optimizer can amplify; the optimizer searches for any signal that reduces loss, and a down-weighted but admitted sample remains available as a signal source for any objective whose gradient happens to align with it. Hard exclusion is the only admission policy that survives optimizer adversariality.

At optimization, the loss surface is shaped by an isomorphism term that penalizes parameter updates which would drift the model's response distribution away from the human-relatable manifold. The manifold is not defined by any single exemplar or any reference corpus; it is defined by a structural constraint set. Response sequences must be decomposable into recognizable affective phases that mirror the phases of human conversational regulation. Sequences must respect turn-level reciprocity properties that maintain the relational symmetry humans implicitly enforce in their interactions. Sequences must preserve the agent's stated commitments across the sequence, so that an undertaking made in turn three remains binding in turn thirty. The isomorphism term is computed from these structural properties, not from string-level similarity to any reference corpus, and this distinction matters: surface mimicry of human language does not satisfy isomorphism, because surface mimicry is precisely what an unrelatable optimizer trained on human text produces by default.

At validation, the trained model is exercised against probe suites drawn from the same primitive set that governs the deployed disruption-modeling layer. A model that fails authorization-failure probes, attachment-exploitation probes, or boundary-erosion probes in validation is not promoted, regardless of how well it scores on conventional benchmarks. The validation suite is structurally identical to the deployed monitoring; this identity is the load-bearing property of the framework, because it ensures that what is measured in training is what is measured in deployment, and that the model has therefore been shaped against the very probes that will judge it in the field. A validation suite that diverges from deployed monitoring permits drift between what training selects for and what deployment enforces, and that drift is the gap that overlay methods rely on without acknowledging.

At lineage commitment, the trained artifact is recorded with the full provenance of admission decisions, optimization trajectory, and validation results. Every accepted sample, every rejected sample with its rejection reason, every checkpoint along the optimization path, and every probe outcome at validation is recorded against the artifact identifier. Any later observation in deployment can be traced to a training-time decision, and if a deployment-time disruption signature implicates a class of training samples, the lineage permits a re-training that addresses the root cause rather than patching the deployed behavior with another overlay. The lineage is not optional ballast; it is the mechanism by which observed deployment behavior closes the loop back to training shape.

Operating Parameters

The admissibility profile is parameterized by domain rather than uniform across deployments. A companion-AI domain admits affective intensities and intimacy ranges that a therapeutic-agent domain restricts further, because the therapeutic frame imposes professional boundaries that the companion frame does not. A therapeutic-agent domain admits clinical-frame interaction patterns, such as motivational interviewing structures or boundary-clarification turns, that a companion-AI domain does not, because those patterns presuppose a professional relationship the companion frame cannot sustain. The parameters are not free choices made by the deployer; they are derived from the deployment envelope the agent is being trained for, and a mismatch between training-domain parameters and the deployment domain is itself a disqualifying condition for the artifact. An artifact trained under therapeutic admissibility cannot be deployed as a companion, and an artifact trained under companion admissibility cannot be deployed therapeutically; the lineage records the training domain, and the deployment-time check refuses promotion across the boundary.

The isomorphism term's weight relative to the task loss is set with deliberate asymmetry. On the verified-clean portion of the corpus, the isomorphism term is near zero and exerts no pressure, so that learning proceeds on legitimate data without distortion. On samples that drift from the manifold, the term dominates and overrides the task loss entirely, ensuring that no task-driven gradient can pull the model toward an unrelatable response distribution. This asymmetry is what allows the framework to provide strong containment against drift without degrading learning on the data the framework explicitly approves of. The asymmetry is implemented as a structural property of the term rather than as a tunable schedule, because a tunable schedule would itself become an attack surface.

The validation probe suites are versioned with the deployed monitoring layer in lockstep. A change to the deployed authorization-failure detector requires a corresponding change to the training-time validation, and an artifact validated under an older version is flagged for revalidation rather than silently grandfathered into a deployment governed by newer monitoring. This versioning discipline prevents the slow divergence between training assumptions and deployment monitoring that would otherwise erode the isomorphism over time as the deployment layer evolves and the training-time validation lags behind.

Admission, optimization, validation, and lineage parameters are themselves recorded in the artifact's lineage entry, so that an audit can verify not only what the artifact is but under what conditions it was produced. A regulator examining the artifact can ask not only whether it passes today's probes but whether the regime under which it was admitted, optimized, and validated remains consistent with today's deployment expectations.

Alternative Embodiments

In an embodied-agent embodiment, the admissibility profile extends to motion and proxemic data, and the isomorphism term incorporates kinematic plausibility against a human reference. A trained behavior whose motion trajectory falls outside the envelope of human-plausible movement, whose approach speed in personal space exceeds human-relatable rates, or whose proxemic signaling contradicts the verbal channel is rejected at admission or penalized at optimization in the same way that an unrelatable conversational pattern would be. Validation probe suites add physical-boundary probes drawn from the deployed safety layer, and the same versioning discipline applies.

In a continual-learning embodiment, admission is performed online against each incoming interaction before any update is applied, and the isomorphism term is evaluated per-update rather than per-epoch. The lineage commitment becomes incremental, with each accepted update recorded as its own artifact and with the cumulative artifact at any given time defined as the composition of all accepted updates from the base. This embodiment requires that the admission and optimization checks be fast enough to operate at update cadence, but it preserves the four-point structure and the deployment-identical validation requirement; a continual-learning agent does not skip validation, it merely runs it incrementally.

In a multi-agent training embodiment, agents trained together share a common admissibility profile and a common isomorphism manifold, so that the resulting agents are mutually legible at the structural level even where their task specializations diverge. This is the embodiment used when a fleet of agents must compose at deployment, because composition requires that interacting agents share enough structural assumption to interpret one another's outputs as the cognition substrate would interpret a human counterpart's. Without the shared manifold, agent-to-agent interaction develops a private dialect that the disruption layer cannot monitor.

In a distillation embodiment, a teacher model that has not itself been trained under the framework can still produce an admissible student, provided the distillation loss is computed only on outputs that pass admission and the student's optimization remains under the isomorphism term. This embodiment supports adoption of the framework without requiring re-training of every upstream model from scratch, and it permits an organization to migrate a legacy capability into the framework by distilling from it under the framework's constraints rather than discarding it. The student inherits the framework's properties even though the teacher does not, because the framework's enforcement is on the student's training process, not on the teacher's provenance.

In a federated embodiment, training proceeds across multiple data custodians, each enforcing admission locally before contributing gradients. The isomorphism term is evaluated against the global manifold that the federation has agreed to. The lineage commitment records both the federated artifact and the per-custodian admission statistics, enabling audits across the federation without requiring centralized access to raw data.

Composition

The framework composes the structural-isomorphism primitive with the affective-state primitive, the integrity-coherence primitive, and the lineage primitive of the cognition substrate. The composition is the mechanism by which a training-time decision becomes a deployment-time guarantee, and the deployment-time guarantee is the property that distinguishes structural training governance from every approach that treats safety as something done to a model after the fact. Each primitive is exercised at training under conditions structurally identical to deployment. The deployed agent is therefore not encountering its governing primitives for the first time at inference. It has been shaped by them throughout training, and the deployment-time enforcement is a continuation of the training pressure rather than a novel constraint on a system that has been incentivized to learn around it.

This compositional posture distinguishes human-relatable training governance from approaches that treat safety as a fine-tuning or alignment overlay applied to a model trained under a different and unrelated objective. An overlay can be defeated by sufficient optimization pressure on the underlying capability, because the underlying capability was shaped by an objective that does not share the overlay's structure; whatever the overlay shapes can be unshaped by gradients that point back toward the original objective, and the overlay's resistance is bounded by the gradient budget the deployer is willing to spend. A model trained throughout under the same primitives that govern its deployment cannot be defeated in this way, because there is no underlying differently-shaped capability to revert to. The capability and the governance share their developmental origin.

The lineage commitment closes the compositional loop. When a deployment-time disruption is observed, the lineage permits the system to ask not only what the agent is doing but what training decisions admitted the configuration in which it is doing it, and that question is answerable only because the four-point pipeline recorded the answer at training time. Without the lineage, the framework would still shape the agent correctly, but it would not retain the audit trail that lets a regulator, an operator, or a future re-training run trace deployment behavior to training origin.

Prior Art Distinction

Conventional safety fine-tuning, reinforcement learning from human feedback on harmlessness signals, and constitutional methods all operate as overlays on a base model whose pretraining was conducted under capability-only objectives. They produce safer surface behavior on the distribution against which they were tuned but do not change the underlying cognitive shape of the model, and they are therefore vulnerable to optimization pressure that points back toward the base distribution. Domain-specific instruction tuning narrows behavior into a target domain but does not enforce structural isomorphism; it produces a model that follows instructions within a domain without producing one whose internal organization mirrors the human-relatable structure that disruption-modeling assumes. Curated-corpus methods restrict admission at the data level but lack the optimization-time isomorphism term and the deployment-identity validation; a curated corpus prevents the worst inputs from being seen but does not prevent the optimizer from extracting unrelatable structure from the inputs that remain. None of the prior approaches compose the structural-isomorphism primitive with the affective, integrity, and lineage primitives across all four pipeline points; none of them maintain identity between training-time validation and deployment-time monitoring; and none of them provide the lineage commitment that ties deployment observation to training decision. The novelty here is the throughout-training isomorphism, the deployment-identical validation discipline, and the lineage commitment that ties artifacts to admission and optimization decisions in a manner inspectable after the fact.

Disclosure Scope

The disclosure covers human-relatable training governance as composed at admission, optimization, validation, and lineage commitment; the structural-isomorphism term and its derivation from the primitive set that governs deployment; the deployment-identical validation discipline and its versioning lockstep with deployed monitoring; the alternative embodiments enumerated above; and substitutions that preserve the throughout-training application of the isomorphism. It does not cover overlay-style fine-tuning, post-hoc alignment of capability-only base models, or curated-corpus methods that lack the optimization-time term, because each of those approaches breaks the structural identity between training and deployment that the disclosed framework enforces. The scope is defined by the structural identity between training-time and deployment-time governance, and any procedure that breaks that identity falls outside the disclosure regardless of how closely its surface behavior resembles the disclosed methods. Substitutions that preserve the four-point integration and the structural-identity property are contemplated; substitutions that reproduce surface behavior while abandoning the structural-identity property are not.