AI-Mediated Curriculum and Progressive Capability Unlocking Using Semantic Performance States

Introduction: From Credentials to Demonstrated Readiness

Credentials are blunt instruments. A license, certificate, or role asserts that a capability exists, but says little about whether that capability is current, authentic, or safely exercisable in context. Once granted, credentials typically persist regardless of behavioral drift, skill decay, misuse, or identity substitution.

The architecture described here replaces static credentialing with a performance-native model governed by the same pre-execution principles described in the ethical enforcement layer. A semantic agent represents the participant and owns the performance state, curriculum state, and the rules for how evidence may change them. LLMs and other inference models can be invoked by that agent as bounded tools, but they do not possess authority. Authority belongs to cryptographically governed policy agents that gate whether a performance state may mutate and whether a capability may be unlocked, downgraded, or revoked.

Users progress through curricula designed to elicit behavior that reveals competence over time. Readiness is represented as a structured semantic performance state whose updates are expressed as typed deltas and admitted only through policy-gated mutation. Capabilities are unlocked only when admissible state transitions exist, and they are revoked when subsequent evidence no longer supports safe operation.

The result is a system in which inference is used to structure evidence, but governance determines what may execute.

1. Inference Without Authority: The AQ Division of Labor

LLMs were built for inference and language generation. They are useful for classifying interaction, summarizing sessions, extracting topics, producing explanations, and proposing structured representations of ambiguous data. They were not built for governance. They cannot provide enforceable guarantees about admissibility, and they cannot be the authority surface for high-stakes permissions.

In Adaptive Query, the semantic agent is the unit of authority. The agent controls what context exists, what evidence may be considered, what policies apply, and what mutations are even eligible to propose. The agent may call an LLM to produce a candidate interpretation or a candidate response, but the output is always treated as non-authoritative data. It cannot unlock capabilities, cannot mutate state, and cannot bypass policy. The only thing an LLM can do is return a proposal for the agent to validate.

This resolves the apparent tension between “ethics without inference” and “curriculum with inference.” AQ does not attempt to enforce ethics by interpreting meaning. AQ enforces admissibility structurally: typed declarations, scope, lineage, and cryptographically governed policy precedence determine whether an update or action can exist before execution.

2. Semantic Performance States as the Core Abstraction

The semantic performance state is a structured representation of demonstrated readiness. It is not a single score. It is a multi-field object with independently evolving dimensions such as cognitive mastery, behavioral stability, safety compliance, physical skill, communication clarity, and contextual reliability.

The performance state is owned by a semantic agent and evolves only through admissible mutation. Proposed updates are expressed as typed deltas referencing evidence, context, and evaluator lineage. Policy agents gate whether a given field may increase, decrease, decay, or remain unchanged, and under what constraints. This prevents performance from becoming an informal model output and preserves it as a governable object.

Because capability is a living property, the state supports growth and regression. Demonstrations can raise relevant fields, while drift, inconsistency, inactivity, or unsafe behavior can cause decay. This allows the system to maintain longitudinal truth without resetting identity or relying on one-time tests.

Each admitted mutation extends lineage and can be audited, challenged, or revoked under governance-defined rules.

3. Curriculum as a Governed Evidence Generator

Curriculum in this system is not merely instructional content; it is an evidence generator designed to elicit behavior that reveals readiness. Curriculum objects define tasks, challenges, and mastery criteria that are selected and sequenced by the semantic agent based on current performance state, policy constraints, and safety context.

The semantic agent performs the orchestration. It selects candidate curriculum elements, enforces pacing, binds each interaction to identity continuity, and determines what evidence is admissible to collect. When language mediation is needed, the agent may invoke an LLM as a callable tool to generate explanations, reflections, or phrasing. The agent also determines the exact context passed to the LLM, and the LLM never performs retrieval, never selects governing policies, and never decides progression.

The curriculum becomes adaptive without surrendering authority. Adaptation happens because the agent can propose different evidence-generating interactions, not because a model is granted the power to unlock capabilities.

4. Evidence Extraction, Retrieval, and Policy-Gated Mutation

The architecture separates three phases that conventional systems often conflate: non-authoritative inference, admissibility validation, and enforcement. During non-authoritative inference, the semantic agent may call one or more models to transform raw interaction into structured candidate evidence, such as topic tags, rubric markers, contradiction flags, safety events, or multimodal consistency indicators.

Retrieval is performed by the governed system, not the model. The semantic agent retrieves the relevant memory, curriculum rubric, and any permitted reference material from governed stores and then decides what subset may be provided to an inference call. The LLM does not pull from databases or decide what is relevant; it receives only what the agent has authorized for that call, and it returns only a proposal.

Candidate evidence may be cross-checked by additional models to reduce error, for example by testing internal consistency, detecting contradictions, or scoring uncertainty bounds. Cross-model checking remains inferential and advisory. Where inference is uncertain, the system records uncertainty and may require additional demonstrations rather than allowing irreversible mutations.

Mutation is gated separately. The semantic agent expresses a proposed performance-state delta as a typed declaration and submits it for admissibility. Policy agents and meta-policies enforce whether that delta is allowed under scope, lineage, and governance rules. The enforcement step is structural and cryptographic: it verifies signatures, precedence, and admissible mutation categories and then admits or rejects the update before any state change is applied.

5. Authenticity, Anti-Gaming, and Identity Continuity

Performance-based systems fail if they can be gamed. The architecture therefore binds evidence to identity continuity and rejects upgrades that cannot be attributed to the same evolving participant. This reduces transfer attacks, replay, coaching-by-proxy, and automated simulation.

Multimodal validation can be used where policy permits: text, audio, video, telemetry, interaction timing, and other signals may be evaluated for internal consistency and human-typical variation. These evaluations can use inference models, but they do not produce authority. They produce structured evidence and bounded uncertainty to be governed.

Where authenticity cannot be established within policy-defined bounds, the system does not guess. It defers progression, restricts capabilities, or requires additional validation, ensuring that safety-critical upgrades remain conservative without requiring constant human oversight.

6. Progressive Capability Unlocking as Pre-Execution Enforcement

Capabilities are unlocked by mapping performance-state fields to governed access rules. A participant who demonstrates stability and compliance may unlock higher-trust interactions. A participant who demonstrates physical proficiency and safety compliance may unlock higher-autonomy modes in embodied systems.

Unlocking is not a discretionary outcome of model inference. It is a policy-gated state transition that occurs before privileged execution. When a capability is requested, the system computes whether a valid execution is admissible under policy given current performance state, identity continuity, and contextual constraints. If admissible, the capability is enabled; if not, the request is denied or deferred without partial execution.

Unlocking is progressive and reversible. As performance improves, additional capabilities reliability become admissible. If regression or risk is detected, capabilities can be downgraded or revoked through governed mutation. Capability control becomes a living contract, not a one-time grant.

The same mechanism governs agent upgrades. A semantic agent representing a user or autonomous process may receive expanded delegation rights only when the policy-gated performance evidence supports that upgrade. This allows systems to grant more autonomy over time without surrendering governance to inference.

7. Certification as a Verifiable, Revocable Artifact

When defined milestones are reached, the system can issue certification artifacts representing validated capability. These artifacts are cryptographically signed, tamper-resistant, and bound to identity continuity and performance lineage.

Certification artifacts are described here as structurally definable outcomes of governed performance state, not as a claim of current issuance, standardization, or production deployment. Their form, lifecycle, and trust regime are expected to vary by domain and policy authority.

Unlike traditional certificates, these artifacts are backed by longitudinal evidence rather than a single test. They can be consumed by external systems to verify readiness without exposing private behavioral data. Verification can be scoped by policy: a verifier may confirm that a capability is currently admissible under a specified regime without receiving the underlying evidence stream.

Because capability is a living state, certification can be revocable or time-bounded. Tokens may remain valid only while underlying performance remains admissible or while revalidation rules are satisfied.

8. From Software to Embodied Systems

The same mechanism governs access across digital, interpersonal, and physical domains. In software applications, it controls feature access and tool permissions. In companion systems, it governs relational depth, communication modes, and safety-critical boundaries.

These domains are presented to illustrate the structural scope of the governance model, not to imply deployment maturity or universal applicability. The same admissibility principles can be defined across domains, while implementation remains context-specific and subject to policy, safety, and regulatory constraints.

In embodied systems, it determines whether a participant may engage higher autonomy levels, operate hazardous equipment, or override safety constraints. Privileged physical actions remain gated before execution by policy and admissible state, rather than being granted because a credential exists.

This unification allows the same governance bodies that define ethical policy to define readiness thresholds for embodied operation, including safety rules, decay rates, and revalidation requirements.

9. Why Evidence-Based Access Matters

Credentials assume permanence. Evidence acknowledges change. By grounding access in accumulated, validated performance, the system aligns with how real capability behaves: improving with practice, degrading without reinforcement, and varying across contexts.

This is especially important for safety-critical and autonomy-related systems where misuse, overconfidence, or identity substitution can cause harm. Evidence-based gating reduces risk while preserving autonomy because enforcement occurs structurally before privileged execution.

It also creates a clean governance surface. Institutions can define policies and meta-policies that determine what readiness means, how upgrades occur, how revocations are triggered, how disputes are handled, and what transparency requirements apply, without granting interpretive authority to inference models.

Conclusion: Using LLMs for Inference, Not Governance

AI-mediated curriculum and semantic performance states enable a shift from permission-based systems to readiness-based systems. Capabilities are granted because evidence supports them under policy, not because a credential exists.

The architecture remains AQ-faithful by separating inference from authority. Semantic agents orchestrate retrieval, curriculum, evidence collection, and admissibility requests. LLMs contribute bounded inference and language generation, but policy agents and meta-policies enforce what may mutate and what may execute before any privileged action occurs.

This sets up the next requirement: if capability and governance depend on longitudinal evidence, systems must bind that evidence to the same evolving human over time. Continuity-based biological identity provides that bridge.