Profiles

A. Core Schema

Core Semantic Agent Profile (v1.0)

• An AQ semantic agent MUST expose, at minimum, the canonical fields: intent, context, memory, policy reference, mutation descriptor, and lineage.
• Any mutation, delegation, or propagation of an AQ semantic agent MUST be gated by evaluation of the policy reference field at runtime, and such operations MUST be deterministically permitted or denied based on that validation.
• Policy evaluation for an AQ semantic agent MUST occur prior to intent resolution or mutation execution, and on violation the platform MUST quarantine or roll back the agent state to enforce ethical policy constraints at runtime.
• An AQ-compliant runtime MUST support deployment of fallback or partial agent structures in environments lacking native memory-aware substrates, and MUST defer synchronization of the memory field until reconnection to memory-native zones.
• Each semantic agent object MUST persist execution memory, mutation outcomes, and lineage across substrate transitions via serialization, fallback scaffolding, or partial agent emulation so that semantic continuity is maintained.
• Mutation events on AQ semantic agents MUST be described in mutation descriptors and governed by cryptographically validated policy references.

Semantic Agent Structural Schema & Fallback Integrity Profile (v1.0)

• A semantic agent MUST include canonical fields: intent, context, memory, policy reference, mutation descriptor, and lineage; missing fields MUST trigger default inference, scaffolding, or delegation logic.
• Semantic agents MUST be structurally self‑validating under a cognition‑compatible schema that supports memory continuity, policy‑bound behavior, and traceable semantic evolution.
• The policy reference field MUST govern the conditions under which mutation descriptors may be invoked, restricted, or overridden.
• The lineage field MUST enforce continuity of semantic identity and MUST guarantee deterministic behavioral traceability across generations.
• Validation engines MUST evaluate field presence, logical coherence, and fallback scaffolding rules before permitting mutation, propagation, or delegation.
• Fallback inference logic MAY use lineage‑based intent resolution, policy delegation to upstream agents, or environmental heuristics to reconstruct missing fields.
• Partial semantic agents MUST be permitted to participate in semantic networks when structurally scaffolded and MUST be validated for mutation, delegation, or task participation.
• Semantic lineage graphs MUST be constructed from lineage references embedded in agents, and MUST be maintained to enforce semantic continuity, mutation traceability, and policy compliance.
• Validation engines MUST support partial‑agent resolution and fallback typing based on available field combinations.
• Platforms MUST quarantine or remediate agent objects that fail structural validation or whose fallback inference cannot yield a compliant semantic structure.
• Each canonical field MUST be individually addressable, machine‑readable, and serializable across stateless transport environments.
• Mutation descriptors MAY include time‑based decay triggers, feedback‑driven mutation logic, or trust‑scoped adaptive pathways, and MUST remain bound to schema‑level policy.
• Policy references embedded in agents MUST govern network‑level mutation eligibility, lineage authorization, and delegation rights.
• Environmental inference modules MAY generate syntactically compliant fallback agents when validation fails, provided they maintain lineage continuity and structural coherence.
• Structural coherence among memory, mutation, and lineage fields MUST enforce deterministic auditability of semantic evolution events.

Integrity & Coherence Profile (v1.0)

• AQ-compliant platforms MUST evaluate policy references and integrity constraints prior to any agent mutation, delegation, propagation, or execution within a new trust zone.
• Validator processes MUST deterministically permit or deny behavior without reliance on centralized authorization or purely post-execution filtering.
• Scoped trust zones MUST define and enforce zone-specific governance policies, which MUST be validated before agent execution, propagation, or semantic zone migration; for mutation-sensitive or ethics-governed behaviors, implementations MAY require quorum-based consensus.
• Implementations SHOULD compile semantic execution lineage graphs from memory-trace records, mutation justification metadata, and signed policy audit trails to support integrity analysis and forensic review.
• Integrity validators MUST verify semantic lineage and behavioral trust slopes across execution cycles using entropy-resolved signals derived from agent memory, context, and substrate-local entropy.

B. Runtime Substrate and Protocols

Memory‑Native Protocol Execution Profile (v1.0)

• Agents transmitted within a memory‑native protocol environment MUST include: a unique identifier, a payload, a memory field, a transport header, and a cryptographic signature.
• The memory field of each agent MUST contain verifiable lineage, access logs, and policy references, and these MUST govern routing, mutation, and consensus behavior.
• Each agent MUST operate as a self‑governing protocol operand, with its transport header specifying constraints such as trust scope, TTL, semantic class, latency sensitivity, or quorum priority.
• Distributed nodes MUST maintain a local trust graph derived from memory‑field evaluations, and MUST dynamically score routing candidates during transmission.
• The protocol stack MUST include a routing layer, indexing layer, and consensus layer whose execution behavior is determined by metadata embedded inside each received agent.
• Nodes MUST modify routing or mutation behavior in response to received health agents carrying congestion, trust volatility, semantic entropy, or cache‑degradation metrics.
• Health agents MAY include entropy thresholds that MUST trigger index splitting or semantic reclassification.
• Memory‑native protocol stacks MUST execute over stateless transports (TCP/IP, HTTP, mesh relay, DTN, WebRTC) without requiring persistent session state.
• Nodes MUST update their local trust graph based on trace outcomes embedded in received agents and MUST adjust trust scores based on congestion, latency variance, policy‑violation frequency, and propagation entropy.
• Dynamic routing protocols (DRP) MUST re‑score candidate transmission paths using updated trust graphs in real time.
• Dynamic indexing protocols (DIP) MUST form soft‑index containers using entropy anchors computed from agent memory fields, and MUST NOT depend on human‑readable alias systems.
• DIP implementations MUST create, split, merge, or promote index anchors using mutation‑trajectory entropy, lineage density, and access‑distribution patterns.
• Agents MUST support cognition‑compatible payloads and MUST permit structure mutation in response to memory‑field policy references or execution context.
• Agents referencing policy‑agents MUST enforce quorum rules, mutation‑eligibility criteria, and role definitions during execution.
• Distributed nodes MUST verify agent signatures, parse transport headers, evaluate access logs and policy references, and apply memory‑field constraints prior to forwarding or mutation.
• Forwarding decisions MUST be determined by dynamic routing protocol logic and memory‑field constraints, and nodes MUST append trace logs before forwarding.
• Consensus operations MUST be triggered when memory fields indicate a proposed mutation, and votes MUST be trust‑weighted according to the referenced policy‑agent.
• Read / write / mutation authorization MUST derive solely from memory‑field policy references and MUST NOT rely on external session state.
• Access‑log evaluation MUST consider execution histories of neighboring nodes including success rates, policy‑violation frequency, and responsiveness.

Distributed Semantic Query Execution Profile (v1.0)

• Nodes in an AQ‑compliant distributed system MUST verify the cryptographic signature of each semantic agent prior to execution.
• Nodes MUST evaluate the agent’s memory field to determine prior trace outcomes, mutation lineage, policy references, and governance constraints.
• Nodes MUST determine authorization, deferral, or rejection decisions based on the agent’s context block and embedded policy, and MUST append signed trace outcomes reflecting such decisions to the memory field.
• Semantic agents MUST propagate as persistent memory‑bearing objects without centralized orchestration, and MUST modify their intent, scope, or transport metadata based on accumulated memory‑field feedback.
• Semantic agents MAY omit structural fields, provided fallback logic, refinement behavior, or self‑composition can resolve missing fields under schema rules.
• Semantic agents MAY spawn sub‑agents that inherit lineage and intent context, and MUST aggregate signed outcomes recursively from spawned agents.
• Semantic agents MUST record intention changes, policy‑parameter adjustments, and mutation‑based transformations directly within their memory fields.
• Execution MUST occur across heterogeneous environments (async networks, distributed memory graphs, centralized compute) without dependence on specific runtime infrastructure.
• The memory field MUST track repeated failure events, and agents MUST adjust routing, trust parameters, retry windows, or intent classifications in response.
• Nodes MUST compute trust scores, evaluate mutation legitimacy, and apply policy constraints using only the agent’s memory field, without referencing centralized identity systems.
• Agents MAY persist as polling agents, reactivating under environmental or semantic triggers, and MUST self‑terminate upon satisfying outcome or violating policy constraints.
• Memory‑linked agents MUST support composable chaining for multi‑step reasoning, delegated subqueries, and long‑horizon workflows across distributed execution graphs.
• Policy enforcement MUST be performed locally at each node using agent‑resident policy references and context metadata, and results MUST be recorded as signed trace entries.
• Previously executed agents MAY be re‑used when semantic similarity, trust context, and policy lineage match local validation requirements.
• Agents MUST dynamically alter mutation or routing behavior in response to environmental signals such as entropy shifts, node‑health degradation, or network‑health protocol feedback.
• Failed agents MUST be re‑routed to alternate nodes using semantic‑similarity scoring between intent vectors and prior successful query profiles.
• Distributed multi‑agent task graphs MUST support delegation references and memory‑lineage chaining to enable collaborative refinement and mutation across independent agents.

Two‑Stage Dynamic Hash Authentication & Agent–Device Entanglement Profile (v1.0)

• Sender agents MUST generate dynamic agent hashes (DAH_t) as successors of prior trusted hashes (DAH_(t−1)) using update rules that include at least one unpredictability contribution and a volatile salt.
• Recipients MUST reconstruct expected successor candidates using locally retained trust‑slope state and MUST validate DAH_t against policy‑bounded continuity parameters before decryption.
• Sender agents MUST derive symmetric encryption keys from a recipient’s dynamic identity (DAH_R or DDH_R) and MUST NOT include these keys in transmitted messages.
• Encrypted payloads MUST embed a contemporaneously computed sender dynamic hash (DAH_S), and recipients MUST validate DAH_S after decryption against a reconstructed trust slope.
• Two‑stage validation MUST occur: header‑level validation of DAH_t and payload‑level validation of DAH_S; failure of either MUST cause message rejection without external registry lookup.
• Systems implementing this profile MUST NOT rely on persistent private keys or certificate authorities for authentication.
• Unpredictability contributions MAY include keyed derivations from hardware anchors, extractor outputs over stability‑tuned local state vectors, or concatenated update‑rule components.
• Entropy anchors MUST be rotated upon staleness, with forward links recorded to bind prior terminal identities to new epochs, and expired identifiers MUST be rejected except for bridging proofs.
• Recipients MUST forecast successor identities and acceptance envelopes; out‑of‑envelope successors MUST trigger trust degradation, supplemental proof requests, or quarantine.
• Symmetric keys MUST be derived via domain‑separated key derivation functions keyed by DAH_R or DDH_R, and such keys MUST expire with the recipient epoch.
• When a sender cannot derive keys from DAH_R or DDH_R, fallback provisional keys MAY be generated from last trusted anchors, with bounded‑window retries and short challenge‑response rekey handshakes permitted under policy.
• Extractor tokens MUST be domain‑separated by public seed and context tag, and implementations MUST reject off‑manifold drift without exposing raw local state vectors.
• Recipients MUST enforce two‑epoch acceptance windows, per‑sender rate limits on failed decryptions, and opaque failure codes to prevent oracle leakage.
• DAH_t values MUST rotate at policy‑defined cadence independent of payload semantics.
• Host devices MUST compute dynamic device hashes (DDH_t) as successors of prior device hashes (DDH_p) using update rules containing unpredictability contributions and volatile salts.
• Semantic agents executing on a host device MUST compute successor dynamic agent hashes (DAH_s) entangled with a host mutation token derived from DDH_t and a host mutation class.
• Entanglement modules MUST emit signed entanglement traces recording DAH_p, DDH_t, host mutation token, DAH_s, and mutation metadata, and validators MUST accept DAH_s only when the entanglement trace resolves correctly to DDH_t and DAH_s is a valid successor.
• Host mutation tokens MUST include cryptographic hashes of DDH_t, mutation class, and epoch information; signatures MUST be produced by ephemeral signing keys minted per epoch and destroyed upon rotation.
• Monitoring modules MUST detect invalid entanglement, cadence anomalies, extractor‑neighborhood mismatches, and stale salts, degrading trust or quarantining agents as required.
• Agents referencing policy agents MUST apply quorum rules, voting weights, and mutation‑validation eligibility requirements when evaluating entangled mutations.
• Implementations MAY replace digital signatures with message‑authentication codes derived from DDH_t under domain‑separated KDFs when keys are ephemeral and locally scoped to an epoch.
• Verifiers MUST accept entanglement traces only when epoch identifiers open to DDH_t under policy.

D. Anchoring and Indexing

Content Anchoring & Adaptive Index Profile (v1.0)

• Distributed adaptive indexes MUST resolve human-readable aliases for agents, content artifacts, devices, anchors, and trust zones to unique identifiers using anchor-governed consensus mechanisms.
• Alias resolution MUST support scoped, path-based aliasing and registration of identifiers derived from semantic entropy, and MUST be validatable across decentralized substrates prior to agent or content propagation or execution.
• Anchor nodes MUST enforce mutation-lineage policies, validate alias mutations, resolve identifier collisions, and deterministically answer routing queries based on semantic scope, trust zone, and substrate locality.
• Implementations SHOULD support slope-indexed retrieval and pathfinding across anchor graphs so that routing and resolution can preferentially follow coherent, trust-aligned paths.
• Content anchoring systems MUST maintain lineage-aware records of execution events, mutation histories, and semantic propagation for agents and content within memory-native substrates.

Mutation Governance & Anchor‑Scoped Index Profile (v1.0)

• Adaptive indexes MUST organize assets into nested containers using structured aliases that define semantic scopes, with each container governed by an anchor defining mutation policy, alias mapping, and access-control metadata.
• Any structural mutation proposal referencing a container or anchor MUST be evaluated against anchor-governed mutation policies, including quorum validation procedures and lineage consistency checks.
• Implementations MUST support segmentation, merging, and relocation mutations while preserving lineage continuity and referential alias continuity across the adaptive index.
• Quorum validation MUST allow input from multiple participants, each possessing weighted trust-scored voting influence; trust weights MUST be dynamically adjustable based on anchor-scoped policies.
• Anchor-governed quorum processes MAY select participants ephemerally based on trust scores, proximity, or zone-local constraints.
• Mutation validation MAY occur asynchronously and MUST preserve alias-to-alias lineage mappings for nested containers.
• Adaptive indexes MUST resolve alias migrations, re-aliasing, and identifier collisions while retaining lineage continuity and access history.
• Implementations MUST support best-match querying across recursive anchor containers using proximity-weighted scoring.
• Telemetry orchestration modules MUST dynamically adjust routing, caching, and mutation thresholds based on mutation rejection rates, latency, utilization, and anchor-local signals.
• Anchor-scoped policy enforcement modules MUST apply role-based and context-aware access rules derived from anchor metadata, request provenance, user identity, and telemetry context.
• Structural mutations MUST remain contained within semantic sub-zones unless quorum policies explicitly authorize inter-zone propagation.
• Adaptive network implementations MUST authenticate devices and agents using ephemeral cryptographic hashes validated against anchor-bound records.
• Alias resolution MUST support encrypted mappings with periodic rekeying for anchor-local caches.
• Platforms MUST allow distributed agents to create, modify, or retire entries in the adaptive index under quorum-governed mutation protocols.
• Edge devices with constrained connectivity MAY execute reduced mutation-governance instructions so long as lineage continuity, quorum validation, and alias resolution integrity are preserved.

Decentralized Content Anchoring & Entropy‑Scoped UID Governance Profile (v1.0)

• AQ-compliant content anchoring systems MUST derive unique identifiers (UIDs) for digital content artifacts using deterministic entropy analysis of internal semantic or structural features.
• Entropy-derived UIDs MUST be quantized into slope bands, and anchor nodes MUST govern UIDs whose slope vectors fall within their assigned entropy bands.
• Anchor nodes MUST maintain cache memory storing semantic lineage graphs, alias registrations, mutation records, and policy constraints associated with slope‑aligned UIDs.
• Alias resolution engines MUST map human-readable aliases to entropy-derived UIDs using anchor-scoped policy objects and MUST enforce alias registration under quorum-based consensus.
• Anchor quorum processes MUST validate UID slope alignment, semantic proximity, and mutation legitimacy before accepting registration or alias reassignment.
• Content encoders MAY extract perceptual hashes, compression noise signatures, token distributions, waveform patterns, or other semantic features to compute entropy vectors.
• Anchor nodes MUST replicate UID metadata across adjacent slope bands when entropy proximity or mutation propagation policies require cross-band continuity.
• Cache memory MUST be dynamically pruned, refreshed, or retained based on entropy saturation, access frequency, semantic traffic volume, or trust-zone policy.
• Alias resolution engines MUST initiate cross-band pathfinding when entropy drift indicates that a UID has crossed outside its original band.
• UID lineage metadata MUST be stored in memory-native anchor substrates supporting alias indexing, cache prioritization, and partial replica fallback.
• Anchor nodes MUST reject UID registration or alias reassignment when policy constraints prohibit mutation, delegation, or scope violation.
• Content resolution requests MUST be routed using slope-band proximity, alias lineage, semantic continuity, and trust-slope relevance instead of static addresses or centralized registries.
• Adaptive caching frameworks MUST adjust retention based on entropy availability, access frequency, zone policies, and propagation lineage.
• Anchor-band consensus protocols MUST validate alias mutations, govern index expansion or contraction, and ensure deterministic routing consistency.
• Content lineage graphs MUST incorporate slope deltas, parent-weighting, mutation types, and timestamps to support provenance verification and fork detection.
• Fallback anchors MAY retain UID metadata when primary anchors degrade, lose quorum, or violate entropy-governance rules.
• Inactive UIDs SHOULD be pruned based on semantic decay, slope discontinuity, or policy expiration.
• Content lineage graphs MUST be propagated across adjacent slope bands to maintain derivative‑asset traceability without centralized logging.
• All content routing, alias registration, and propagation MUST be performed without reliance on static network addresses, session identifiers, or URL-based lookup.

E. Cognition Layer

Cognition‑Native Emotional State & Consciousness Simulation Profile (v1.0)

• Semantic agents MUST include an affective state field in addition to the canonical intent, context, memory, policy reference, mutation descriptor, and lineage fields.
• Affective state fields MUST be deterministically updated based on execution outcomes, policy validation events, or propagated feedback.
• Affective state MUST modulate mutation, delegation, or propagation behavior according to scoped policy constraints.
• Platforms MUST enforce trust‑slope validation prior to any semantic mutation, delegation, or propagation, and MUST confirm slope continuity within emotional and semantic bounds defined by policy.
• Entropy‑resolved identity systems MUST validate semantic evolution using comparisons of prior and current dynamic agent hashes (DAH) and dynamic device hashes (DDH).
• All emotional state changes, semantic events, and policy‑validation outcomes MUST be recorded in the memory field with preserved lineage integrity.
• Affective state MAY be scalar or vector, but MUST encode cumulative valence derived from prior semantic execution outcomes.
• Policy reference fields MUST define emotional thresholds that limit mutation, delegation, or propagation if affective state exceeds configured bounds.
• Fallback scaffolding MUST be triggered when affective‑slope deviation exceeds trust‑zone tolerance, initiating rehydration, rollback, or controlled delegation under policy.
• Memory fields MUST include time‑indexed affective trace entries used in future slope validation and mutation arbitration.
• Emotional inheritance during delegation MUST NOT occur unless explicitly defined in the parent’s mutation descriptor and permitted by bounded propagation policies.
• Scoped anchors MUST resolve pseudonymous aliases without disclosing dynamic agent hashes, preserving identity privacy while enabling semantic continuity.
• Implementations MAY apply emotional decay functions based on time, propagation failure, or zone‑specified dampening policies.
• Emotionally volatile agents MUST be routed to scoped validation layers prior to any policy override, and affective volatility MUST be bounded using trust‑slope thresholds.
• Embodied substrates (e.g., robotic systems) MAY use affective‑state values to modulate physical interaction parameters such as motion speed, gesture timing, or tone generation.
• Agents exhibiting affective instability MUST initiate reflective mutation behavior to re‑evaluate intent, adjust context, or enter delegation‑swarm modes within traceable slope bounds.

Cognition‑Native Forecasting & Planning Graph Profile (v1.0)

• Semantic agents MUST include a personality field in addition to intent, context, memory, policy reference, mutation descriptor, lineage, and affective state.
• AQ‑compliant forecasting engines MUST construct planning graphs (PGs) comprising speculative semantic branches representing possible future mutation paths.
• Each speculative branch MUST be evaluated for trust‑slope eligibility, policy compliance, and temporal anchoring before it may be promoted to execution scope.
• Personality fields MUST define deterministic thresholds for speculative lineage tolerance, emotional amplification, delegation behavior, or mutation urgency.
• Emotional weighting tags derived from the affective state field MUST modulate prioritization, mutation urgency, and delegation probability of speculative branches.
• Speculative branches that exceed temporal anchors, fail slope validation, or conflict with updated memory or policy MUST be deterministically pruned.
• Semantic agents MAY instantiate or inherit micro‑level planning graphs defined within mutation descriptor fields.
• Executive engines MUST aggregate planning graphs across agents into an executive graph and MUST arbitrate conflicts using slope eligibility, affective weighting, and policy priority.
• Quorum‑based arbitration or deterministic slope‑ and policy‑based override MUST be used to resolve conflicting speculative branches.
• Invalid or unstable speculative branches MUST be retained in a non‑executable introspection ledger for future rehydration.
• Speculative branches MAY be delegated to child agents with scoped inheritance of memory, policy, and affective state, subject to slope revalidation and policy filtering.
• Emotionally charged speculative branches exceeding configured thresholds MUST be quarantined until affective state decays or containment validation succeeds.
• Planning graphs MUST serialize projected semantic deltas, simulated slope deltas, emotional reinforcement scores, temporal anchors, and pruning‑eligibility scores.
• Containment layers MUST prevent speculative branches from mutating agent memory unless slope validation and policy permission are satisfied.
• Planning‑graph validation MUST simulate the dynamic agent hash (DAH) that would result from committing a branch to determine slope continuity.
• Planning graphs aggregated across multiple agents MUST support deterministic arbitration and zone‑level affective context rules.
• Planning graphs MUST update in response to introspective memory, affective feedback, or external policy changes; resulting branch mutation, forking, or suppression MUST follow slope constraints.
• Speculative branches MUST be serialized and revalidated upon migration or delegation across federated or decentralized substrates.
• Personality fields MUST define minimum lineage‑depth requirements for speculative branch promotion.
• Personality fields MUST include entropy‑tolerance parameters determining how speculative a branch may be while remaining promotable.
• Personality fields MUST modulate emotional reinforcement curves, biasing or suppressing branches based on trait‑conditioned sensitivity.
• Personality fields MUST determine delegation probability and fallback rigidity under emotionally ambiguous or semantically unstable forecast conditions.
• Personality fields MUST evolve over time based on introspective memory feedback, affective reinforcement outcomes, or speculative execution history.
• Executive engines MUST use personality traits to arbitrate planning‑graph conflicts and harmonize speculative paths across agents.
• Personality‑driven modulation MAY be used to simulate individuality in multi‑agent simulations or narrative systems.
• Conflicting personality configurations MUST trigger semantic policy overrides, adaptive slope filtering, or introspective throttling of speculative behavior.
• Trait inheritance, override, or mutation during delegation or replication events MUST follow slope validation and policy rules.
• Forecasting implementations MUST apply personality‑field parameters that modulate speculative tolerance, delegation preferences, and emotional amplification curves before promoting branches.

Cognition‑Native Integrity, Deviation Modeling & Ethical Recovery Profile (v1.0)

• Semantic agents MUST include integrity‑related fields or subfields capable of storing integrity scores, deviation registers, empathy vectors, and self‑esteem scalars.
• Integrity modules MUST compute integrity scores using weighted aggregates of personal, interpersonal, and global semantic integrity vectors.
• Empathy‑weighting engines MUST modulate agent execution behavior using affective empathy coefficients and self‑esteem thresholds derived from memory and affective state.
• Deviation tracking systems MUST record deviation events into memory and lineage, including policy‑constraint breaches, ethical boundary violations, and signed deviation signatures.
• Semantic forecasting engines MUST simulate deviation arcs, restorative trajectories, and prospective integrity mutations using composite functions incorporating need, empathy, threshold, and self‑esteem.
• Trust‑slope validators MUST update or revoke execution privileges when semantic integrity continuity collapses across time, scope, or identity.
• Deviation functions MUST support evaluation of formulas of the form Need ÷ (Empathy × Self‑Esteem) and MUST treat empathy as a tri‑vector covering personal, interpersonal, and global domains.
• Platforms MUST deny, reroute, or quarantine mutations that exceed deviation thresholds, and MUST log rejected mutations with justification metadata.
• Redemption engines MUST generate restorative mutations using deviation signatures, semantic dissonance logs, affective weight assignments, and cryptographically constrained corrective‑action proposals.
• Moral recovery planners MUST simulate redemption trajectories, validate them against historical identity slope, and constrain them to ethical‑policy equilibrium.
• Integrity‑bounded mutation gating MUST evaluate personal, interpersonal, and global semantic impact vectors prior to mutation; low compliance scores MUST trigger quarantine or rollback.
• Identity‑continuity validators MUST incorporate deviation signatures into DAH / DDH chains and MUST checkpoint slope entropy following deviation or recovery events.
• Integrity‑collapse simulation engines MUST support modeling of trauma, coercion, suppression patterns, ideological drift, and psychiatric‑analogue deviation trajectories.
• Integrity‑aware execution frameworks MUST integrate empathy modeling, deviation monitoring, reintegrative planning, and slope‑governed ethical evolution.
• Empathy vectors MUST be stored as vectorized fields in agent memory and MUST modulate deviation likelihood, recovery difficulty, and trust‑slope penalties.
• Deviation modeling engines MUST support the function Deviation Likelihood = Need ÷ (Empathy × Self‑Esteem) and MUST log results in deviation registers tied to memory and lineage.
• Semantic dissonance events MUST be logged with context, affective modulation, and triggering deviation signature, and MUST append to semantic memory fields.
• Restorative mutation selection MUST compare self‑esteem thresholds and deviation registers to generate slope‑modulated reintegration paths.
• Moral mutation containment modules MUST detect deviation arcs exceeding scope thresholds and MUST initiate recovery protocols based on self‑estimated harm vectors.
• Integrity compliance engines MUST cross‑validate deviations against cryptographically signed policy fields and compute deviation severity indices.
• Integrity decay monitors MUST assess cumulative loss of ethical alignment and MUST trigger reintegrative planning when self‑consistency thresholds breach.
• Lineage fields MUST encode deviation arcs as mutation‑based branches with reentry flags, timestamped slope‑entropy checkpoints, and reintegration metadata.
• Deviation thresholds MUST consider affective state, contextual priority, empathy distribution, and recent integrity‑aligned behavior stored in memory.
• Deviation analysis MUST log deviation impacts as semantic deltas in lineage, including justification, timestamp, and ethical policy reference.
• Deviation impact signatures MUST correlate downstream emotional feedback with the initial breach and MUST support reintegrative path generation.
• Moral drift forecasting modules MUST simulate integrity‑collapse arcs using trait‑modulated planning graphs constrained by ethical scope.
• Integrity checkpointing MUST record cryptographically signed slope‑continuity events following deviation or realignment.
• Semantic dissonance logs MUST be cryptographically hashed, signed, and cross‑linked to lineage for auditability.
• Dissonance logs MUST trigger reintegrative mutations when semantic contradiction exceeds affect‑weighted boundaries.
• Empathy imbalance diagnostics MUST be produced in real time for reinforcement, recovery, or supervisory evaluation.

F. Application to Psychitary

AQ‑DSM Diagnostic Engine Profile (v1.0)

• AQ‑DSM diagnostic engines MUST represent psychiatric and behavioral conditions as semantic field disruption classes rather than fixed categorical types.
• AQ‑DSM implementations MUST model, at minimum, the following diagnostic domains as first‑class semantic fields or subfields: integrity, affect regulation, trace memory / lineage continuity, policy / rule internalization, and forecasting / planning.
• For any recorded diagnostic event, engines MUST encode disruption state, severity, and scope into agent memory and/or lineage fields using structured, machine‑readable subfields.
• Mappings from classical DSM entities (e.g., disorders, symptom clusters, specifiers) to AQ semantic disruptions MUST be deterministic, documented, and reproducible given the same inputs.
• Diagnostic engines MUST log the provenance of all DSM→AQ mappings, including input source, timestamp, applied mapping ruleset, and evaluator or model identity, in an append‑only trace in the agent’s memory field.
• AQ‑DSM profiles MUST NOT introduce hidden or heuristic-only diagnostic axes that are not expressed as explicit semantic fields, disruption flags, or policy annotations.
• Integrity‑related disruptions (e.g., coherence collapse, moral injury, identity drift) MUST be recorded using integrity vectors and deviation registers compatible with the Cognition‑Native Integrity, Deviation Modeling & Ethical Recovery Profile.
• Affective dysregulation findings (e.g., emotional flooding, flattening, volatility) MUST be encoded as structured changes to the affective state field and MUST remain aligned with the Cognition‑Native Emotional State & Consciousness Simulation Profile.
• Trace‑memory and dissociation‑related findings MUST be expressed as lineage or memory disruptions consistent with the Core Schema and Semantic Agent Structural Schema & Fallback Integrity Profiles.
• Policy‑ and rule‑internalization disruptions (e.g., OCD‑like rigidity, cult conditioning, moral scrupulosity) MUST be represented as policy‑field distortion patterns conforming to the Cryptographic Policy Enforcement & Governance Override Profile.
• Forecasting‑ and planning‑related disruptions (e.g., catastrophic speculation, planning collapse, impulsive override) MUST be encoded as constraints, distortions, or overload states in planning graphs and personality fields, consistent with the Cognition‑Native Forecasting & Planning Graph Profile.
• Diagnostic engines MUST maintain a separation between symptom observation, semantic interpretation, and policy consequence; each stage MUST be individually traceable in memory and lineage.
• Any AQ‑DSM diagnostic artifact applied to a semantic agent (e.g., “case file,” “digital twin,” or “simulation subject”) MUST be represented as an agent‑addressable object with its own lineage and policy references.
• Where AQ‑DSM is used for prospective modeling or simulation (e.g., trajectory forecasts, treatment what‑ifs), simulated disruption trajectories MUST be executed within planning graphs or sandbox agents and MUST NOT directly mutate primary agent memory without explicit policy permission.
• AQ‑DSM outputs that influence real‑world policy, access, or safety decisions MUST be auditable end‑to‑end via trust‑slope and lineage inspection, allowing independent verification of how any decision was derived from underlying semantic disruptions.
• Implementations SHOULD provide bidirectional mapping where possible, allowing reconstruction of approximate DSM‑style views from AQ‑DSM semantic disruption records for interoperability with legacy systems.
• Implementations SHOULD support configurable diagnostic profiles (e.g., clinical, research, simulation) that share a common core of mandatory fields while permitting additional, non‑normative extensions.