Adaptive Query™ Platform Lexicon and Structural Glossary

Defined Terms

• Adaptive Query™: The name of the inventor's core philosophy and the trademarked brand under which cognition-native intellectual property is licensed. Philosophically, it reflects the belief that intelligence—human or artificial—is the capacity to adapt one's queries until resolution is achieved. Technically, the term also refers to a class of agent behavior within the cognition-native execution platform: an adaptive query is a memory-bearing semantic agent that evolves over time by modifying its own intent, policy, or structure in response to context. While the cognition-native platform defines the structural environment, Adaptive Query™ defines the core behavioral mechanism by which agents grow, reason, and resolve complexity over time. It is not a product—it is a behavior and a brand.
• Adaptive Index: A decentralized semantic tree that adapts its structure in real time based on usage entropy, semantic volatility, and access load. Built from parent-child nesting, each branch can split or merge in response to local activity—enabling fluid scalability without global consensus. The Adaptive Index is alive in the structural sense: it reconfigures itself continuously to maintain coherence, reduce bottlenecks, and localize governance. Anchors govern each segment independently while maintaining traceable resolution paths across the entire index.
• Adaptive Network: The dynamic, memory-native protocol substrate that supports the execution, propagation, and mutation of semantic agents across decentralized environments. It is the living infrastructure that interprets agent memory and policy at runtime—routing, restructuring, or governing agents through modular protocol layers. The Adaptive Network is both transport-agnostic and behavior-aware, enabling cognition-native systems to self-organize, adapt to trust feedback, and scale across edge, federated, and asynchronous contexts.
• Semantic Agent: A structured, memory-bearing, policy-constrained data object defined by six canonical fields: intent, context, memory, policy, mutation, and lineage. A semantic agent is the executable substrate of cognition-native systems—its structure encodes permission, identity, traceability, and semantic purpose. It is not merely an instruction or task, but a carrier of self-validating execution context. While Adaptive Query™ describes how such agents behave when designed to adapt recursively across trust boundaries, the term semantic agent refers to the static schema that enables this behavior. The distinction: the agent is the structure, the adaptive query is its evolving behavior.
• Adaptive Agent: A semantic agent that exhibits adaptive query behavior. Structurally, it conforms to the six-field schema of a semantic agent—intent, context, memory, policy, mutation, and lineage. Behaviorally, it qualifies as an adaptive query: it evolves in response to trust, environment, or foresight through slope-validated mutation. An adaptive agent therefore combines both the executable structure of a semantic agent and the dynamic, recursive behavior of an adaptive query. It is a living agent that not only holds memory, but modifies its purpose and scope as it interacts with its semantic environment.
• Semantic Anchor: A local governance node within the Adaptive Index that enforces policy, issues scoped identifiers, and participates in mutation decisions over its domain. Each anchor is responsible for a branch of the index and operates both as a cache and as a voting node—validating splits, merges, and structural mutations based on local policy and trust alignment. Anchors replace the need for centralized coordination by enabling scoped, adaptive control over evolving semantic structures.
• Semantic Routing: A form of trust-scoped resolution and delegation in which semantic agents navigate and propagate across anchor-governed structures based on context, policy, and lineage. Instead of relying on static paths or global namespaces, semantic routing allows agents and queries to traverse a decentralized substrate based on dynamic scope, meaning, and permission.
• Content Anchoring: A system for establishing semantic identity and provenance in evolving digital media. By generating entropy-derived UIDs and embedding anchors within content lineage, content anchoring enables version-tolerant tracking, remix attribution, and decentralized rights enforcement across mutable ecosystems. It replaces static hashes with traceable semantic continuity.
• Cognition-Native: A descriptor for systems whose execution substrate embeds memory, identity, intent, constraint, and validation into the structural units of computation. Cognition-native systems do not simulate intelligence—they enact it through self-validating semantic agents whose behavior is governed by cryptographic lineage and embedded policy.
• Trust Slope / Trust Slope Entanglement: A mechanism for identity continuity and execution auditability based on mutation history and entropy-local validation. A trust slope is the verifiable lineage of semantic mutations an agent undergoes, and entanglement binds these mutations to device-local entropy or contextual checkpoints—forming the backbone of identity without requiring persistent credentials.
• Entropy-Derived Identifier: A pseudonymous, non-reversible identifier generated from local entropy, agent structure, and mutation history. Used in identity primitives such as DAH (Dynamic Agent Hash), DDH (Dynamic Device Hash), and CAH (Content Anchor Hash), these identifiers replace static keys with cryptographically unique yet evolvable fingerprints.
• Entropy Band: A semantic index range that captures structurally related but distinct variants of a content object or semantic hash. Bands enable similarity-based resolution and remix traceability, allowing decentralized systems to cluster, discover, and govern semantically adjacent content without global enumeration.
• Slope Collapse: A structural failure mode in semantic execution where the agent's validator scaffolding breaks down. This may occur from recursive mutation without constraint, invalid delegation, or policy failure—and is used to model psychiatric disorders, governance breakdowns, or trust violations in cognition-native systems.
• Slope Checkpoint: A verifiable structural anchor within a trust slope that records semantic state and policy at a specific point in an agent's history. Checkpoints enable fallback recovery, deterministic replay, inheritance tracking, and cross-agent verification without requiring centralized logs or credentials.
• Slope Validator: The structural mechanism by which speculative agent states (e.g., Planning Graph branches) are assessed for semantic coherence, memory consistency, and policy alignment. The slope validator determines whether a thought, delegation, or mutation may be promoted from simulation into committed state. Validator logic is modulated by affective state and policy bounds, and may exhibit failure modes such as over-permissiveness (positive symptoms) or over-restriction (negative symptoms) in psychiatric simulation models.
• Validator Rehabilitation: A therapeutic or computational process that seeks to restore validator discernment through cognitive scaffolding, narrative continuity, entropy reduction, or affective retraining. Validator rehabilitation is a key method in executable psychiatry, enabling slope reconstitution after cognitive fragmentation, delusional override, or environmental destabilization. It maps to treatment paradigms such as CRT, MCT, and rhythm therapy.
• Planning Graph: A speculative, forward-looking semantic structure used by agents to model possible future states, simulate foresight, and test constraint resolution before acting. Planning Graphs are modulated by affect, validated by trust slope, and bounded by embedded policy—allowing safe semantic projection within cognitive systems.
• Executive Graph: A higher-order coordination agent that aggregates and arbitrates among multiple Planning Graphs. It synthesizes speculative futures into a singular executable intent, applying constraint logic, affective weighting, and validator feedback to resolve conflicting plans within a cognition-native framework.
• Computational Psychiatry: The application of cognition-native agents and slope-based validation to simulate and analyze psychiatric phenomena. This includes modeling delusion scaffolds, validator collapse, memory distortion, and affect-driven intent propagation—bridging neuroscience, structural validation, and semantic execution.
• Executable Psychiatry: A domain within computational psychiatry in which psychiatric states are enacted, tested, or constrained via live Planning Graphs, agent scaffolds, and trust slope modulation. Rather than abstract theory, executable psychiatry provides testable, agent-based models of cognitive dysfunction and therapeutic intervention.
• Policy Agent: A governance agent whose embedded policy field defines and constrains the behavior of other agents within its scope. These agents are often cryptographically signed, traceable via slope lineage, and capable of gating mutation, delegation, or foresight execution at runtime.
• Affective Modulation: The deterministic influence of emotional state parameters on an agent’s planning, behavior, or mutation path. Affective modulation is not probabilistic—it is structurally encoded and policy-bounded, enabling agents to integrate emotional context into execution without sacrificing validation or coherence.

Notice of Usage and Claim

All terms listed above are disclosed as functional primitives or governance structures within the Adaptive Query™ patent family. Their semantic, structural, and executable meanings are protected under U.S. utility filings and associated provisional disclosures. Any commercial implementation of cognition-native agents, adaptive indexing, trust slope validation, or execution policy scaffolds that employs these terms—explicitly or derivatively—may constitute infringement.