Neuro-Symbolic AI — governed reasoning architecture

c4v1

/01 Views

Neural representation and proposalc4

Symbolic knowledge and reasoningc4

Knowledge and provenancec4

Decision and enforcementc4

Assurance and accountabilityc4

Evaluation and escalationc4

Safety and adversarial controlsc4

Candidate hypothesis generator → Semantic and ontology bindingc4

Versioned knowledge lookup → Knowledge graph queryc4

Logical inference engine → Claim reconciliationc4

Constraint and consistency verifier → Decision policy guardc4

Evidence grounding resolver → Proof obligation checkerc4

Decision policy guard → Proof obligation checkerc4

Proof obligation checker → Explainable decision assemblerc4

Untrusted input isolation → Input and intent normalizationc4

Instruction injection detection → Decision policy guardc4

Abstention and refusal guard → Safe output gatewayc4

Explainable decision assembler → Reasoning trace builderc4

/02 About

Hybrid neural inference and symbolic reasoning with ontology grounding, policy-constrained conclusions, explainability, verification and abstention.

Purpose: Hybrid neural inference and symbolic reasoning with ontology grounding, policy-constrained conclusions, explainability, verification and abstention. Architecture scope: independently reconstructed reference responsibilities, operational flows, policy decisions, assurance concerns, exception pathways and security boundaries. Grouped viewpoints describe coherent service or activity sequences. Focused trace views expose inter-domain obligations and information exchanges. Adoption: refine control and data-flow semantics to the enterprise ecosystem; map resource owners, role and workload identities, interfaces, failure policies, information classification, privacy obligations, deployment options and operational evidence. Reference elements alone do not establish an authorization, compliance result, formal proof, cryptographic assurance, or production readiness. Public conceptual basis: https://www.nist.gov/itl/ai-risk-management-framework. This is an original Arq vendor-neutral interpretation, not an official implementation diagram or an endorsed/certified solution.

Published by Lattix · 28 elements · 32 relationships · validated on publish

/03 Contents

Application Component: Input and intent normalization, Neural representation encoder, Statistical inference model, Candidate hypothesis generator, Semantic and ontology binding, Knowledge graph query, Logical inference engine, Constraint and consistency verifier, Knowledge source validation, Evidence grounding resolver, Versioned knowledge lookup, Claim reconciliation, Decision policy guard, Explainable decision assembler, Safe output gateway, Reasoning trace builder, Proof obligation checker, Decision lineage recorder, Untrusted input isolation, Instruction injection detection, Uncertainty calibration, Abstention and refusal guard
Data Store: Knowledge and evidence graph, Immutable evidence ledger
Activity: Evaluate factual consistency, Test rule coverage and contradictions, Search counterexamples, Escalate or abstain when unsupported