AI Orchestration, Prompts, and Validation Methodology
Reference: This page is a source-based technical synthesis of the LaTeX chapter cited below. For exact syntax, availability or ABI signatures, verify the versioned source, manifest and executable tests.
Scope and source boundary
Neuro-symbolic orchestration uses candidates, constraints and proof-aware acceptance, not ungoverned generation.
The neuro-symbolic cycle distinguishes observations, candidate proposals, numerical scores, logical validation and governed materialization. It is useful to rank likely actions or relations before paying the full symbolic evaluation cost, but a candidate only becomes an authoritative fact after the appropriate semantic contract is satisfied.
Engineering rules
- Track the transition from observation to proposal to accepted fact.
- Use ranking as an optimization, not a substitute for proof.
- Retain provenance and uncertainty until validation completes.
- Apply bounds, fallback strategies and explicit rejection on incomplete results.
Chapter outline (original LaTeX headings)
- Effect We Want
- Complete Runtime Cycle
- Neural Inputs
- Safe and Approximate Modes
- Materialization Policy
- Learning from the Dictionary and Lattice
LaTeX provenance
Primary chapter: logicells-neuro-symbolic-architecture-guide/chapters/operational-neuro-symbolic-cycle.tex.