review-completes-llm-quality-lifecycle
OUT derived (depth 5)
The LLM-driven belief quality lifecycle is complete across all phases: creation via derive (safe, complete, efficient), classification via list-negative (bounded, batch-scalable), and quality evaluation via review (scoped to derived beliefs, mutation-safe, fault-tolerant) — covering belief genesis, categorization, and ongoing quality assessment with no unmonitored phase.
Summary
The system's LLM-powered quality pipeline was considered to cover every stage of a belief's life — from generating new beliefs, to classifying them, to reviewing their quality — with no blind spots. This matters because any unmonitored phase could let low-quality or contradictory beliefs accumulate silently, so full coverage would mean the system is self-maintaining. However, this conclusion is currently retracted, meaning one of its supporting claims no longer holds and the full-lifecycle coverage guarantee cannot be relied on right now.
Justifications
SL — Review adds independent quality evaluation to derive+classify, closing the LLM quality lifecycle
Antecedents (all must be IN):
- llm-belief-operations-span-creation-and-classification — All LLM-driven belief operations — creation via derive (with safety, completeness, and resource efficiency) and classification via list-negative (with defensive bounding and batch scaling) — share consistent defensive patterns across the complete belief quality lifecycle.
- review-pipeline-is-scoped-and-mutation-safe — The belief review pipeline restricts evaluation to derived beliefs only (premises excluded) and gates auto-retraction behind the dry-run flag, ensuring review operations are scope-limited and mutation-safe by default.
Dependents
These beliefs depend on this one:
- quality-lifecycle-is-complete-and-resource-efficient — The complete LLM-driven quality lifecycle — creation via derive with defensive validation, classification via list-negative with batch scalability, and validation via review with read-only fault tolerance — operates within resource-efficient bounds spanning zero-dependency packaging through lazy-loading startup through bounded runtime execution, ensuring quality assurance scales sustainably with belief network size.