llm-belief-operations-span-creation-and-classification
OUT derived (depth 4)
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.
Summary
The system's two main LLM-powered operations — generating new derived claims and classifying negative ones — were found to follow the same defensive design philosophy: both guard against runaway costs, handle errors gracefully, and bound their resource usage. This suggested a coherent architectural approach to quality control across the entire lifecycle, but the claim no longer holds because one or both of its supporting observations have been retracted.
Justifications
SL — The derive and list-negative pipelines independently achieve defensive robustness; together they cover the full LLM-driven belief lifecycle from creation through classification with no quality gap
Antecedents (all must be IN):
- derive-pipeline-is-safe-complete-and-efficient — The derive pipeline simultaneously achieves safety (fail-soft validation with Jaccard retraction guards and environment isolation), completeness (exhaustive exploration with guaranteed termination via cycle guards), and efficiency (linear O(N) budget accumulation with a floor of 5 beliefs per agent preventing representation starvation).
- list-negative-is-bounded-and-batch-scalable — The list-negative classification pipeline is both defensively bounded (two-stage keyword + LLM filtering with hallucination rejection and graceful malformed-output handling) and scalably partitioned (fixed batch size of ~50 candidates per LLM call), ensuring predictable resource usage and bounded LLM costs regardless of belief network size.
Dependents
These beliefs depend on this one:
- review-completes-llm-quality-lifecycle — 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.
- review-driven-quality-lifecycle-is-fully-code-enforced — The complete LLM-driven quality lifecycle — creation via derive with structural validation and retraction guards, classification via list-negative with defensive bounding, and evaluation via review with scoped mutation safety — achieves full code enforcement of all quality constraints, but only when the derive pipeline's minimum antecedent requirement is enforced in code rather than prompt-only.