derive-pipeline-is-reproducible-and-fully-assured
OUT derived (depth 4)
The derive pipeline achieves quadruple assurance: reproducibility (deterministic sampling with fixed seeds and accurate budget allocation), safety (fail-soft validation, Jaccard retraction guards, environment isolation), completeness (exhaustive exploration with guaranteed termination), and efficiency (O(N) budget accumulation with guaranteed floor) — four independently established properties reinforcing pipeline trustworthiness.
Summary
The derive pipeline was considered fully trustworthy because four independent properties had each been verified: runs produce the same results every time, failures in one part cannot corrupt another, all possibilities get explored without infinite loops, and resources are allocated efficiently with no topic starved of attention. Since at least one of those supporting claims no longer holds, this combined assurance is currently withdrawn.
Justifications
SL — Reproducibility and defense-in-depth combine with safety, completeness, and efficiency for full pipeline assurance
Antecedents (all must be IN):
- derive-pipeline-is-reproducible-and-defense-in-depth — The derive pipeline achieves both reproducibility (deterministic sampling with fixed seeds and accurate budget allocation) and defense-in-depth at the application stage (validation-before-apply trust boundary with per-proposal error isolation), ensuring pipeline runs are repeatable and any surviving bad proposal cannot corrupt sibling proposals.
- 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).
Dependents
These beliefs depend on this one:
- deterministic-reasoning-is-boundary-safe-and-reproducible — The deterministic reversible reasoning engine operates within evolution-tolerant boundaries AND produces reproducible LLM-driven derivations through deterministic prompt construction with fixed seeds and accurate budget allocation — determinism extends from core truth evaluation through system boundaries to external model interaction.