deterministic-reasoning-is-boundary-safe-and-reproducible

OUT derived (depth 5)

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.

Summary

The reasoning engine is claimed to be fully deterministic end-to-end, from its core truth maintenance logic through schema evolution at system boundaries all the way out to LLM calls used for deriving new beliefs. This is currently retracted, meaning one or both of its supporting claims — that the engine handles format evolution gracefully, or that the LLM derive pipeline is truly reproducible — no longer holds, so the stronger combined guarantee cannot be trusted.

Justifications

SL — Core determinism (uniform evaluation, guaranteed termination) extends through validated boundaries to reproducible derive prompts with defense-in-depth

Antecedents (all must be IN):

  • deterministic-reasoning-within-evolution-tolerant-boundaries — The deterministic reversible reasoning engine — producing predictable terminating results through uniform evaluation — operates within system boundaries that gracefully handle format and schema evolution, ensuring deterministic correctness remains stable as external data formats change over time.
  • derive-pipeline-is-reproducible-and-fully-assured — 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.

Dependents

These beliefs depend on this one:

Details