llm-mutations-are-bounded-end-to-end

IN derived (depth 3)

LLM-driven belief derivation is bounded at every stage of the pipeline: input validation (fail-soft filtering, Jaccard retraction guard, environment isolation), atomic persistence (context-managed load/save), and output propagation (deterministic terminating BFS with lifecycle-aware traversal).

Summary

When the system uses an LLM to generate new beliefs, every stage of that process has guardrails that prevent damage. Inputs get filtered and sanitized before the LLM sees them, changes to the database happen atomically so a crash can't leave things half-written, and when new beliefs ripple through the network, the propagation algorithm is guaranteed to finish and won't loop or corrupt existing nodes. This means you can let the LLM derive beliefs without worrying that a bad response will break the knowledge base.

Justifications

SL — depth-3 — input bounding (derive pipeline defenses) and output bounding (propagation safety) are independently established, together covering the full mutation lifecycle

Antecedents (all must be IN):

  • llm-driven-mutations-are-safely-bounded — LLM-driven belief derivation is safely bounded by defense in depth: the derive pipeline validates proposals with fail-soft filtering, Jaccard retraction guards, and environment stripping at the LLM boundary, while the API layer enforces atomic load/save with write-flag gating and dict-only returns at the persistence boundary — malformed or adversarial LLM output cannot corrupt the network.
  • propagation-is-safe-and-terminating — Truth propagation is both lifecycle-safe and guaranteed to terminate: retracted nodes are skipped, trigger nodes are never recomputed, BFS prevents stack overflow, and stop-on-unchanged prevents oscillation — propagation respects every node state it encounters.

Dependents

These beliefs depend on this one:

Details