llm-driven-mutations-are-safely-bounded
IN derived (depth 2)
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.
Summary
The system's LLM-powered belief derivation cannot corrupt the underlying reasoning network, even if the LLM produces malformed or adversarial output. This safety comes from two independent layers working together: the derivation pipeline itself filters and validates LLM proposals before they reach the database, while the storage layer independently enforces atomic transactions and prevents any caller from bypassing write controls or holding direct references to internal state.
Justifications
SL — LLM-boundary defenses (depth-1) + persistence-boundary atomicity (depth-1) form defense in depth against the unique risks of machine-generated mutations
Antecedents (all must be IN):
- derive-pipeline-is-defensive — The derive pipeline applies multiple defensive measures: fail-soft validation, Jaccard-based retraction guard, and environment variable stripping to prevent recursive spawning
- api-layer-ensures-atomic-isolated-mutations — The API layer enforces mutation safety through four mechanisms: context-managed load/save, per-function transaction scope, write-flag gating to prevent unintended persistence, and dict-only returns that prevent callers from holding live network references.
Dependents
These beliefs depend on this one:
- all-external-inputs-safely-integrated — Both LLM-derived beliefs and agent-imported beliefs are safely integrated into the network: defensive validation with retraction guards bounds LLM output, while complete reconciliation with dual modes and heterogeneous truth handling manages agent imports.
- llm-mutations-are-bounded-end-to-end — 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).