all-external-inputs-safely-integrated

IN derived (depth 3)

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.

Summary

The system can safely accept knowledge from two external sources — LLM-generated conclusions and beliefs imported from other agents — without risking corruption of its reasoning network. LLM output is filtered and validated before it can affect anything, and imported beliefs go through a reconciliation process that handles conflicts and prevents naming collisions, so neither pathway can introduce bad data that undermines existing reasoning.

Justifications

SL — depth-3, gated — the agent count bug inflates belief counts during LLM budget allocation, potentially truncating context and producing incomplete derivations; safe integration cannot be claimed while budget allocation is incorrect

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.
  • import-provides-complete-reconciliation — The import subsystem provides complete reconciliation coverage: heterogeneous truth states are handled correctly on initial load, dual modes support additive import and remote-wins sync for different operational needs, and the colon-based namespace convention with auto-wiring prevents ID collisions across agents.

Unless (any of these IN defeats this justification):

  • derive-agent-count-bug — `_build_beliefs_section` has a bug: `count += len(belief_ids)` is inside the per-belief loop instead of outside it, inflating the count and shrinking the non-agent budget below intended size

Dependents

These beliefs depend on this one:

Details