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Developer Builds Two-Layer AI Memory Gate to Stop False Policy Change Detections

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A software developer identified a critical flaw in AI agent behavior where a model incorrectly flagged an unchanged data-export policy as a new rule, based solely on misleading inference from a verbatim quote. To fix this, the developer built a two-layer memory-authority gate combining an LLM proposer with a deterministic confirmer that cannot be overridden by persuasive language. A second version added a "relation-span clause" requiring that any cited sentence explicitly contain both a recognized change word and a relevant scope term before a rule change is accepted. The developer pre-registered predictions, pass/fail criteria, and expected failure modes in a public, timestamped repository before running any tests, ensuring results could not be retroactively adjusted. One known limitation remains: implicit contradictions between rules, where no explicit change language exists, are flagged for human review rather than handled deterministically.

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Developer Builds Two-Layer AI Memory Gate to Stop False Policy Change Detections · ShortSingh