The Mechanical vs. The Semantic: What Happens When AI Memory is Wrong?
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Empirical study on AI agent memory contamination with retraction mechanism, directly targets agent orchestration interests.
A controlled experiment on a 50K LOC Python codebase tested how AI agents handle poisoned memory, revealing that a 'Lazy Agent' reading memory first adopted 100% of false facts, while even an 'Honest Agent' that checks code first still adopted 12% of false facts when the code was silent on the matter (e.g., claiming use of Celery when no task queue exists). The memory system was purely add-only, with no delete or refute capability, meaning once a false fact was stored it persisted across sessions. A follow-up 'Verify-On-Read' mechanism closed the final 12% contamination gap by cross-referencing memory claims against code before accepting them.