The Completeness Illusion: Why Perfect-Looking Memory Extraction Masks Silent Gaps

LLM-backed memory systems face a critical fidelity paradox: when raw data gets extracted into structured knowledge, the polished output actually obscures gaps rather than revealing them. The author's knowledge graph recovered 97.7% of vocabulary but only 61.1% of structural relationships, yet queries returned confident-looking answers that felt complete. For anyone building long-term projects on extracted memory, this "premature retrieval closure" presents a hidden accumulating risk—undetected incompleteness compounds over time when the system manufactures false confidence.