TL;DR: a controlled four-phase experiment in pure Python, with real benchmark numbers. No API key. No GPU. Runs in under 10 seconds. As memory grows from 10 to 500 entries, accuracy drops from 50% to 30% Over the same range, confidence rises from 70.4% to 78.0% — your alerts will never fire The fix is four architectural mechanisms: topic routing, deduplication, relevance eviction, and lexical reranking 50 well-chosen entries outperform 500 accumulated ones. The constraint is the feature. The Failure That Shouldn’t Have Happened I ran a controlled experiment on a customer support LLM with long-term memory. Nothing else changed. Not the model. Not the retrieval pipeline. At first, it worked perfectly. It answered questions about payment thresholds, password resets, and API rate limits with near-perfect accuracy. Then the system kept running. Every interaction was stored: meeting notes onboarding checklists internal reminders operational noise All mixed with the actual answers.…