I built a continuity-first memory system for AI. Here's what the benchmarks actually showed.

What My Continuity-First AI Memory Benchmark Actually Showed I’ve spent a stupid amount of time thinking about AI memory. Not just “how do I retrieve more text,” but how do I make an AI keep the right current truth over time instead of constantly resurfacing stale context, superseded state, old preferences, and half-relevant junk. That frustration is what pushed me to build a continuity-first memory system for Morph / Haven. The original goal was not “beat RAG in a benchmark.” It was much more practical than that. I wanted an AI that could: remember the newest correct thing preserve ongoing work over time pick up where we left off without me re-explaining everything constantly So I built a benchmark harness and compared three memory backends: continuity_tcl — my structured continuity memory system rag_baseline — a simple retrieval baseline rag_stronger — a stronger retrieval path with reranking I tested them across four broad behavior families: memory poisoning / bad memory admission c