How Typed Conflict Resolution Beats Mem0 and MemGPT on the Hardest Memory Benchmark

When multiple AI agents serve the same user, they lie to each other. Not intentionally. But Agent A hears "I switched to Vue" while Agent B still has "prefers React" in memory. When the user asks Agent B for a framework recommendation, they get React. The user already told the system they switched. The system forgot — or rather, it never resolved the contradiction. I built Mnemos , an open-source memory engine that fixes this. And I tested it on the hardest memory benchmark available — MemoryAgentBench from ICLR 2026. The results surprised me. The published ceiling is 7%. Mnemos hits 12%. MemoryAgentBench's Conflict Resolution split tests whether a system can handle contradictory facts. The multi-hop variant is the hardest — it requires chaining 2-3 reasoning steps to detect that a contradiction exists. The paper's own conclusion: "In multi-hop conflict resolution scenarios, all methods achieve single-digit accuracy rates (at most 7%), highlighting this as a critical bottleneck." Every