Midbrain

After 10 years at Volvo Cars, I left to co-found MidBrain. AI doesn’t fail because it lacks intelligence. It fails because it doesn’t remember. Over the past year, we’ve explored this across very different environments: -AI agents in Minecraft -Long-running AI companions -Human behavior simulation in robotics Different domains. Same failure mode. AI agents process information - but they don’t improve from experience. Today, most AI systems are: Stateless across sessions Dependent on large context windows Repeating the same mistakes Retrained in expensive batch cycles They retrieve the past. But they don’t learn from it. What’s often called “memory” today is: store → retrieve → inject → re-run This improves context. It does not change behavior. At MidBrain, we’re building the missing layer: Memory + continual learning for long-running AI systems Our thesis: Intelligence is not just inference. It is the ability to change through experience. Today, we’re sharing our first step: SmartSearch - memory retrieval for long-horizon agents 93.5% on LoCoMo (SOTA) 88.4% on LongMemEval-S (SOTA) LLM-free retrieval (CPU-only) ~8.5× fewer tokens Paper: https://arxiv.org/abs/2603.15599 This work shows: why retrieval alone doesn’t lead to learning why memory must consolidate into procedural behavior why today’s training paradigm is fundamentally inefficient (2–5× wasted compute, repeated data movement, batch retraining loops) This is the starting point. From: retrieval → memory → learning → behavior Toward: AI agents that run for years, not minutes One identity across chat, code, and physical environments We’re opening early design partnerships for teams building long-running agents (copilots, coding agents, companions) https://midbrain.ai