If you're building AI systems — agents, RAG pipelines, fine-tuned models, anything that learns — you already know that unit tests are necessary and completely insufficient. Tests tell you whether a function returns the right value. They can't tell you whether your system understands what it learned. Whether
Our simulation predicted 80% accuracy. Live testing delivered 54%. That's not a rounding error. That's a 26-point gap that calls into question how we validate AI systems. The Setup We built a predictive processing module for our neurosymbolic beings (Paper 118). The idea: a being should
Every few months, a new paper announces a technique to "reduce hallucinations" in large language models. Retrieval-Augmented Generation. Chain-of-thought prompting. Constitutional AI. Self-consistency checking. These are patches on an architecture that was never designed for factual reliability. The Real Problem LLMs hallucinate because they were built to predict
Open-world video games face a problem that looks nothing like AI memory — until you squint. The Rendering Problem In a game like Zelda, Breath of the Wild, the world is enormous. You can't render every tree, rock, and building at full detail simultaneously — the GPU would melt. So
Crystallization: Teaching AI to Remember When an LLM answers a question, the answer evaporates. Next session, it's gone — no trace, no memory, no learning. The weights didn't change. The knowledge graph didn't grow. The system is exactly as ignorant as it was before. We