When large language models (LLMs) hallucinate, developers typically assume the model lacks the required facts. Engineering teams diagnose the error as missing knowledge. The standard response is to increase model size, expand training data, or build complex retrieval architectures. A new study by researchers at Google Research and Technion demonstrates that the knowledge is often not missing. The model has the information encoded parametrically but fails to surface it during generation.  Their experiments show that frontier models like GPT-5 and Gemini-3 encode 95-98% of tested facts. This indicates that in many cases, recall, rather than encoding, is the primary bottleneck for factual accuracy.  By understanding how to unlock existing knowledge through inference-time computation, engineering teams can build more reliable applications without necessarily relying on larger models or external databases. Knowledge profiling: measuring what models actually know To map this gap between storage and retrieval, the researchers propose shifting the evaluation focus from question-level accuracy to fact-level profiling. Instead of simply scoring whether an LLM answers an isolated prompt right or wrong, fact-level profiling tests a …