Google Research study reveals recall limitations in frontier models like GPT-5 and Gemini-3

Your AI model knows the answer. It just can’t find it. That’s the central finding of a new study from Google Research and Technion, which analyzed over 4 million responses across 13 large language models and discovered that when frontier models hallucinate, the problem usually isn’t that they lack the relevant facts. The facts are sitting right there in the model’s parameters. The model simply fails to pull them up when asked. The lost keys problem The paper, titled “Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality,” introduces a framework the team calls “knowledge profiling.” The framework separates two distinct capabilities: encoding (whether a model has stored a fact) and recall (whether it can actually surface that fact during generation). Using a benchmark called WikiProfile, derived from Wikipedia, the researchers found that leading models have gotten remarkably good at the storage part. Gemini-3-Pro and GPT-5 both achieve 95-98% saturation in factual encoding on the benchmark. The retrieval side tells a very different story. Direct recall fails on 26-34% of those encoded facts. Even when chain-of-thought reasoning is enabled, it still misses 11-...