Researchers have developed a new method called VAKE to help Large Language Models (LLMs) recall and verify factual knowledge. Existing methods often mix knowledge retrieval with reasoning, making it hard to tell if the model is using its stored knowledge or just the input context. VAKE uses two stages: explicit priming, where the model adds verifiable evidence to a query, and implicit reasoning, where the model answers from the original input. Experiments show that VAKE outperforms standard baselines and can transfer knowledge between different datasets. The method also shows that over 80% of the added evidence provides new factual information not available in the context. --- Why it matters: This matters to AI researchers because it addresses a key bottleneck in factual question answering: reliably recalling stored knowledge. By developing methods like VAKE, researchers can improve the accuracy and trustworthiness of LLMs. Source: https://arxiv.org/abs/2608.18581