From Storage to Access: Verifiable Activation of Parametric Knowledge in LLMs via Explicit Priming and Implicit Reasoning

Although Large Language Models (LLMs) encode rich factual knowledge in their parameters, reliably recalling and verifying such knowledge remains a key bottleneck in factual question answering. Existing end-to-end methods entangle knowledge elicitation with reasoning, making it difficult to determine whether correct answers arise from parametric knowledge or the input context. To address this challenge, we propose VAKE (_**V**erifiable **A**ctivation of Parametric **K**nowledg**E**_), a two-stage reinforcement-learning framework that externalizes latent parametric knowledge through explicit Priming and transfers the acquired elicitation capability to implicit Reasoning. Given a query and an insufficient retrieved subgraph, the **Priming** policy explicitly inserts bridging triples as verifiable evidence, with supervision provided by rewards derived from answers generated by a separate frozen model over the augmented subgraph. Building on the policy learned during Priming, the **Reasoning** stage trains the model to answer from the original input, testing whether the capability acquired through explicit knowledge elicitation transfers to implicit reasoning. Across seven benchmarks and two backbone families, VAKE achieves the best average performance on both the in-distribution multi-hop and OOD benchmark groups, outperforming inference-time methods and GRPO. Complementary attribution and probing analyses on 2Wiki suggest that the inserted triples primarily contribute bridging information not derivable from the retrieved context and can surface knowledge missed by direct prompting. These results support the view that VAKE activates latent parametric knowledge rather than copying the input context or memorizing dataset-specific associations.