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Life sciences · Preprint
arXiv · August 19, 2026
Early or partial results. Treat as a signal, not a conclusion.
VAKE is a novel two-stage reinforcement learning framework designed to elicit latent factual knowledge from large language models through explicit priming followed by implicit reasoning. The method shows computational improvements across multiple benchmarks but remains unreviewed and is presented as a machine learning technical contribution without clinical or real-world validation.
Computational framework with benchmark evaluation. Intervention: VAKE: two-stage reinforcement learning framework with explicit Priming policy (inserting bridging triples) and implicit Reasoning stage. Compared with: Standard baselines.
VAKE outperforms standard baselines across seven benchmarks and models from 3B to 14B parameters Over 80% of inserted triples provide factual bridging knowledge not derivable from retrieved context More than half of elicited knowledge is inaccessible through direct prompting
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This is an early-stage machine learning method paper presenting a novel framework without peer review, demonstrating proof-of-concept across benchmarks but lacking independent validation or clinical applicability.
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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 (Verifiable Activation of Parametric KnowledgE), 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. Experiments across seven benchmarks and models from 3B to 14B show that VAKE consistently outperforms standard baselines, including when transferring directly from HotpotQA to OOD datasets. LLM-based evaluation further shows that over 80% of the inserted triples provide factual bridging knowledge not derivable from the retrieved context, while more than half elicit knowledge inaccessible through direct prompting. These results suggest that VAKE activates latent parametric knowledge rather than copying the input context or memorizing dataset-specific associations.
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