From Implicit Parameters to Explicit Knowledge Graphs: A Structured Knowledge Recall Framework for Bidirectional Generalization in LLMs

Large Language Models (LLMs) have become a core component of modern intelligent systems for knowledge-intensive applications. However, despite storing vast amounts of factual knowledge in their parameters, they often fail to effectively utilize this knowledge for bidirectional reasoning. A representative manifestation of this limitation is the “Reversal Curse”, where models trained on “A is B” cannot generalize to “B is A.” To systematically diagnose this deficiency, we design a knowledge graph-based structured recall evaluation framework that assesses whether LLMs can externalize internally stored knowledge into explicit relational triples. Evaluating six LLMs of varying scales and training strategies on a benchmark of 4000 entities with 84,142 Wikidata triples, we find that even the strongest models achieve less than 12% zero-shot structured recall accuracy on the model-specific benchmark, and recall rates remain critically low on a cross-validated intersection benchmark that controls for dataset variation. This finding suggests that the bottleneck lies not merely in knowledge storage, but in the model’s ability to actively retrieve and organize its parametric knowledge into structured representations that support inference. To address this, we propose the Structured Knowledge Recall (SKR) framework, a curriculum learning-inspired two-stage training paradigm that teaches models to first recall entity-centered knowledge graphs from their parameters and then perform reasoning over the explicitly recalled structures. Extensive experiments across four datasets and three model backbones demonstrate that SKR substantially mitigates the Reversal Curse, achieving over 92% reverse generalization accuracy while maintaining forward accuracy above 98%, and that endogenously recalled structured knowledge significantly outperforms externally injected knowledge, offering a new practical pathway for building more robust intelligent language systems.