GAKU: Towards Dynamic Cognition on Knowledge Graphs

Knowledge graphs (KGs) provide structured and reliable factual knowledge, yet large language models (LLMs) often struggle to align their parametric knowledge with the semantics, conventions, and coverage of a specific KG. This mismatch is particularly problematic in domain-specific knowledge reasoning, where conventional adaptation often requires substantial annotation and fine-tuning. In this paper, we propose GAKU, a multi-agent framework that builds dynamic cognition over KGs. GAKU follows a human-like cognitive loop: inspecting local graph evidence, reflecting on mistakes, inducing ontology-level cognition, validating them, and reusing the validated knowledge in subsequent decisions. The resulting cognition state bridges episodic experience and reusable reasoning skills, enabling an LLM to adapt to unfamiliar KG-specific semantics without parameter fine-tuning. Extensive experiments on KG fact verification and completion show that GAKU consistently outperforms strong fine-tuning and training-free baselines, using only 100 labeled examples to construct dynamic cognition. Further analyses demonstrate that the gains generalize across diverse backbone LLMs and support cross-model cognition transfer, highlighting explicit cognitive adaptation as a data-efficient and parameter-free approach to KG-grounded reasoning.