Dynamic Encoding Selection: Adaptive Mamba and LLM Fusion for Temporal Knowledge Graph Reasoning

This paper introduces Dynamic Encoding Selection DES , a novel framework for temporal knowledge graph reasoning that adaptively fuses representations from state space models and large language models LLMs . While recent advancements in sequence modeling have improved temporal pattern recognition, they often lack the semantic understanding necessary for comprehensive reasoning. Similarly, large language models possess rich semantic knowledge but struggle with structured temporal dependencies. Our approach leverages the complementary strengths of both paradigms—employing Mamba's state space architecture to efficiently capture sequential patterns with linear complexity, while utilizing LLMs' pre-trained knowledge for semantic understanding. The key innovation lies in our adaptive fusion mechanism, which dynamically selects between sequential, semantic, or combined representations for each query based on contextual factors like temporal proximity and entity connectivity.