Existing vector-based memory systems rely on cosine or inner product similarity within real-valued embedding spaces. While computationally efficient, these approaches are inherently phase-insensitive and have limited ability to capture resonance phenomena, which are crucial for semantic representation. In this paper, we propose a novel framework, Wave-Based Semantic Memory, which models knowledge as wave patterns $\psi(x) = A(x) e^{i\phi(x)}$ and retrieves it via resonance-based interference. This approach preserves both amplitude and phase information, enabling richer and more powerful semantic similarity. We demonstrate that resonance-based retrieval achieves higher discriminative power in cases where vector approaches fail, such as phase shifts, negation, and compositional queries. Our implementation, ResonanceDB, demonstrates scalability with millisecond latency for millions of patterns, establishing Wave-Based Memory as a viable alternative to vector stores for AGI-oriented inference and knowledge representation.
