用波动模型替代向量存储，通过共振实现更精准的语义检索。
Wave-Based Semantic Memory with Resonance-Based Retrieval: A Phase-Aware Alternative to Vector Embedding Stores
- 将知识表示为带相位的波函数，保留幅度与相位双重信息。
- 在相位偏移、否定和组合查询中表现优于传统向量方法。
- 系统可扩展至百万级条目，毫秒级响应，适合AGI知识推理。
传统向量记忆系统依赖实数嵌入空间中的余弦或内积相似度，虽计算高效，但对相位不敏感，难以捕捉意义表征中关键的共振现象。本文提出波动语义记忆框架，将知识建模为波函数 ψ(x) = A(x) e^{iϕ(x)}，并通过共振干涉实现检索。该方法同时保留幅度与相位信息，显著提升语义相似性的表达力与鲁棒性。实验表明，在相位偏移、否定及组合查询等向量方法失效的场景下，基于共振的检索具备更强区分能力。我们实现的 ResonanceDB 系统可扩展至数百万模式，延迟仅毫秒级别，为面向AGI的推理与知识表征提供了可行的波动替代方案。
原文摘要 · Abstract (English)
Conventional vector-based memory systems rely on cosine or inner product similarity within real-valued embedding spaces. While computationally efficient, such approaches are inherently phase-insensitive and limited in their ability to capture resonance phenomena crucial for meaning representation. We propose Wave-Based Semantic Memory, a novel framework that models knowledge as wave patterns $ψ(x) = A(x) e^{iϕ(x)}$ and retrieves it through resonance-based interference. This approach preserves both amplitude and phase information, enabling more expressive and robust semantic similarity. We demonstrate that resonance-based retrieval achieves higher discriminative power in cases where vector methods fail, including phase shifts, negations, and compositional queries. Our implementation, ResonanceDB, shows scalability to millions of patterns with millisecond latency, positioning wave-based memory as a viable alternative to vector stores for AGI-oriented reasoning and knowledge representation.
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