Wave Based Semantic Memory · ψ-stack · phase-aware retrieval
Wave Based Semantic Memory — ψ-stack for high-precision retrieval
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EvaCortex Lab builds Wave Based Semantic Memory — a ψ-stack that you can add
alongside existing models, embeddings and RAG. Keep your current stack and gain phase-coded memory and
resonance-grade retrieval for high-precision, interference-based search.
Built for teams already running vector search, RAG or agentic workflows in domains like
pharma, genetics, toxicology,
AML/KYC, finance and compliance
— where retrieval precision, explainability and auditability are non-negotiable.
Products
Our wave-based semantic stack comprises interoperable modules. Each product operates on amplitude/phase encodings;
together they form a cohesive ψ-stack that extends existing RAG, vector search and agentic systems.
Phase-aware memory engine that stores ψ-patterns and retrieves through interference and resonance rather than distance alone.
Semantic ψ-codec that projects text into ψ-patterns with amplitude A and phase φ, making salience, modality and relations explicit.
Graph-augmented RAG engine in Wave-RAG, Graph-RAG or hybrid modes, assembling provenance-linked semantic subgraphs.
Trace-first multi-agent orchestrator managing plans, dependency graphs and rollback points so reasoning remains auditable.
Why embeddings aren’t enough
Most modern RAG systems rely on vector search. This is a strong baseline, but magnitude-only similarity
struggles with negation, modality and
relational structure — where high-stakes systems need clarity, recall and precision.
Negation & modality
Queries like “patients where drug X is not recommended” may retrieve passages about recommendations for X, because
magnitude-only vectors place them in the same neighbourhood without a dedicated channel for semantic orientation.
Relations & structure
Guidelines, interactions and exceptions depend on how statements relate: support, contrast, alternative, hypothetical.
Plain embeddings retrieve text that looks similar, not text that is structurally relevant in a reasoning chain.
Explainability & trust
Similarity scores can say “these vectors are close” but not which semantic components aligned, which cancelled, or
where contradictions sit — not enough for regulated environments.
Wave Based Semantic
Wave semantics represents meaning as a structured signal with multiple channels. Instead of relying only on magnitude-based
similarity, information is expressed as wave patterns whose interference reveals alignment, contrast and contextual nuance.
This complements existing retrieval pipelines by adding a phase-aware layer where meaning is compared by resonance, not only proximity.
- Amplitude: how strongly content is expressed — assertion, emphasis, salience.
- Phase: modality and relations — support, contrast, exceptions, conditional/hypothetical structure.
- Resonance retrieval: constructive/destructive interference strengthens coherent evidence and cancels contradictions.
Wave Based Semantic Memory is the representation underlying ResonanceDB. EchoThesis generates ψ-patterns, but the concept is model-agnostic
and can be introduced as a side-car layer without replacing your current stack.
Seamless migration
Wave Based Semantic Memory is designed as an upgrade path, not a rewrite. You keep your current stack and introduce the wave layer alongside it.
- Step 1: Deploy ResonanceDB next to your stack and compare retrieval offline.
- Step 2: Hybrid scoring — combine conventional similarity with resonance signals.
- Step 3: Wave-first — promote wave retrieval when it proves itself on your KPIs; keep conventional indices as fallback.