Skip to main content

RFS Use Cases — White Papers

Status: Public Documentation
Last Updated: 2025-12-11

Overview​

This directory contains white paper documentation for six high-value use cases demonstrating Resonant Field Storage (RFS) capabilities. Each use case is presented as an educational white paper focusing on problems, solutions, and business value rather than implementation details.

Use Cases​

1. Incident Memory for On-Call Teams​

Directory: 01_incident_memory_oncall/
Problem: On-call engineers struggle to find related incidents quickly; current keyword search misses relationships
RFS Solution: Superposition + interference patterns automatically discover related incidents, contradictions, and patterns
Key Value: Reduced MTTR, automatic pattern detection, explainable incident relationships

2. RAG with Proofs (Retrieval-Augmented Generation)​

Directory: 02_rag_with_proofs/
Problem: LLMs need context but current systems can't prove why documents were selected
RFS Solution: Resonance-based document selection with interference pattern explanations
Key Value: Explainable AI, better context selection, compliance-ready citations

3. Code Intelligence​

Directory: 03_code_intelligence/
Problem: Developers need to find similar code patterns; keyword search misses analogies
RFS Solution: Field-native search discovers code analogies, patterns, and contradictions
Key Value: Faster development, code reuse, pattern discovery

4. Compliance/Legal Archive​

Directory: 04_compliance_legal_archive/
Problem: Legal teams need to find related documents and prove relationships for evidence
RFS Solution: Relationship discovery + exact byte recall (AEAD-verified) with explainable connections
Key Value: Faster legal research, audit-ready explanations, exact recall for evidence

5. Research Knowledge Graph​

Directory: 05_research_knowledge_graph/
Problem: Researchers need to find related papers and understand how concepts evolved over time
RFS Solution: Temporal field dimension tracks evolution; entanglement graph shows research communities
Key Value: Knowledge discovery, temporal analysis, research community mapping

6. Pharmaceutical Discovery​

Directory: 06_pharmaceutical_discovery/
Problem: Pharmaceutical companies need to discover novel drug combinations; manual analysis misses most combinations
RFS Solution: Relationship discovery through field interference + GNN prediction for novel combinations
Key Value: Systematic combination discovery, synergy prediction, safety assessment, accelerated drug development

Quick Comparison​

Use CasePrimary RFS FeatureKey DifferentiatorEnterprise Value
Incident MemorySuperposition + InterferenceAutomatic relationship discoveryReduced MTTR, pattern detection
RAG with ProofsResonance + ExplainabilityProvable document selectionExplainable AI, compliance
Code IntelligenceField-native searchAnalogy discoveryFaster dev, code reuse
Legal ArchiveRelationships + Exact RecallExplainable connections + AEADAudit-ready, faster research
Research GraphTemporal + EntanglementEvolution tracking + communitiesKnowledge discovery
Pharmaceutical DiscoveryEntanglement + GNNNovel combination predictionAccelerated drug development, systematic discovery

Common RFS Capabilities Used​

All use cases leverage core RFS features:

  1. Superposition: All documents in one field (not isolated)
  2. Interference Patterns: Constructive (relationships) and destructive (contradictions)
  3. Resonance Search: Query field resonates with superposed field
  4. Explainability: Interference patterns explain why results were returned
  5. Exact Recall: AEAD-verified byte retrieval (where applicable)
  6. Temporal Dimension: Track evolution over time (where applicable)
  7. Entanglement Graph: Relationship mapping (where applicable)
  • Start Here: Read this README to understand all use cases
  • Deep Dive: Navigate to each use case subdirectory for detailed white paper documentation
  • RFS Overview: See RFS README for technical architecture
  • SMARTHAUS Vision: See SMARTHAUS Vision Document for the complete vision