CRDT

Polyglot uses specialized CRDTs designed for AI research workflows to ensure research memory and conversation context remain consistent across devices and collaborators without conflicts.

Research-Specific CRDT Design

Memory Context CRDT

Traditional CRDTs are designed for generic collaborative editing. Polyglot's Memory Context CRDT is optimized for research workflow patterns:

• Temporal Consistency: Research insights have chronological relationships that must be preserved

• Causal Dependencies: Memory markers often build upon previous insights, requiring causal ordering

• Research Integrity: Conflicts resolved with research continuity as the primary concern

• Context Preservation: Memory context maintained across model switches and device synchronization

interface ResearchMemoryCRDT {
  type: 'research-memory-context';
  nodeId: string; // device or researcher identifier

  // Core CRDT State
  vectorClock: VectorClock;
  causalGraph: CausalGraph;

  // Research Memory Components
  memoryMarkers: GSetCRDT<MemoryMarker>; // grow-only set of insights
  conversationHistory: SequenceCRDT<Message>; // ordered conversation sequence
  knowledgeReferences: LWWMapCRDT<string, KnowledgeReference>; // last-writer-wins for references
  researchInsights: ResearchInsightCRDT; // custom CRDT for research insights

  // Research Context
  researchState: ResearchStateCRDT;
  modelHistory: ModelHistoryCRDT;

  // Conflict Resolution Metadata
  researchIntegrityHash: string;
  contextConsistencyValidation: boolean;
}

Research Insight CRDT

Research insights have unique properties that require specialized conflict resolution:

interface ResearchInsightCRDT {
  type: 'research-insight';
  insights: Map<string, InsightState>;

  // Insight evolution tracking
  evolutionGraph: InsightEvolutionGraph;
  confidenceUpdates: ConfidenceCRDT;
  validationHistory: ValidationCRDT;
}

interface InsightState {
  id: string;
  content: string;
  confidence: number; // 0-1 confidence score
  evidence: EvidenceSet;

  // CRDT metadata
  createdAt: Timestamp;
  updatedAt: Timestamp;
  authorId: string;
  vectorClock: VectorClock;

  // Research-specific metadata
  researchRelevance: number;
  validationStatus: 'unvalidated' | 'peer-reviewed' | 'evidence-supported' | 'contradicted';
  supersededBy?: string; // reference to newer insight that replaces this
  buildsupon?: string[]; // references to insights this builds upon
}

// Custom merge logic for research insights
function mergeInsights(local: InsightState, remote: InsightState): InsightState {
  // Research-aware conflict resolution
  if (local.evidence.strength > remote.evidence.strength) {
    return {
      ...local,
      // Incorporate non-conflicting elements from remote
      confidence: Math.max(local.confidence, remote.confidence),
      validationStatus: mergeValidationStatus(local.validationStatus, remote.validationStatus)
    };
  }

  // Handle temporal evolution of insights
  if (isEvolutionOf(remote, local)) {
    return {
      ...remote,
      buildsupon: [...(local.buildsupon || []), local.id]
    };
  }

  // Create branched insight for genuine conflicts
  return createBranchedInsight(local, remote);
}

Conversation Memory CRDT

Preserves conversation flow and context across model switches:

interface ConversationMemoryCRDT {
  type: 'conversation-memory';
  conversationId: string;

  // Message sequence with research context
  messages: RGASequenceCRDT<ResearchMessage>; // Replicated Growable Array for messages
  memoryMarkers: GSetCRDT<MemoryMarker>; // Grow-only set of research insights
  modelSwitches: ModelSwitchCRDT; // Track model changes with context preservation

  // Research context preservation
  contextSnapshots: ContextSnapshotCRDT;
  knowledgeIntegration: KnowledgeIntegrationCRDT;

  // Cross-conversation links for research projects
  crossReferences: CrossReferenceCRDT;
}

interface ModelSwitchCRDT {
  switches: GSetCRDT<ModelSwitchEvent>;
  contextPreservation: LWWMapCRDT<string, ContextPreservationRecord>;
}

interface ModelSwitchEvent {
  id: string;
  timestamp: Timestamp;
  fromModel: string;
  toModel: string;
  contextTransferIntegrity: number; // 0-1 score
  memoryPreservation: 'full' | 'summary' | 'markers-only';

  // CRDT metadata
  vectorClock: VectorClock;
  authorId: string;
}

Knowledge Base CRDT

Manages RAG documents and MCP integrations across researchers:

interface KnowledgeBaseCRDT {
  type: 'knowledge-base';
  projectId: string;

  // Document management
  documents: LWWMapCRDT<string, RAGDocument>;
  documentChunks: LWWMapCRDT<string, DocumentChunk>;
  embeddings: EmbeddingCRDT;

  // MCP integrations
  mcpConnections: GSetCRDT<MCPConnection>;
  toolUsageLog: SequenceCRDT<ToolUsageEvent>;

  // Research-specific knowledge tracking
  knowledgeGraph: KnowledgeGraphCRDT;
  citationNetwork: CitationNetworkCRDT;

  // Access control and privacy
  accessControl: AccessControlCRDT;
}

interface KnowledgeGraphCRDT {
  nodes: GSetCRDT<ConceptNode>; // concepts don't get deleted, only evolved
  edges: LWWMapCRDT<string, ConceptRelationship>; // relationships can be updated
  conceptEvolution: ConceptEvolutionCRDT;
}

// Custom merge for knowledge updates
function mergeKnowledgeUpdates(local: RAGDocument, remote: RAGDocument): RAGDocument {
  // Preserve research citations and usage tracking
  const mergedCitations = mergeCitations(local.citations, remote.citations);
  const mergedUsage = mergeUsageMetrics(local.usageMetrics, remote.usageMetrics);

  // Content updates use timestamp-based resolution with research context awareness
  if (remote.lastModified > local.lastModified &&
      remote.researchRelevanceScore >= local.researchRelevanceScore) {
    return {
      ...remote,
      citations: mergedCitations,
      usageMetrics: mergedUsage,
      previousVersions: [...local.previousVersions, local.id]
    };
  }

  return {
    ...local,
    citations: mergedCitations,
    usageMetrics: mergedUsage
  };
}

Collaborative Research CRDTs

Multi-Researcher Project CRDT

Coordinates multiple researchers while preserving individual privacy:

interface CollaborativeProjectCRDT {
  type: 'collaborative-research-project';
  projectId: string;

  // Individual researcher contributions (encrypted)
  researcherContributions: Map<string, EncryptedResearcherCRDT>;

  // Shared research components
  sharedMethodology: LWWMapCRDT<string, MethodologyComponent>;
  aggregatedInsights: AggregatedInsightsCRDT;
  comparativeAnalyses: ComparativeAnalysesCRDT;

  // Collaboration coordination
  researcherPresence: PresenceCRDT; // who's actively researching
  collaborationEvents: SequenceCRDT<CollaborationEvent>;

  // Privacy-preserving aggregation
  anonymizedMetrics: AnonymizedMetricsCRDT;
}

interface EncryptedResearcherCRDT {
  researcherId: string;
  encryptedData: EncryptedBlob; // contains individual ConversationMemoryCRDT
  publicMetadata: {
    lastActiveTimestamp: Timestamp;
    contributionCount: number;
    researchPhase: string;
  };

  // Contribution signatures for aggregation without decryption
  contributionSignatures: GSetCRDT<ContributionSignature>;
}

Privacy-Preserving Aggregation CRDT

Enables collaborative insights while maintaining individual privacy:

interface AggregatedInsightsCRDT {
  type: 'aggregated-research-insights';

  // Anonymized insights that can't be traced to individuals
  anonymizedInsights: GSetCRDT<AnonymizedInsight>;
  consensusInsights: ConsensusInsightsCRDT;

  // Statistical aggregations without individual data exposure
  researchMetrics: StatisticalAggregationCRDT;

  // Collaborative validation without revealing individual votes
  privateVoting: PrivateVotingCRDT;
}

interface AnonymizedInsight {
  id: string;
  content: string;
  aggregatedConfidence: number;
  supportingEvidence: number; // count, not individual evidence
  researchDomain: string;

  // No individual attribution - cryptographically anonymized
  contributorCount: number;
  consensusLevel: 'emerging' | 'supported' | 'strong-consensus' | 'universal';

  // CRDT metadata
  vectorClock: VectorClock;
  aggregationHash: string; // verifies proper anonymization
}

CRDT Conflict Resolution for Research

Research-Aware Conflict Resolution

interface ResearchConflictResolution {
  // Priority-based resolution for research data types
  resolutionPriorities: {
    memoryContextIntegrity: 'critical',      // never lose research memory
    temporalConsistency: 'critical',         // maintain chronological order
    researchContinuity: 'high',             // preserve research flow
    collaborativeConsistency: 'high',       // maintain team coordination
    individualPrivacy: 'high',              // protect individual research data
    dataCompleteness: 'medium'              // prefer complete over partial data
  };

  // Custom resolution strategies for research conflicts
  conflictResolutionStrategies: {
    memoryMarkerConflicts: 'preserve-all-create-synthesis',
    insightEvolutionConflicts: 'temporal-ordering-with-branching',
    knowledgeReferenceConflicts: 'evidence-strength-weighted',
    collaborativeDataConflicts: 'privacy-preserving-merge',
    modelPerformanceConflicts: 'statistical-aggregation'
  };
}

// Example: Memory marker conflict resolution
function resolveMemoryMarkerConflicts(
  local: MemoryMarker[],
  remote: MemoryMarker[]
): MemoryMarker[] {

  const merged: MemoryMarker[] = [];

  // Add all non-conflicting markers
  const localIds = new Set(local.map(m => m.id));
  const remoteIds = new Set(remote.map(m => m.id));

  // Preserve all unique insights
  merged.push(...local.filter(m => !remoteIds.has(m.id)));
  merged.push(...remote.filter(m => !localIds.has(m.id)));

  // Handle conflicts with research-aware resolution
  for (const localMarker of local) {
    const remoteMarker = remote.find(r => r.id === localMarker.id);
    if (remoteMarker) {
      // Resolve conflict preserving research value
      const resolvedMarker = resolveMarkerConflict(localMarker, remoteMarker);
      merged.push(resolvedMarker);
    }
  }

  // Maintain temporal ordering for research continuity
  return merged.sort((a, b) => a.timestamp.compare(b.timestamp));
}

function resolveMarkerConflict(local: MemoryMarker, remote: MemoryMarker): MemoryMarker {
  // Research-specific conflict resolution logic
  if (local.evidence.strength > remote.evidence.strength) {
    return {
      ...local,
      // Incorporate additional evidence from remote
      evidence: combineEvidence(local.evidence, remote.evidence),
      confidence: Math.max(local.confidence, remote.confidence),
      validationHistory: mergeValidationHistory(local.validationHistory, remote.validationHistory)
    };
  } else if (remote.evidence.strength > local.evidence.strength) {
    return {
      ...remote,
      evidence: combineEvidence(local.evidence, remote.evidence),
      confidence: Math.max(local.confidence, remote.confidence),
      validationHistory: mergeValidationHistory(local.validationHistory, remote.validationHistory)
    };
  } else {
    // Equal evidence strength - create synthesis insight
    return createSynthesisInsight(local, remote);
  }
}

Performance Optimization for Research CRDTs

interface ResearchCRDTOptimization {
  // Memory management for large research projects
  memoryOptimization: {
    contextCompression: 'semantic-preserving-compression',
    historicalDataArchiving: 'research-importance-based',
    cacheStrategy: 'research-workflow-aware',
    garbageCollection: 'preserve-research-critical-data'
  };

  // Network optimization for research collaboration
  networkOptimization: {
    deltaCompression: 'research-context-aware',
    prioritizedSync: 'memory-context-first',
    batchOptimization: 'research-operation-grouping',
    conflictReduction: 'predictive-conflict-avoidance'
  };

  // Storage optimization for long-term research
  storageOptimization: {
    indexingStrategy: 'research-semantic-indexing',
    compressionAlgorithm: 'research-data-optimized',
    accessPatterns: 'research-workflow-optimized',
    archivalStrategy: 'research-value-preserving'
  };
}

This CRDT implementation ensures that Polyglot can maintain research data consistency and integrity across distributed research environments while preserving the unique requirements of AI research workflows.