ACS (Adaptive Context Scaling) Component

Overview: ACS serves as the "cognitive budget manager" of Mnemoverse orchestration, making intelligent decisions about what information to include in context under strict resource constraints. It transforms the fundamental trade-off between quality, latency, and cost into mathematically optimized selections.

Component Architecture

Core Responsibilities

1. Intelligent Context Planning

• Function: Transform user intent into optimal retrieval strategy
• Scope: Multi-source coordination, LOD (Level-of-Detail) optimization, parallel execution planning
• Features: Query complexity analysis, provider capability matching, deadline management
• Output: Execution plan with provider allocations and performance expectations

2. Adaptive Budget Management

• Function: Dynamically allocate computational resources for maximum benefit
• Scope: Token budgets, time constraints, quality targets, provider cost optimization
• Features: Benefit/cost scoring, tie-breaking algorithms, graceful degradation
• Output: Resource allocation matrix with fallback strategies

3. Multi-Provider Orchestration

• Function: Coordinate parallel knowledge retrieval across heterogeneous sources
• Scope: L1 (Noosphere), L2 (Project Library), L4 (Experience) integration
• Features: Timeout handling, partial results, provider health monitoring
• Output: Candidate fragments with quality metadata and source attribution

4. Context Assembly & Quality Assurance

• Function: Select and organize optimal information fragments
• Scope: Fragment ranking, diversity optimization, coherence validation
• Features: Mathematical benefit/cost optimization, KV policy generation, quality scoring
• Output: Assembled context with KV management policies

Key Algorithms

Benefit/Cost Scoring Model

interface ScoringModel {
  // Core benefit calculation
  calculateBenefit(fragment: Fragment, gaze: Gaze): number {
    const α = 0.6; // Entity overlap weight  
    const β = 0.3; // Recency weight
    const γ = 0.1; // Citation weight
    
    const entity_overlap = this.calculateEntityOverlap(fragment.entities, gaze.entities);
    const recency = this.calculateRecencyScore(fragment.timestamp);
    const citation_availability = fragment.citations.length > 0 ? 1.0 : 0.5;
    
    return α * entity_overlap + β * recency + γ * citation_availability;
  }
  
  // Normalized cost-benefit ratio
  calculateScore(fragment: Fragment, gaze: Gaze): number {
    const benefit = this.calculateBenefit(fragment, gaze);
    const cost = fragment.cost_tokens;
    return benefit / (1 + cost / 1000);
  }
}

Greedy Selection with Tie-Breaking

interface SelectionAlgorithm {
  selectFragments(candidates: Fragment[], budgets: Budgets): SelectionResult {
    // 1. Score and sort candidates
    const scored = candidates
      .map(fragment => ({ fragment, score: this.calculateScore(fragment, this.gaze) }))
      .sort((a, b) => b.score - a.score);
    
    // 2. Apply sophisticated tie-breaking
    const ranked = this.applyTieBreaking(scored);
    
    // 3. Greedy selection under budget constraints
    return this.selectUnderBudget(ranked, budgets);
  }
}

KV Policy Generation

interface KVPolicyEngine {
  generatePolicy(selectedFragments: Fragment[]): KVPolicy {
    return {
      pin: selectedFragments
        .filter(f => f.lod === 'macro')
        .map(f => f.id),
      compress: selectedFragments
        .filter(f => f.cost_tokens > 500 && f.benefit >= 0.3 && f.benefit <= 0.6)
        .map(f => f.id),
      evict: this.selectEvictionCandidates(this.currentCache, 64)
    };
  }
}

Performance Characteristics

Latency Targets

• Planning Phase: < 15ms (p95) - intent analysis and provider coordination
• Provider Orchestration: < 200ms (p95) - parallel fetching with timeouts
• Selection & Assembly: < 25ms (p95) - fragment ranking and policy generation
• End-to-End Processing: < 240ms (p95) - total ACS processing time

Throughput Capacity

• Single Instance: 200+ requests/second (context assembly operations)
• Provider Coordination: 1000+ concurrent provider requests
• Memory Efficiency: < 50MB base footprint, scales linearly with cache size

Quality Metrics

• Selection Accuracy: > 85% optimal fragment selection vs. exhaustive search
• Budget Efficiency: < 5% resource waste through optimization
• Coverage Quality: > 75% entity coverage for domain-specific queries
• Provider Success Rate: > 95% successful multi-provider coordination

Integration Architecture

Upstream Integration (CEO Component)

interface CEOIntegration {
  // Receives structured requests from CEO
  processRenderRequest(request: CEORenderRequest): Promise<ACSResponse>;
  
  // Request transformation and validation
  validateRequest(request: CEORenderRequest): ValidationResult;
  
  // Error reporting back to CEO
  reportError(error: ACSError): CEOErrorResponse;
}

Downstream Integration (Knowledge Providers)

interface ProviderIntegration {
  // Standardized provider interface
  fetchCandidates(request: ProviderRequest): Promise<ProviderResponse>;
  
  // Health monitoring and performance tracking
  monitorProvider(providerId: string): ProviderHealth;
  
  // Dynamic provider selection and load balancing
  selectOptimalProvider(query: Query, constraints: Constraints): ProviderId;
}

Horizontal Integration (HCS Component)

interface HCSIntegration {
  // Future streaming coordination (v0.2+)
  prepareStreamingContext(fragments: Fragment[]): StreamingPlan;
  
  // Progressive delivery recommendations
  generateDeliveryStrategy(fragments: Fragment[], userPrefs: UserPreferences): DeliveryStrategy;
}

Current Implementation Status

✅ Completed (v0)

• Architecture specification (28,000+ lines) - comprehensive technical design
• Core algorithms documented - benefit/cost model, tie-breaking rules, KV policies
• Provider API standardized - unified interface for L1/L2/L4 integration
• Contract definitions finalized - JSON schemas for CEO↔ACS communication
• TODO roadmap created - implementation priorities and success metrics

🚧 In Progress (v0 MVP)

• Core algorithm implementation - TypeScript/Python reference implementations
• Provider adapter development - L1/L2/L4 integration modules
• Performance testing framework - benchmarking and optimization validation
• Monitoring and observability - metrics collection and alerting systems

📋 Planned (v0.1+)

• Machine learning enhancements - learned scoring models with offline evaluation
• Advanced caching strategies - intelligent fragment caching with TTL policies
• Subgraph expansion - pre-selection graph traversal for related concepts
• A/B testing framework - continuous algorithm improvement and validation

Development Resources

Technical Specifications

• Architecture: ./architecture.md - Comprehensive 28,000+ line specification
• Algorithms: ./planner-and-selection.md - Detailed algorithm documentation
• Contracts: ./contracts.md - JSON schema definitions
• Development Plan: ./TODO.md - Implementation roadmap and priorities

API Documentation

• Internal API: ../api/internal.md - CEO↔ACS communication protocols
• Provider API: ../api/provider.md - Quick reference for provider integration
• Provider Specification: ../api/provider-specification.md - Comprehensive provider architecture

Integration Guides

• Overall System: ../README.md - Orchestration overview and component relationships
• End-to-End Examples: ../../walkthrough.md - Complete request flow demonstrations
• CEO Integration: ../ceo/README.md - Upstream component integration
• HCS Integration: ../hcs/README.md - Downstream streaming coordination

Usage Patterns

For Component Developers

1. Study core algorithms in ./planner-and-selection.md
2. Review architecture in ./architecture.md for system design
3. Understand provider integration via ../api/provider.md
4. Follow implementation plan in ./TODO.md
5. Test with examples from ../../walkthrough.md

For Provider Implementers

1. Start with provider quick reference in ../api/provider.md
2. Deep dive into specification in ../api/provider-specification.md
3. Review timeout and error handling patterns
4. Implement required JSON schemas from ./contracts.md
5. Test integration with ACS mock implementations

For System Integrators

1. Understand orchestration flow in ../README.md
2. Review CEO→ACS contracts in ../api/internal.md
3. Study error handling patterns and recovery strategies
4. Plan capacity and scaling based on performance characteristics
5. Configure monitoring for production deployment

Architecture Philosophy

Cognitive Realism

ACS implements principles from Cognitive Load Theory and Working Memory research:

• Limited capacity assumption - humans can only process ~7±2 information chunks
• Level-of-detail adaptation - adjust information granularity based on cognitive budget
• Attention management - prioritize most relevant information under resource constraints

Mathematical Optimization

Multi-objective optimization balancing:

• Quality: Semantic relevance and information completeness
• Cost: Computational resources (tokens, time, provider costs)
• Diversity: Information source variety and perspective coverage
• Coherence: Logical consistency and narrative flow

System Reliability

Production-ready design patterns:

• Circuit breaker pattern - prevent cascade failures across providers
• Graceful degradation - maintain functionality under resource pressure
• Observability first - comprehensive metrics and alerting
• Horizontal scalability - stateless design for cloud deployment

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Component Status: Architecture Complete → Implementation Active → Production Target (v0.1)

Next Steps: Focus on high-priority TODO items - core algorithm implementation and provider adapter development.