ACS Development TODO β
Context: ACS (Adaptive Context Scaling) is the cognitive budget manager and a context assembly engine. This document tracks implementation priorities and documentation improvements needed to reach production readiness.
High Priority (v0 MVP) β
Core Algorithm Implementation β
[ ] Implement benefit/cost scoring function
- TypeScript implementation of scoring components (Ξ±Β·entity_overlap + Ξ²Β·recency + Ξ³Β·citation)
- Unit tests for edge cases (empty entities, zero costs, identical scores)
- Performance benchmarking for large candidate sets (1000+ fragments)
[ ] Complete tie-breaking algorithm
- Implement deterministic tie-breaking chain (freshness β citations β size β ID)
- Validate stable sorting behavior across different JavaScript engines
- Add comprehensive test coverage for tie-breaking scenarios
[ ] KV Policy engine
- Implement pin/compress/evict policy generation
- Memory management with configurable cache size (64 items default)
- Eviction scoring algorithm with age/size/benefit factors
Provider Integration β
[ ] L2 Provider adapter
- Project Library integration with graph traversal
- Handle PROJECT_EMPTY warning scenario gracefully
- Implement timeout handling and partial response processing
[ ] L4 Provider adapter
- Experience Layer integration with temporal scoring
- Historical pattern matching and success rate weighting
- Session correlation and learning from outcomes
[ ] L1 Provider adapter
- Noosphere integration with hyperbolic search
- Multi-method coordination (vector + graph + agents)
- Global knowledge quality assessment and filtering
Medium Priority (v0.1) β
Performance Optimization β
[ ] Caching layer
- Fragment cache with LRU eviction
- Query result caching with TTL policies
- Cache warming strategies for common queries
[ ] Parallel processing
- Concurrent provider requests with deadline management
- Result streaming and early termination optimization
- Resource pool management for provider connections
[ ] Metrics and observability
- Performance metrics collection (latency, hit rates, quality scores)
- Real-time dashboards for ACS operation monitoring
- A/B testing framework for algorithm improvements
Algorithm Refinements β
[ ] Dynamic budget allocation
- Query complexity estimation for budget sizing
- Adaptive timeout adjustment based on historical performance
- Budget redistribution when providers exceed deadlines
[ ] Quality-cost optimization
- Multi-objective optimization for quality vs. latency trade-offs
- Learned scoring models with offline evaluation framework
- User feedback integration for quality assessment
Low Priority (v0.2+) β
Advanced Features β
[ ] Subgraph expansion
- Pre-selection graph traversal for related concepts
- Graph hop optimization with benefit prediction
- Cycle detection and infinite expansion prevention
[ ] Context coherence
- Fragment relationship analysis for coherent context assembly
- Conflict detection between information sources
- Narrative flow optimization for assembled context
[ ] Learning and adaptation
- Session-based learning from user interactions
- Pattern recognition in successful query resolutions
- Personalization based on user preferences and domain expertise
Documentation Improvements β
Technical Specifications β
[ ] Complete algorithm pseudocode
- Formal mathematical notation for benefit/cost functions
- Complexity analysis and performance characteristics
- Reference implementation in multiple languages (TypeScript, Python, Rust)
[ ] Integration patterns
- Common integration scenarios with code examples
- Error handling best practices and recovery strategies
- Testing strategies for ACS-dependent systems
Developer Resources β
[ ] SDK development
- Client libraries for popular languages
- Mock providers for testing and development
- Integration testing utilities and test fixtures
[ ] Performance guides
- Tuning guides for different workload patterns
- Capacity planning and scaling recommendations
- Troubleshooting guide for common performance issues
Research and Exploration β
Theoretical Improvements β
[ ] Hyperbolic geometry optimization
- Investigate geodesic path optimization for fragment selection
- Curvature-based relevance scoring experiments
- Hierarchical embedding space navigation algorithms
[ ] Cognitive load modeling
- Human cognitive load prediction for context assembly
- Attention-based fragment ordering and presentation
- Progressive disclosure patterns for complex information
Future Architecture β
[ ] Federated ACS
- Multi-tenant ACS deployment patterns
- Cross-organization knowledge sharing with privacy preservation
- Collaborative context assembly across multiple knowledge bases
[ ] Real-time adaptation
- Online learning algorithms for continuous improvement
- Real-time A/B testing of scoring functions
- Dynamic algorithm selection based on query characteristics
Implementation Notes β
Current Status (v0) β
- β Core architecture documented with PhD-level detail
- β API contracts specified and organized
- β Provider interface standardized
- β Basic algorithm framework designed
- π§ Implementation in progress
- π§ Integration testing ongoing
Dependencies β
- Provider APIs: L1, L2, L4 implementations must be available for testing
- Schema validation: JSON schema definitions for all API contracts
- Test infrastructure: Comprehensive test suite for algorithm validation
- Monitoring stack: Observability infrastructure for production deployment
Success Metrics β
- Performance: < 200ms p95 latency for context assembly
- Quality: > 0.8 user satisfaction score for assembled context
- Reliability: 99.9% uptime with graceful degradation
- Scalability: 1000+ concurrent requests with linear resource scaling
Getting Started β
- Review architecture documentation in
./architecture.md - Study algorithm specifications in
./planner-and-selection.md - Understand API contracts in
../api/internal.md - Examine provider interface in
../api/provider.md - Start with high-priority implementation tasks above
For questions or clarifications, refer to the comprehensive provider specification at ../api/provider-specification.md.