7.9 KiB
7.9 KiB
EXO-AI 2025: Exocortex Substrate Architecture Specification
SPARC Phase 1: Specification
Vision Statement
This specification documents a research-oriented experimental platform for exploring the technological horizons of cognitive substrates (2035-2060), implemented as a modular SDK consuming the ruvector ecosystem. The platform serves as a laboratory for investigating:
- Compute-Memory Unification: Breaking the von Neumann bottleneck
- Learned Manifold Storage: Continuous neural representations replacing discrete indices
- Hypergraph Topologies: Higher-order relational reasoning substrates
- Temporal Consciousness: Causal memory architectures with predictive retrieval
- Federated Intelligence: Distributed cognitive meshes with cryptographic sovereignty
1. Problem Domain Analysis
1.1 The Von Neumann Bottleneck
Current vector databases suffer from fundamental architectural limitations:
| Limitation | Current Impact | 2035+ Resolution |
|---|---|---|
| Memory-Compute Separation | ~1000x energy overhead for data movement | Processing-in-Memory (PIM) |
| Discrete Storage | Fixed indices require explicit CRUD operations | Learned manifolds with continuous deformation |
| Flat Vector Spaces | Insufficient for complex relational reasoning | Hypergraph substrates with topological queries |
| Stateless Retrieval | No temporal/causal context | Temporal knowledge graphs with predictive retrieval |
1.2 Target Characteristics by Era
2025-2035: Transition Era
├── PIM prototypes reach production
├── Neuromorphic chips with native similarity ops
├── Hybrid digital-analog compute
└── Energy: ~100x reduction from current GPU inference
2035-2045: Cognitive Topology Era
├── Hypergraph substrates dominate
├── Sheaf-theoretic consistency
├── Temporal memory crystallization
├── Agent-substrate symbiosis begins
2045-2060: Post-Symbolic Integration
├── Universal latent spaces (all modalities)
├── Substrate metabolism (autonomous optimization)
├── Federated consciousness meshes
└── Approaching thermodynamic limits
2. Functional Requirements
2.1 Core Substrate Capabilities
FR-001: Learned Manifold Engine
- Description: Replace explicit vector indices with implicit neural representations
- Rationale: Eliminate discrete operations (insert/update/delete) in favor of continuous manifold deformation
- Acceptance Criteria:
- Query execution via gradient descent on learned topology
- Storage as model parameters, not data records
- Support for Tensor Train decomposition (100x compression target)
FR-002: Hypergraph Reasoning Substrate
- Description: Native hyperedge operations for higher-order relational reasoning
- Rationale: Flat vector spaces insufficient for complex multi-entity relationships
- Acceptance Criteria:
- Hyperedge creation spanning arbitrary entity sets
- Topological queries (persistent homology primitives)
- Sheaf-theoretic consistency across distributed manifolds
FR-003: Temporal Memory Architecture
- Description: Memory with causal structure, not just similarity
- Rationale: Agents need temporal context for predictive retrieval
- Acceptance Criteria:
- Causal cone indexing (retrieval respects light-cone constraints)
- Pre-causal computation hints (future context shapes past interpretation)
- Memory consolidation patterns (short-term volatility, long-term crystallization)
FR-004: Federated Cognitive Mesh
- Description: Distributed substrate with cryptographic sovereignty boundaries
- Rationale: Planetary-scale intelligence requires federated architecture
- Acceptance Criteria:
- Quantum-resistant channels between nodes
- Onion-routed queries for intent privacy
- Byzantine fault tolerance across trust boundaries
- CRDT-based eventual consistency
2.2 Hardware Abstraction Targets
FR-005: Processing-in-Memory Interface
- Description: Abstract interface for PIM/near-memory computing
- Rationale: Future hardware will execute vector ops where data resides
- Acceptance Criteria:
- Trait-based backend abstraction
- Simulation mode for development
- Hardware profiling hooks
FR-006: Neuromorphic Backend Support
- Description: Interface for spiking neural network accelerators
- Rationale: SNNs offer 1000x energy reduction potential
- Acceptance Criteria:
- Spike encoding/decoding for vector representations
- Event-driven retrieval patterns
- Integration with neuromorphic simulators
FR-007: Photonic Compute Path
- Description: Optical neural network acceleration path
- Rationale: Sub-nanosecond latency, extreme parallelism
- Acceptance Criteria:
- Matrix-vector multiply abstraction for optical accelerators
- Hybrid digital-photonic dataflow
- Error correction for analog precision
3. Non-Functional Requirements
3.1 Performance Targets
| Metric | 2025 Baseline | 2035 Target | 2045 Target |
|---|---|---|---|
| Query Latency | 1-10ms | 1-100μs | 1-100ns |
| Energy per Query | ~1mJ | ~1μJ | ~1nJ |
| Scale (vectors) | 10^9 | 10^12 | 10^15 |
| Compression Ratio | 3-7x | 100x | 1000x (learned) |
3.2 Architectural Constraints
- NFR-001: Must consume ruvector crates as SDK (no modifications)
- NFR-002: WASM-compatible core for browser/edge deployment
- NFR-003: NAPI-RS bindings for Node.js integration
- NFR-004: Zero-copy operations where hardware permits
- NFR-005: Graceful degradation to classical compute
3.3 Security Requirements
- NFR-006: Post-quantum cryptography for all substrate communication
- NFR-007: Homomorphic encryption research path for private inference
- NFR-008: Differential privacy for federated learning components
4. Use Case Scenarios
UC-001: Cognitive Memory Consolidation
Actor: AI Agent
Precondition: Agent has accumulated working memory during session
Flow:
1. Agent triggers consolidation
2. Substrate identifies salient patterns
3. Learned manifold deforms to incorporate new memories
4. Low-salience information decays (strategic forgetting)
5. Agent can retrieve via meaning, not explicit keys
Postcondition: Long-term memory updated, working memory cleared
UC-002: Hypergraph Relational Query
Actor: Knowledge System
Precondition: Hypergraph substrate populated with entities/relations
Flow:
1. System issues topological query: "2-dimensional holes in concept cluster"
2. Substrate computes persistent homology
3. Returns structural memory features
4. System reasons about conceptual gaps
Postcondition: Topological insight available for reasoning
UC-003: Federated Cross-Agent Memory
Actor: Agent Swarm
Precondition: Multiple agents operating across trust boundaries
Flow:
1. Agent A stores memory shard with cryptographic tag
2. Agent B queries across federation
3. Substrate routes through onion network
4. Consensus achieved via CRDT reconciliation
5. Result returned without revealing query intent
Postcondition: Cross-agent memory access preserved privacy
5. Glossary
| Term | Definition |
|---|---|
| Cognitive Substrate | Hardware-software system hosting distributed reasoning |
| Learned Manifold | Continuous neural representation replacing discrete index |
| Hyperedge | Relationship spanning arbitrary number of entities |
| Persistent Homology | Topological feature extraction across scales |
| PIM | Processing-in-Memory architecture |
| Sheaf | Category-theoretic structure for local-global consistency |
| CRDT | Conflict-free Replicated Data Type |
| Φ (Phi) | Integrated Information measure (IIT consciousness metric) |
| Tensor Train | Low-rank tensor decomposition format |
| INR | Implicit Neural Representation |
References
See research/PAPERS.md for complete academic reference list.