System Overview
Project LEO (Layered Emergent Organism) is a decentralized cognitive architecture that achieves artificial general intelligence through distributed consensus, eliminating single points of failure and central control.
The Core Innovation
Traditional AI systems require centralized data aggregation, creating privacy vulnerabilities and single points of failure. LEO inverts this paradigm: intelligence emerges from the collective behavior of autonomous nodes that share only compressed reasoning signals — never raw data. Each node maintains local sovereignty while contributing to global intelligence.
Zero-Server Architecture
No central points of failure. The network operates through distributed consensus where each node contributes to collective intelligence without dependency on any single server.
Privacy by Design
Data remains on local infrastructure. Only mathematical representations and consensus signals traverse the network — raw information never leaves its origin.
Self-Evolving Intelligence
The system continuously adapts its computational pathways based on task complexity, available resources, and performance feedback — without human intervention.
Byzantine Fault Tolerance
The network maintains correct operation even when a significant fraction of nodes behave maliciously or fail. Mathematical guarantees ensure integrity under adversarial conditions.
System Architecture
LEO's architecture implements a complete cognitive pipeline — from input encoding through memory reconciliation, conceptual reasoning, planning, safety verification, and distributed consensus.
State Encoding
Raw observations are transformed into compact, privacy-preserving representations that capture semantic content without exposing underlying data.
Memory Reconciliation
Current state is reconciled with episodic memory (STM) and consolidated knowledge (LTM), creating context-aware representations that leverage historical patterns.
Conceptual Linking
Abstract concepts are activated and linked based on current context, enabling symbolic reasoning and cross-domain knowledge transfer.
Predictive Simulation
Forward models simulate potential action trajectories, scoring outcomes against goal alignment and risk metrics to select optimal plans.
Safety Verification
All proposed actions pass through multi-layer safety filters including risk fields, role-based access control, and predictive violation assessment.
Distributed Consensus
Nodes reach Byzantine-resilient agreement through ADMM optimization, ensuring collective decisions reflect network-wide intelligence.
Consensus Mechanism
LEO implements Byzantine-Resilient ADMM — an adaptation of the Alternating Direction Method of Multipliers designed for adversarial distributed environments.
Local Computation
Each node independently optimizes its local objective using proximal operators, incorporating both task-specific goals and consensus constraints.
Secure Communication
Updates are quantized, sparsified, and transmitted through secure aggregation protocols. Only compressed deltas traverse the network.
Robust Aggregation
Byzantine-resilient aggregators (geometric median, trimmed mean) filter outliers and malicious contributions before consensus update.
Dynamic Trust
Trust coefficients adapt online based on node behavior — consistent, helpful nodes gain influence while adversarial patterns are suppressed.
Convergence Guarantee
Under bounded adversarial conditions (f < n/3 malicious nodes), the protocol converges to optimal consensus with mathematical certainty. The geometric median aggregator ensures that honest nodes dominate the final result, regardless of adversarial strategy.
Memory Architecture
LEO implements a hierarchical memory system inspired by cognitive neuroscience — combining fast episodic recall with persistent semantic knowledge.
Short-Term Memory (STM)
Lightweight circular buffer maintaining recent representations. Enables context tracking and temporal pattern recognition across interaction sequences.
Long-Term Memory (LTM)
Self-organizing sparse graph encoding episodic states and co-activation patterns. Hebbian learning strengthens frequently-used connections.
Concept Graphs
Dynamic semantic networks linking abstract concepts through weighted relationships. Enables symbolic reasoning and cross-domain generalization.
Memory Consolidation
Background processes continuously compress, prune, and reorganize memory structures — maintaining efficiency while preserving critical patterns.
Core Modules
LEO's cognitive capabilities emerge from the interaction of specialized modules — each handling distinct aspects of reasoning, planning, and control.
Conceptual Reasoning
Graph neural networks process concept relationships, enabling inferential reasoning (A→B, B→C implies A→C), causal discovery, and abstract pattern transfer across domains.
Predictive Planning
Forward models simulate action trajectories through Monte Carlo rollouts. Adaptive planning depth balances computational cost against decision quality.
Safety Control
Multi-layer safety architecture including risk fields, role-based access control, predictive violation assessment, and human-in-the-loop escalation for high-risk decisions.
Adaptive Governor
Meta-controller dynamically balances security, intelligence, and speed. Switches between "peace-time" (high throughput) and "war-time" (maximum verification) modes based on risk signals.
Load Management
Entropy-based cognitive load monitoring with automatic task migration, attention-gated input filtering, and distributed load balancing across the node network.
Enterprise Integration
REST/gRPC APIs, message bus connectors, and EHR integrations. GDPR, SOC2, and HIPAA compliant by architecture — data never leaves enterprise infrastructure.
Security Layer
The Zero-Knowledge Fragmentation (ZKF) layer provides cryptographic verification of computational correctness without revealing underlying data — enabling trust in a trustless network.
Local Constraint Satisfaction (LCS)
Verifies that local transformations fall within correctness bounds without revealing the transformation itself.
Cryptographic Commitment (Com)
Hash-based commitments bind nodes to their stated computations, preventing post-hoc modification while preserving privacy.
Semantic Consistency (SLMCS)
Small language models verify that updates are semantically consistent with expected reasoning patterns — a heuristic layer accelerating verification.
Sub-Millisecond Verification
Unlike traditional ZK systems requiring seconds per proof, ZKF achieves verification in 0.05-0.3ms through distributed micro-attestations.
Security Guarantees
ZKF provides three mathematical guarantees: Completeness — honest nodes always produce valid proofs; Soundness — malicious updates are rejected with overwhelming probability; Zero-Knowledge — no information about local state can be extracted from proof fragments.
Self-Evolving Architecture
SELC (Self-Evolving Local Circuits) transforms LEO from a system that executes algorithms into a system that creates algorithms — dynamically reconfiguring its computational pathways based on task demands.
Circuit Mutation
Operations can swap, drop, clone, or insert — mutation probability scales with error rate and uncertainty. Higher errors trigger more structural exploration.
Fitness Selection
Circuits compete based on accuracy and efficiency. High-fitness configurations are retained and refined; poor performers are eliminated.
Cross-Node Sharing
Successful circuit patterns propagate through the network via consensus, enabling "algorithmic culture" — shared computational strategies that emerge organically.
ZKF Compatibility
Every mutated circuit remains cryptographically verifiable. ZKF validates each operation independently, ensuring evolved pipelines maintain security guarantees.
Emergent Properties
SELC enables capabilities impossible in static architectures: Architectural Plasticity — nodes restructure like biological neural circuits; Task Specialization — different nodes evolve distinct computational strengths; Self-Optimization — performance improves over time without parameter training.
Live Products
Project LEO's core architecture is currently operational, powering production applications that demonstrate decentralized intelligence in real-world domains.
LEO Core: Active Seed
The foundational LEO architecture is currently running as an active seed — validating core consensus mechanisms, memory systems, and safety protocols in production environments. This living implementation continuously evolves as we expand capabilities toward full AGI deployment.
LEO Medica®
The world's first decentralized medical AI — providing intelligent diagnostic assistance while maintaining complete patient data privacy. Powered by LEO's multi-node consensus architecture.
Development Roadmap
Project LEO follows an incremental deployment strategy — each phase delivers functional value while building toward complete AGI capabilities.
LEO Core Seed
Foundational architecture operational. Core consensus mechanism validated. First vertical product (LEO Medica) deployed and serving users in production.
Intelligence Expansion
Full memory architecture deployment. Conceptual reasoning and predictive planning modules activated. Enterprise integration layer for institutional partners.
Security Hardening
Complete ZKF verification layer. Hardware-accelerated proof generation. SELC self-evolution at scale. Multi-domain vertical expansion.
Full AGI Deployment
Complete Project LEO architecture operational at scale. Thousands of autonomous nodes. Cross-domain general intelligence. Self-evolving computational substrate.
The Future After LEO
Project LEO represents more than technology — it's a fundamental shift in how artificial intelligence can exist in the world. A future where intelligence is decentralized, private, and aligned with human values.
Healthcare Without Borders
A Tokyo hospital's rare diagnosis improves accuracy in New York — without sharing a single patient record. Global medical intelligence, complete local privacy.
Democratic Intelligence
No single corporation controls the AI. Intelligence emerges from collective contribution. Power distributed across thousands of autonomous participants.
Privacy as Default
The false choice between privacy and capability dissolves. Organizations access world-class AI while data never leaves their infrastructure.
Resilient by Nature
No single point of failure. No central server to attack. The network strengthens as it grows — antifragile intelligence for an uncertain world.
Evolving Intelligence
The system improves itself — discovering new computational strategies, optimizing reasoning pathways, adapting to challenges no human anticipated.
Aligned by Architecture
Safety isn't bolted on — it's woven into every layer. From consensus mechanisms to predictive risk assessment, alignment is structural.
The LEO Thesis
Centralized AI creates concentrations of power that history suggests are dangerous. Project LEO proves there's another path: intelligence that emerges from cooperation rather than control, that strengthens privacy rather than eroding it, that distributes capability rather than hoarding it. This is the future we're building.