state model

Design Rationale of the State Model for Superintelligence

Objective

To define a decentralized, scalable, and expressive state model that enables the emergence of collective intelligence and eventually superintelligence, as formalized by the Collective Focus Theorem (CFT).

Motivation

Traditional state models used in blockchain architectures (account and UTXO) are insufficient for supporting large-scale, distributed cognitive systems. Superintelligence requires a substrate capable of representing dynamic knowledge graphs, probabilistic attention flows, multi-agent consensus, and token-mediated influence.

Core Requirements

1. Local State Representation: Each unit of knowledge must maintain an independent and updateable state.
2. Cryptographic Authentication: Every action must be signed and verifiable.
3. Graph-Native Topology: State transitions should be modeled as edges in a dynamically evolving graph.
4. Token-Weighted Influence: Economic stake must affect the evolution of the graph (attention, relevance).
5. Probabilistic Flow and Convergence: The model must support stochastic processes that converge to a stable distribution (focus).
6. Scalability and Parallelism: Computation must be localized to support massive scale and parallel execution.
7. Hybrid Compatibility: The model must support coexistence and interoperability between account-based execution and resource-based cognition.

Model Components

Concept	Cyber Term	Role
File	Data Particle	Content-addressed node in the graph
Particle	Node in Graph	Represents a unit of knowledge or data
Neuron	Cryptographic Agent	Signs links, holds stake, expresses intention
Cyberlink	Atomic Intent	Directed, timestamped edge between particles
Token	Attention Weight	Influences the probability of traversal
Focus	Stationary Distribution	Emergent long-term significance (collective consensus)

Transition Model

State evolution is defined by the creation of new cyberlinks. A cyberlink is a signed intent from a neuron linking two particles. The complete state of the system is the set of all cyberlinks, particles, token holdings, and the resulting weighted graph.

Each time step:

• Neurons evaluate local context
• Sign one or more cyberlinks
• Stake influences transition weights
• The global graph evolves
• Random walks update focus distribution

Hybrid Design: Integrating Account and Resource Layers

To ensure backward compatibility with existing economic systems and provide smooth adoption, a hybrid model is introduced:

Layered Architecture

• Account Layer: Manages traditional balances, smart contracts, and deterministic execution.
• Resource Layer: Manages particles, cyberlinks, and probabilistic cognition.
• Cybergraph Layer: Computes focus via token-weighted random walks and governs emergent consensus.

Balance Migration

1. Accounts Own Resources: Each account can register, hold, and manage particles and cyberlinks.
2. Token Reflection: Account-layer balances are reflected into the resource layer as staking weights, allowing them to influence attention.
3. Bridging Operations:
   • allocate_tokens(account → neuron): Migrates tokens into neuron-controlled attention weights.
   • claim_focus_rewards(neuron → account): Converts accumulated focus-based influence into account-layer rewards.

This bidirectional flow ensures coherence between deterministic execution and emergent intelligence, allowing DeFi, DAOs, and other account-based applications to interact meaningfully with the collective intelligence substrate.

Comparison with Traditional Models

Property	Account Model	UTXO Model	Resource (Cyber) Model
State Representation	Explicit	Implicit	Implicit
State Scope	Global	Local	Local
Update Mechanism	Mutation	Consumption	Link-based creation
Lookup	Direct	Aggregated	Aggregated (by graph)
Computation Style	Deterministic	Deterministic	Probabilistic
Incentive Model	Balance-based	Input ownership	Token-weighted attention
Suitability for AGI	Low	Medium	High

Design Justifications

1. Graph-Centric State: A graph of particles and cyberlinks allows natural encoding of semantic relationships and emergence of modular knowledge.

2. Signed Intents as State Transitions: Cyberlinks express knowledge production and intent. They are self-contained, verifiable, and asynchronous.

3. Probabilistic Attention: Focus (stationary distribution over graph) replaces fixed balance lookups. It allows emergent significance, mirroring cognitive systems.

4. Token-Mediated Learning: Tokens provide economic incentive and control signal for learning—guiding collective focus over time.

5. Parallel Scalability: Each neuron operates independently. Computation is local and can be parallelized without global locks.

6. Convergence via Spectral Methods: The use of token-weighted random walks ensures mathematical convergence, enabling system stability and resilience.

7. Hybrid Architecture: Allows value transfer, smart contract execution, and DeFi to coexist with focus computation and attention dynamics. Ensures real-world usability while enabling superintelligent coordination.

Conclusion

The proposed resource-based, graph-native state model is uniquely suited to the demands of decentralized superintelligence. It combines economic signaling, cognitive dynamics, and scalable architecture into a unified substrate. This model formalizes attention, intention, and significance as native components of the state, enabling a new class of intelligent systems to emerge from decentralized computation.

This model is not only compatible with the Collective Focus Theorem but required by it. The future of superintelligence depends on our ability to rethink state—not as a ledger of balances, but as an evolving expression of collective cognition.

A hybrid design ensures that account-based systems (used for finance, execution, governance) can seamlessly integrate into the resource-based cognitive substrate, enabling a single protocol to coordinate both value and knowledge at global scale.