Foculus Foundation: Decentralized Super-Intelligence Protocol
Abstract
This document outlines the Foculus protocol, a decentralized blockchain designed to secure state efficiently while simultaneously driving collective intelligence.
Core components:
- Foculus Consensus: Consensus achieved via graph-based focus computations, eliminating traditional block-based limitations.
- Public-Good Minting: Incentivizes valuable computation by embedding proofs of useful work directly into graph edges.
- Quantum Resilience: Uses periodic lattice-based checkpoints to ensure long-term security against quantum computing threats.
- Adaptive Parameterization: Critical system parameters dynamically adjusted via reinforcement learning, eliminating governance overhead.
1. Introduction
Existing blockchain systems suffer from two main problems:
- Excessive energy consumption without external value (traditional Proof-of-Work)
- Difficulty verifying useful computations economically and at scale (Proof-of-Useful-Work)
Foculus addresses these issues by merging consensus and useful computation. Every GPU cycle contributes directly to updating a network-wide knowledge state, known as the focus vector π, achieving consensus efficiently and meaningfully.
2. Collective Focus Theorem
Foculus is built upon the collective focus theorem, which guarantees convergence of attention-based computations:
- Define a random-walk transition matrix from weighted, signed edges in a strongly connected graph.
- Repeated sparse-matrix multiplications converge towards a stable stationary distribution π.
- Finality occurs when a node’s stationary distribution value (πᵢ) surpasses a dynamically set threshold (τ).
- Ensures safe and irreversible consensus under honest-majority conditions.
3. Foculus Consensus Protocol
3.1 Network Structure
- Particles represent transactions or data points, identified by unique content-addressed hashes.
- Cyberlinks represent weighted endorsements between particles.
- Validators maintain and iterate on the global graph, using dynamically adjusted, reinforcement-learning-driven parameters.
3.2 Focus Calculation
- Every ~100 ms, GPU-accelerated calculations iteratively update π.
- Transactions finalize rapidly once π exceeds the threshold τ.
- Conflicting transactions with insufficient π are discarded to maintain consistency.
3.3 Advantages
- Rapid finality within seconds.
- Scalable throughput (millions of transactions per second).
- Minimal communication overhead via incremental gossip updates.
4. Economic Model
4.1 Reward Architecture
To ensure predictable minting behavior and minimize complexity in reward prediction, the system uses a hybrid incentive split:
- Minted tokens are exclusively allocated to Flow Focus Update proofs (the backbone of consensus).
- All other proof particles (e.g., checkpoint anchoring, compression, availability, routing) are rewarded via a shared allocation from transaction fees.
- 50% of transaction fees are burned to preserve long-term scarcity; the other 50% funds all auxiliary proofs.
This ensures:
- A unified economic weight focused on maintaining consensus and π evolution.
- Pluggable proof classes that scale with usage, without diluting mint-driven monetary policy.
- Clear upper bounds on reward surfaces, reducing attack vectors via arbitrary proof flooding.
| Proof Type | Description | Rewarded For |
|---|---|---|
| Focus Update | Adds meaningful links that shift the stationary distribution | Contributing to state finality and consensus |
| Checkpoint Anchoring | Cryptographically anchors prior π states using lattice or VDF | Providing historical finality and rollback resistance |
| Data Availability Attestation | Proves reliable access or storage of graph data | Ensuring availability of referenced knowledge and transactions |
| Storage Commitment | Commits to holding rare or foundational graph regions | Preserving long-term semantic memory |
| Distillation Proof | Abstracts or summarizes meaningful subgraphs | Improving attention economy and structural efficiency |
| Flow Focus Compute Proof | Performs inference or logic flow to guide future graph evolution | Advancing reasoning and emergent decision intelligence |
| Signature Verification Proof | Verifies chains of trust and message authorship cryptographically | Enabling secure graph integrity and identity-linked computation |
| Censorship Detection | Proves omission of valid but unreferenced particles | Restoring fairness and healing attention blind spots |
| Fork Finality Monitor | Detects double-finality or network split attempts | Enhancing security via contradiction awareness |
| Message Delivery Proof | Proves fast, efficient message routing across the network | Enabling reliable communication and lowering propagation latency |
| Location Presence Proof | Cryptographically anchors a node to physical or logical topology | Supporting trust geometry and routing optimization |
| Model Distillation Proof | Derives symbolic or neural abstraction from π-subgraph | Creating higher-level cognitive models shared by agents |
| Temporal Anchoring Proof | Establishes verifiable causal order using delay or trust-weighted chains | Strengthening historical consistency and global synchrony |
| Private Knowledge Injection | Embeds encrypted or zk-proven latent information into the graph | Enabling privacy-preserving cognition and value contribution |
| Interoperability Proof | Verifies authenticated state transitions or messages across chains | Enabling secure interchain communication and shared cognition |
| Proof Type | Description | Rewarded For |
|---|---|---|
| Focus Update | Adds meaningful links that shift the stationary distribution | Contributing to state finality and consensus |
| Checkpoint Anchoring | Cryptographically anchors prior π states using lattice or VDF | Providing historical finality and rollback resistance |
| Data Availability Attestation | Proves reliable access or storage of graph data | Ensuring availability of referenced knowledge and transactions |
| Storage Commitment | Commits to holding rare or foundational graph regions | Preserving long-term semantic memory |
| Distillation Proof | Abstracts or summarizes meaningful subgraphs | Improving attention economy and structural efficiency |
| Flow Focus Compute Proof | Performs inference or logic flow to guide future graph evolution | Advancing reasoning and emergent decision intelligence |
| Signature Verification Proof | Verifies chains of trust and message authorship cryptographically | Enabling secure graph integrity and identity-linked computation |
| Censorship Detection | Proves omission of valid but unreferenced particles | Restoring fairness and healing attention blind spots |
| Fork Finality Monitor | Detects double-finality or network split attempts | Enhancing security via contradiction awareness |
| Message Delivery Proof | Proves fast, efficient message routing across the network | Enabling reliable communication and lowering propagation latency |
| Location Presence Proof | Cryptographically anchors a node to physical or logical topology | Supporting trust geometry and routing optimization |
| Model Distillation Proof | Derives symbolic or neural abstraction from π-subgraph | Creating higher-level cognitive models shared by agents |
| Temporal Anchoring Proof | Establishes verifiable causal order using delay or trust-weighted chains | Strengthening historical consistency and global synchrony |
| Private Knowledge Injection | Embeds encrypted or zk-proven latent information into the graph | Enabling privacy-preserving cognition and value contribution |
4.3 Emergence of Stake-Weighted Attention
Stake, reputation, and endorsement flow collectively determine the weight of each proof particle in the graph. Through power-iteration, the π vector converges on a shared focus, and minting naturally favors proofs that contribute most to it. This creates a cryptoeconomic equilibrium, where:
- Network consensus evolves continuously
- The token economy tracks and amplifies shared belief
4.4 Damping and Focus Evolution
To prevent long-term concentration and encourage network adaptability, a decay function is applied over time:
πᵢ ← πᵢ × γ^t , where γ ∈ (0, 1)
This damping ensures that:
- Older or less-endorsed particles fade from influence
- The focus vector π evolves with current behavior, endorsements, and work
- The system “forgets” obsolete or less-relevant history, enabling continual learning and adaptability
This models attention as a dynamic, evolving field—just as natural intelligence retains relevant memories and forgets noise—forming the cognitive substrate of the chain.
4.1 The π-Minting Theorem
Minting is governed not by cost of work or subjective utility, but by the measurable shift in the collective attention vector π. Each valid proof—be it a focus update, checkpoint, or other verifiable structure—is treated as a “proof particle” in the network graph. Rewards are distributed proportionally to the influence a proof particle exerts over the current π distribution:
reward(p) ∝ Δπ(p)
Where:
pis a proof particle submitted during a given intervalΔπ(p)is the change in the stationary distribution π resulting from the addition ofpand its links
This ensures that:
- Proof types are treated uniformly, regardless of underlying mechanics
- Attention-weighted relevance drives economic value
- No privileged mechanism is required for minting eligibility
4.2 Emergence of Stake-Weighted Attention
Stake, reputation, and endorsement flow collectively determine the weight of each proof particle in the graph. Through power-iteration, the π vector converges on a shared focus, and minting naturally favors proofs that contribute most to it. This creates a cryptoeconomic equilibrium, where:
- Network consensus evolves continuously
- The token economy tracks and amplifies shared belief
4.3 Damping and Focus Evolution
To prevent long-term concentration and encourage network adaptability, a decay function is applied over time:
πᵢ ← πᵢ × γ^t , where γ ∈ (0, 1)
This damping ensures that:
- Older or less-endorsed particles fade from influence
- The focus vector π evolves with current behavior, endorsements, and work
- The system “forgets” obsolete or less-relevant history, enabling continual learning and adaptability
This models attention as a dynamic, evolving field—just as natural intelligence retains relevant memories and forgets noise—forming the cognitive substrate of the chain.
4.1 Reward Structure
- Minted tokens correlate directly with the incremental increase in collective focus (Δπ).
- Contributors rewarded proportionally based on their verified Δπ contributions.
4.2 Fee and Stake Mechanisms
- Transaction fees split evenly between token burns and contributor rewards.
- Validators stake tokens to guarantee honesty and lose them if dishonest behaviors are proven.
5. Quantum Resilience
5.1 Built-In Quantum Resistance
- Network safety parameters dynamically halt operations if quantum-induced variances exceed safe thresholds.
5.2 Checkpoint Anchors
- Regular lattice-based Proof-of-Work (PoW) checkpoints secure historical data, significantly increasing quantum resilience.
- Each checkpoint involves solving a lattice-based puzzle, specifically a Shortest Vector Problem (SVP), known to resist quantum attacks effectively.
- The checkpoints establish cryptographic anchors, effectively finalizing previous network states and preventing quantum-enabled historical rewrites.
- Frequency of checkpoints dynamically determined based on network load and security conditions, allowing responsive protection without slowing bridging.
- Computational resources allocated through a reinforcement learning system, limiting resource usage and preventing unnecessary escalation of hashing power.
- Payments for checkpoint computation derived from dedicated fractions of transaction fees, ensuring predictable and sustainable funding.
6. Adaptive Parameterization via Reinforcement Learning
- System parameters (thresholds, safety margins, resource allocation, checkpoint frequency) continuously optimized through a built-in reinforcement learning algorithm.
- Real-time adjustments maintain optimal security, performance, and resource efficiency without requiring manual governance.
7. Data Availability
- Network data is stored via erasure-coded methods, efficiently sampled by nodes, ensuring reliable access and reducing centralization risks.
- Specialized link-bundlers manage data dissemination and earn rewards by reducing load on validators.
8. Security Guarantees
8.1 Network Safety
- Safety guaranteed by the collective focus theorem under honest-majority conditions.
- Impossible for two conflicting transactions to finalize simultaneously.
8.2 Network Liveness
- Guaranteed by ergodic convergence properties of graph computations.
- Every valid transaction achieves finality within predictable bounds.
8.3 Economic Security
- Security backed by staked economic capital rather than raw computational power, making attacks economically unfeasible.
9. Quantum-Safe Hybrid Integration
- Periodic checkpoints embed quantum-resistant lattice puzzles, providing an additional layer of long-term security.
- Finality anchored by regular quantum-resilient checkpoints, ensuring historical chain integrity.
10. Implementation Roadmap
- Phase 1: Launch test network; prototype consensus and economic layers.
- Phase 2: Integrate lattice checkpoints; optimize resource allocation.
- Phase 3: Launch public-good computation incentive mechanism.
- Phase 4: Enhance data availability and bundling structures.
- Phase 5: Mainnet beta release, continuous adaptive optimizations.
Conclusion
The Foculus Foundation establishes a secure, scalable, and meaningful blockchain by aligning economic incentives, computation, and consensus. Its unique blend of quantum resilience, adaptive parameterization, and useful computation sets a robust foundation for a decentralized, earth-scale super-intelligence network.