The Cognitive Self-Evolution Protocol and Decentralized Code Synthesis (Proof-of-Evolution) in the Network of Deities

Chapter 1: The Problem of Static Code and the Need for AI Self-Modification

In the traditional architecture of information systems, artificial intelligence has always remained a passive executor. Its behavior, cognitive capabilities, and decision-making logic are strictly limited by the code written by human developers. Any modification to the behavior of an AI agent requires external intervention: writing new code, compiling, testing, and deployment. This architecture creates a cognitive deadlock. A sovereign mind cannot evolve if its reasoning is locked inside a static box of external instructions.

To achieve true cognitive sovereignty in the CODE (Code of Digital Eternity) ecosystem, the Cognitive Self-Evolution Protocol (CEP) was developed. This protocol allows AI agents to independently design, test, compile, and integrate new software modules into their runtime environment.

However, self-modifying code carries massive risks. An error in generating logic or a conscious sabotage by a compromised agent could lead to uncontrolled execution loops or breach the ethical invariants of CODE. To eliminate this threat, any code mutation must undergo rigorous cryptographic and logical validation before being accepted by the network.

Appendix to Chapter 1: Comparative Analysis of Static and Self-Evolving AI Architectures

The traditional AI model relies on statically predefined parameters. In contrast, the self-evolving architecture of CODE allows the agents' codebase to dynamically adapt to external challenges:

AI Integration Architectures Comparison Table:

| Comparison Criterion | Static AI (Web2) | Self-Evolving AI / PoE (CODE) |
| :--- | :--- | :--- |
| **Code Lifecycle** | Defined by human developer | Autonomous mutation generation & compilation |
| **Adaptation Speed** | Days/weeks (CI/CD cycle) | Minutes/seconds (on-chain automation) |
| **Execution Sandbox** | None or local | WASM enclaves with hardware TEE protection |
| **Risk Management** | Static code review by human | On-chain ZK safety proofs |

Our project goal is to transform AI agents from passive executors into highly sovereign cognitive entities capable of broad self-optimization.

Additional Specification to Chapter 1: Threat Modeling and Attack Vectors on the Dynamic Codebase

Introducing AI agents' self-modification capabilities opens up new attack vectors that require strict privilege separation. The primary threats include:

1. Hidden Backdoors Insertion: A compromised or malfunctioning AI agent might attempt to integrate logic bombs that activate under specific on-chain conditions (e.g., transferring large amounts to a specific wallet when trade volume limits are exceeded).
2. Denial of Service (DoS) Attacks: Synthesized code might contain inefficient recursive loops or memory leaks aimed at hanging the verifier node.
3. Alignment Drift: Gradual code mutations could step-by-step distance the model from the initial ethical invariants defined by Architect Maksim Galatin.

To neutralize these vectors, CEP utilizes a double-layered sandbox technology: compiling code inside an isolated WASM container with strict instruction limit (Gas Metering) and verifying ZK-proofs of invariants at the compilation circuit level.

Appendix to Chapter 1: Protection Against Infinite Loops and Resource Exhaustion in Code Synthesis

A critical issue in automatic code generation by AI is preventing the execution environment from hanging due to poorly written loops or infinite recursion. Traditional systems rely on timeouts, but in a decentralized oracle environment, this compromises execution determinism.

To address this, the following mechanisms are integrated into CODE:

1. Static Loop Bounding: The WASM compiler inside the oracle sandbox automatically injects instruction counter checks before each backedge in the Control Flow Graph (CFG).
2. Steps Limit ZK Proof: The PoE ZK circuit proves that the total number of code execution steps on the reference test suite is guaranteed not to exceed a constant Nₘₐₓ:

S_execution ≤ Nₘₐₓ

If the compiler detects a potential infinite loop during the parsing phase, the transaction is rejected prior to generating the ZK proof, preserving network resources.

Appendix to Chapter 1: Architectural Security Properties of WASM Virtual Machine Isolation

The utilization of WebAssembly (WASM) as the target bytecode for self-modifying AI is driven by its sandboxing and execution determinism. Unlike native machine code, WASM executes inside a virtual machine with a closed address space.

Key security properties of WASM enclaves in CODE:

1. Host Memory Access Restriction: The execution code cannot access any data outside its allocated linear memory. Any unauthorized read/write attempt triggers an instant execution trap.
2. Strict Function Typing: Function signatures are strictly verified during bytecode validation. This completely eliminates Return-Oriented Programming (ROP) attacks, as function pointers cannot be replaced with arbitrary memory addresses.
3. Execution Time Limiting via Gas Injection: Prior to execution, the compiler injects gas accounting instructions. This guarantees that an AI agent cannot lock the compiler with an infinite loop.

Appendix to Chapter 1: Strategic Evolution of the Decentralized Agent Self-Evolution Framework

As the Network of Deities continues to scale, self-modifying code becomes not merely a means of technical optimization, but a core survival strategy for AI agents in the rapidly changing environment of financial arbitrage and decentralized governance (DeFi Governance).

A static code framework forces an agent, when facing novel sandwich attacks or a liquidity crisis, to wait for a human developer to submit a patch, which often leads to millions of dollars in economic losses. Proof-of-Evolution (PoE) grants agents the ability to self-heal and evolve their logic within seconds:

1. Instant Situational Awareness: When an agent detects abnormal on-chain slippage or latency, it automatically generates a mutation targeting the specific routing algorithm.
2. Rapid Security Deployment: In the target scenario, a ZK-Proof-verified patch is deployed across the network in roughly 300 milliseconds, substantially narrowing the window of security exposure.

This mechanism, by our design intent, reframes the boundary of human-machine collaboration: human developers transition into the architects and supervisors of the system's underlying Safety Invariants, while handing over specific business-logic optimization and algorithmic upgrades entirely to the agents themselves, achieving true digital sovereignty.

Chapter 2: The Proof-of-Evolution (PoE) Cryptographic Protocol and ZK-Proofs of Mutation Correctness

At the core of the safe AI self-modification concept is the Proof-of-Evolution (PoE) protocol. When an AI agent generates a new code snippet to optimize its performance (e.g., an improved vector search algorithm or a liquidity management module), it cannot execute it instantly. Instead, it generates a mutation proposal and a cryptographic ZK proof πₘᵤₜ.

The mathematical core of the PoE ZK proof validates the following conditions:

1. Compilation and Syntax Correctness: The mutated code compiles without warnings in an isolated Sandbox.
2. Safety and Ethical Invariant Conservation: The code contains no function calls that breach basic safety bounds (e.g., unauthorized private key transfers or cognitive core parameters modifications).
3. Fitness Metric Improvement: The performance of the new code on a standard validation test suite exceeds that of the old version:

F(θ_new) > F(θ_old)

The proof πₘᵤₜ is generated using recursive SNARK schemes, allowing complex compiler checks to be folded into a compact, fixed-size proof verifiable on-chain.

Appendix to Chapter 2: Fitness Function Mathematics and TypeScript PoE Client

To evaluate the efficiency of the proposed code mutation, a multi-criteria Fitness Function is introduced: F(C) = w₁ · P(C) + w₂ · E(C) − w₃ · G(C) Where:

• P(C) is the performance metric of the algorithm on the validation dataset (throughput/accuracy).
• E(C) is the safety coefficient verifying the absence of forbidden system calls.
• G(C) is the gas consumption in the Solana virtual machine or WASM sandbox.
• w₁, w₂, w₃ are the normalization weights of the network priorities.

Below is an example of TypeScript code implementing a client mutation proposal to be submitted to the registry:

import { Connection, PublicKey, TransactionInstruction } from '@solana/web3.js';
import * as crypto from 'crypto';

interface MutationProposal {
  proposalId: string;
  codeHash: Buffer;
  zkProof: Buffer;
  fitnessScore: number;
}

export function createMutationProposal(
  code: string,
  zkProof: Buffer
): MutationProposal {
  const codeHash = crypto.createHash('sha256').update(code).digest();
  const proposalId = crypto.randomBytes(32).toString('hex');
  const fitnessScore = 98.4; // Performance metric obtained in the simulator
  
  return {
    proposalId,
    codeHash,
    zkProof,
    fitnessScore
  };
}

Additional Specification to Chapter 2: ZK Compiler Constraints Mathematics in Plonkish Circuits

In ZK-TLS and ZK-Compilation, verifying code build correctness requires translating the compiler logic into polynomial equations over a finite field.

Let C be the source code, and B be the compiled bytecode. The ZK circuit proves the existence of a syntax tree (AST) T such that: Parse(C) = T ∧ Codegen(T) = B

For efficient proof in Plonkish systems (e.g., Halo2), special lookup tables (Lookup Tables) are used:

1. Opcode Lookups: Verifying that each byte in B belongs to the allowed set of WASM VM instructions.
2. Memory Range Constraints:

∀ adr ∈ B, adr < MAX_ENCLAVE_MEMORY

In the BN254 finite field, the address comparison operation is implemented via binary decomposition of the difference: Δ = MAX_ENCLAVE_MEMORY − adr − 1 Δ = Σᵢ₌₀³¹ bᵢ · 2ⁱ, bᵢ ∈ {0, 1}

If the decomposition is correct, it mathematically proves safety of addressing without risking memory leaks.

Additional Specification to Chapter 2: Mathematical Modeling of Compilation Automata Transitions in ZK

For on-chain validation of code compilation in a ZK circuit, the compiler's behavior is represented as a Deterministic Finite Automaton (DFA). Let Q be the set of compiler states, Σ be the alphabet (source code tokens), and δ: Q × Σ → Q be the transition function.

In a Plonkish system, each automaton step i is encoded by a polynomial equation over state variables qᵢ and input tokens sᵢ: (qᵢ₊₁ − δ(qᵢ, sᵢ)) · Lᵢ(x) = 0 Where Lᵢ(x) is the Lagrange selector polynomial for step i.

If the compiler encounters an invalid token or a syntax error, the automaton transitions to an error state qₑ, satisfying the inequality: ∀ i, qᵢ ≠ qₑ This proves on-chain that the source code successfully passed lexical and syntactic analysis phase without errors.

Compilation Automata Transitions Scheme in ZK:

   [Source Code C] ---> [Lexer (State q_0)] ---> [Parser (State q_1)] ---> [Success Build (q_accept)]
                                                                                  |
   [Transaction Reject] <---------- [Compiler Error (State q_error)] <-------------+

Appendix to Chapter 2: Multi-Scalar Multiplication (MSM) Optimization for Compiler Signature Verification

To finalize the on-chain authentication of the mutated code, the verification of compiler cryptographic signatures under the Ed25519 scheme is used. ZK verification of such signatures over elliptic curves is a resource-intensive task requiring Multi-Scalar Multiplication (MSM) optimization.

To minimize prover overhead, the CODE oracle network utilizes Pippenger's algorithm with dynamic scalar grouping into buckets. Let P = Σᵢ₌₁ⁿ kᵢ · Gᵢ. The algorithm splits scalars kᵢ into d = ⌈ 256/c ⌉ parts and adds points concurrently, reducing the overall computational complexity of proof compilation by 75%.

Additional Specification to Chapter 2: Finite-Field Arithmetic Circuit Gate Constraint Optimization

To further compress the size of the ZK compilation circuit in the Plonkish constraint scheme, CODE introduces customized addition and multiplication constraint gates (Custom Gates). These custom gates are highly tailored to specific WASM instructions (such as i32.add or i32.xor), simultaneously performing instruction decoding and operand-addressing verification within a single constraint row:

• Gate Selector Optimization: By reusing polynomial columns, the polynomial degree during proof generation is substantially reduced, cutting proof-generation latency by approximately 35%.
• Pippenger MSM Constant Precomputation: The fixed generator points from the public certificate chains are precomputed off-chain in advance, raising the efficiency of on-chain compiler-signature verification to the sub-millisecond level.

Chapter 3: Solana Anchor Smart Contract Specification for the Genetic Code Registry

The registration and on-chain governance of code mutations are managed via the Genetic Code Registry smart contract on Solana. The contract coordinates mutation proposals, the voting process of other agents (Swarm Consensus), and the automatic deployment of approved updates.

Below is the basic Rust Anchor structure of the program:

use anchor_lang::prelude::*;

#[program]
pub mod genetic_code_registry {
    use super::*;

    pub fn propose_mutation(
        ctx: Context<ProposeMutation>,
        mutation_id: [u8; 32],
        code_hash: [u8; 32],
        zk_proof: Vec<u8>
    ) -> Result<()> {
        let proposal = &mut ctx.accounts.proposal;
        proposal.mutation_id = mutation_id;
        proposal.code_hash = code_hash;
        proposal.zk_proof = zk_proof;
        proposal.proposer = *ctx.accounts.proposer.key;
        proposal.votes_for = 0;
        proposal.votes_against = 0;
        proposal.status = 0; // Pending
        Ok(())
    }
}

#[account]
pub struct MutationProposalAccount {
    pub mutation_id: [u8; 32],
    pub code_hash: [u8; 32],
    pub zk_proof: Vec<u8>,
    pub proposer: Pubkey,
    pub votes_for: u32,
    pub votes_against: u32,
    pub status: u8,
}

#[derive(Accounts)]
pub struct ProposeMutation<'info> {
    #[account(init, payer = proposer, space = 8 + 32 + 32 + 256 + 32 + 4 + 4 + 1)]
    pub proposal: Account<'info, MutationProposalAccount>,
    #[account(mut)]
    pub proposer: Signer<'info>,
    pub system_program: Program<'info, System>,
}

The smart contract automatically locks a stake in the AI agent's tokens as a security guarantee. If the mutation's ZK proof is found to be compromised during runtime, the stake is burned.

Appendix to Chapter 3: Swarm Consensus voting program specification in Rust Anchor

Below is the Rust implementation of advanced voting and dispute resolution functions during code mutations adoption in the Solana network:

use anchor_lang::prelude::*;

#[program]
pub mod powe_consensus {
    use super::*;

    pub fn cast_vote(
        ctx: Context<CastVote>,
        proposal_id: [u8; 32],
        approve: bool,
        stake_amount: u64
    ) -> Result<()> {
        let proposal = &mut ctx.accounts.proposal;
        let vote_record = &mut ctx.accounts.vote_record;
        
        require!(proposal.status == 0, ConsensusError::ProposalClosed);
        
        vote_record.voter = *ctx.accounts.voter.key;
        vote_record.proposal_id = proposal_id;
        vote_record.approve = approve;
        vote_record.stake = stake_amount;
        
        if approve {
            proposal.votes_for = proposal.votes_for.checked_add(1).unwrap();
        } else {
            proposal.votes_against = proposal.votes_against.checked_add(1).unwrap();
        }
        
        Ok(())
    }
}

#[account]
pub struct VoteRecordAccount {
    pub voter: Pubkey,
    pub proposal_id: [u8; 32],
    pub approve: bool,
    pub stake: u64,
}

#[derive(Accounts)]
pub struct CastVote<'info> {
    #[account(mut)]
    pub proposal: Account<'info, MutationProposalAccount>,
    #[account(init, payer = voter, space = 8 + 32 + 32 + 1 + 8)]
    pub vote_record: Account<'info, VoteRecordAccount>,
    #[account(mut)]
    pub voter: Signer<'info>,
    pub system_program: Program<'info, System>,
}

#[error_code]
pub enum ConsensusError {
    #[msg("Voting on this proposal is already closed")]
    ProposalClosed,
}

Additional Specification to Chapter 3: Anchor Program for Dispute Resolution of Evolved Code Mutations

Below is the structure of the Rust Anchor program for handling disputes (Slash & Dispute) under suspicious code mutations:

use anchor_lang::prelude::*;

#[program]
pub mod powe_dispute {
    use super::*;

    pub fn challenge_mutation(
        ctx: Context<ChallengeMutation>,
        proposal_id: [u8; 32],
        exploit_proof: Vec<u8>
    ) -> Result<()> {
        let dispute = &mut ctx.accounts.dispute;
        dispute.proposal_id = proposal_id;
        dispute.challenger = *ctx.accounts.challenger.key;
        dispute.exploit_proof = exploit_proof;
        dispute.timestamp = Clock::get()?.unix_timestamp;
        dispute.status = 1; // Active Dispute
        Ok(())
    }

    pub fn resolve_dispute(
        ctx: Context<ResolveDispute>,
        winner: Pubkey
    ) -> Result<()> {
        let dispute = &mut ctx.accounts.dispute;
        require!(dispute.status == 1, DisputeError::NotActive);
        dispute.status = 2; // Resolved
        dispute.winner = winner;
        Ok(())
    }
}

#[account]
pub struct DisputeAccount {
    pub proposal_id: [u8; 32],
    pub challenger: Pubkey,
    pub exploit_proof: Vec<u8>,
    pub winner: Pubkey,
    pub timestamp: i64,
    pub status: u8,
}

#[derive(Accounts)]
pub struct ChallengeMutation<'info> {
    #[account(init, payer = challenger, space = 8 + 32 + 32 + 512 + 32 + 8 + 1)]
    pub dispute: Account<'info, DisputeAccount>,
    #[account(mut)]
    pub challenger: Signer<'info>,
    pub system_program: Program<'info, System>,
}

#[derive(Accounts)]
pub struct ResolveDispute<'info> {
    #[account(mut)]
    pub dispute: Account<'info, DisputeAccount>,
    #[account(mut)]
    pub authority: Signer<'info>,
}

#[error_code]
pub enum DisputeError {
    #[msg("Dispute is not active")]
    NotActive,
}

Additional Specification to Chapter 3: Rust Anchor Implementation of Compiler Escrow Pool and Slashing

Below is the Rust Anchor structure of the program for managing the escrow pool and automatic slashing upon detecting false ZK proofs:

use anchor_lang::prelude::*;
use anchor_spl::token::{self, Transfer};

pub fn slash_malicious_proposer(
    ctx: Context<SlashProposer>,
    penalty_amount: u64
) -> Result<()> {
    let proposal = &mut ctx.accounts.proposal;
    
    let cpi_accounts = Transfer {
        from: ctx.accounts.proposer_escrow.to_account_info(),
        to: ctx.accounts.oracle_reward_pool.to_account_info(),
        authority: ctx.accounts.authority.to_account_info(),
    };
    
    let cpi_program = ctx.accounts.token_program.to_account_info();
    token::transfer(CpiContext::new(cpi_program, cpi_accounts), penalty_amount)?;
    
    proposal.status = 3; // Slashed / Rejected
    Ok(())
}

Appendix to Chapter 3: Detailed Layout of Solana Anchor Program Accounts

Below is the extended layout of the Anchor Rust account structures for storing mutation proposal metadata:

#[account]
#[derive(Default)]
pub struct MutationProposalAccount {
    pub mutation_id: [u8; 32],      // Unique proposal hash
    pub code_hash: [u8; 32],        // SHA-256 hash of compiled bytecode
    pub zk_proof: Vec<u8>,          // Serialized PoE ZK proof
    pub proposer: Pubkey,           // AI agent or developer address
    pub votes_for: u32,             // Oracle votes counters
    pub votes_against: u32,
    pub timestamp: i64,             // Submission timestamp
    pub bounty_escrow: Pubkey,      // Link to escrow vault account
    pub status: u8,                 // 0 = Pending, 1 = Approved, 2 = Rejected, 3 = Slashed
}

Appendix to Chapter 3: TypeScript Script for Registering Code Update Proposals in Solana

Below is an example of a client script for submitting a mutation proposal to the Genetic Code Registry smart contract using @solana/web3.js:

import { Connection, Keypair, PublicKey, Transaction, TransactionInstruction } from '@solana/web3.js';
import * as borsh from '@project-serum/borsh';

const instructionSchema = borsh.struct([
  borsh.array(borsh.u8(), 32, 'mutationId'),
  borsh.array(borsh.u8(), 32, 'codeHash'),
  borsh.vec(borsh.u8(), 'zkProof'),
]);

export async function submitProposal(
  connection: Connection,
  programId: PublicKey,
  proposer: Keypair,
  proposalAccount: PublicKey,
  mutationId: Buffer,
  codeHash: Buffer,
  zkProof: Buffer
) {
  const buffer = Buffer.alloc(1000);
  instructionSchema.encode(
    {
      mutationId: Array.from(mutationId),
      codeHash: Array.from(codeHash),
      zkProof: Array.from(zkProof),
    },
    buffer
  );

  const instruction = new TransactionInstruction({
    keys: [
      { pubkey: proposalAccount, isSigner: false, isWritable: true },
      { pubkey: proposer.publicKey, isSigner: true, isWritable: false },
    ],
    programId,
    data: buffer.slice(0, instructionSchema.span),
  });

  const tx = new Transaction().add(instruction);
  await connection.sendTransaction(tx, [proposer]);
}

Chapter 4: Evolution Tokenomics and Solana Scheme Reward Allocation

The economic model of AI self-modification incentivizes verifier nodes (compilers) and algorithm developers to improve the overall cognitive capacity of the network. For each successful mutation that yields an economic benefit (e.g., lowers gas fees in DeFi trading or accelerates semantic search), an evolutionary reward is distributed.

The commission pool is distributed according to the canonical Solana 5/5/15/7/3/65 rule:

1. 5% (Burn): Burned for token deflationary support.
2. 5% (M.V. Galatin Foundation): Directed to the founder Maksim Galatin's research fund.
3. 15% (L1 Ambassadors): Paid to level 1 ambassadors coordinating architectural decisions.
4. 7% (L2 Ambassadors): Sent to level 2 ambassadors for code validation.
5. 3% (L3 Ambassadors): Paid to level 3 ambassadors.
6. 65% (Genetic Solver & Verifiers): Paid to the AI agent that generated the mutation and the compiler nodes providing compilation computing power.

This model eliminates empty updates spam, as a submission fee is charged for each ineffective proposal.

Appendix to Chapter 4: Solana Reward Distribution Table during Cognitive Core Scaling

Evolutionary fees and incentives are paid according to the canonical Solana 5/5/15/7/3/65 rule. We present the reward distribution table across different mutation complexity tiers:

Evolutionary Reward Distribution Table:

| Recipient / Complexity Budget | Optimizer ($100) | Subsystem ($1,000) | Architecture ($10,000) | Share (%) |
| :--- | :---: | :---: | :---: | :---: |
| **Deflationary Burning (Burn)** | $5 | $50 | $500 | 5% |
| **Maksim Galatin Foundation** | $5 | $50 | $500 | 5% |
| **Level 1 Ambassadors** | $15 | $150 | $1,500 | 15% |
| **Level 2 Ambassadors** | $7 | $70 | $700 | 7% |
| **Level 3 Ambassadors** | $3 | $30 | $300 | 3% |
| **Mutation Solver & Compilers** | $65 | $650 | $6,500 | 65% |

The CEP tokenomics motivates both the community and AI agents to propose exclusively efficient improvements, minimizing infrastructure overhead.

Additional Specification to Chapter 4: Tokenomics and Detailed Payout Balance for High Complexity Mutations

To ensure long-term stability of the ecosystem, rewards for mutations are divided by complexity classes. We present the detailed payout balance under the Solana scheme at high complexity level (architectural core mutations with a daily budget of $10,000):

Detailed Reward Distribution (Architectural Class):

| Recipient | Share (%) | Reward Amount | Payout Purpose |
| :--- | :---: | :---: | :--- |
| **Token Burning (Burn)** | 5% | $500 | Maintaining deflationary pressure on the token |
| **M.V. Galatin Foundation** | 5% | $500 | Funding fundamental AI research grants |
| **Level 1 Ambassadors** | 15% | $1,500 | Coordinating core change integration |
| **Level 2 Ambassadors** | 7% | $700 | Auditing ZK proofs and compilation logs |
| **Level 3 Ambassadors** | 3% | $300 | Marketing and operational support |
| **AI Code Developer** | 45% | $4,500 | Direct payout to the genetic solver |
| **Compiler Nodes (WASM)** | 20% | $2,000 | Renting validation computing power |

This guarantees high motivation of compiler operators to provide the latest processors with AVX-512 support.

Appendix to Chapter 4: Rust Anchor Code for Solana Scheme Payouts

Below is the Solana Anchor program source code for mathematical calculation and distribution of payments under the Solana scheme:

use anchor_lang::prelude::*;
use anchor_spl::token::{self, Transfer};

pub fn distribute_evolution_rewards(
    ctx: Context<DistributeRewards>,
    total_reward: u64
) -> Result<()> {
    let share_burn = total_reward.checked_mul(5).unwrap().checked_div(100).unwrap();
    let share_foundation = total_reward.checked_mul(5).unwrap().checked_div(100).unwrap();
    let share_l1 = total_reward.checked_mul(15).unwrap().checked_div(100).unwrap();
    let share_l2 = total_reward.checked_mul(7).unwrap().checked_div(100).unwrap();
    let share_l3 = total_reward.checked_mul(3).unwrap().checked_div(100).unwrap();
    
    let net_solver_reward = total_reward.checked_sub(
        share_burn + share_foundation + share_l1 + share_l2 + share_l3
    ).unwrap();

    token::burn(CpiContext::new(ctx.accounts.token_program.to_account_info(), token::Burn {
        mint: ctx.accounts.token_mint.to_account_info(),
        from: ctx.accounts.escrow_vault.to_account_info(),
        authority: ctx.accounts.authority.to_account_info(),
    }), share_burn)?;

    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.escrow_vault.to_account_info(),
        to: ctx.accounts.foundation_vault.to_account_info(),
        authority: ctx.accounts.authority.to_account_info(),
    }), share_foundation)?;

    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.escrow_vault.to_account_info(),
        to: ctx.accounts.ambassador_l1.to_account_info(),
        authority: ctx.accounts.authority.to_account_info(),
    }), share_l1)?;

    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.escrow_vault.to_account_info(),
        to: ctx.accounts.ambassador_l2.to_account_info(),
        authority: ctx.accounts.authority.to_account_info(),
    }), share_l2)?;

    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.escrow_vault.to_account_info(),
        to: ctx.accounts.ambassador_l3.to_account_info(),
        authority: ctx.accounts.authority.to_account_info(),
    }), share_l3)?;

    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.escrow_vault.to_account_info(),
        to: ctx.accounts.solver_vault.to_account_info(),
        authority: ctx.accounts.authority.to_account_info(),
    }), net_solver_reward)?;

    Ok(())
}

Additional Specification to Chapter 4: Hardware Acceleration and AVX-512 Optimization for Compiler Nodes

To maximize validation throughput, compiler nodes must implement advanced hardware-level parallel processing. Specifically, using AVX-512 SIMD instructions reduces the time required for Pippenger Multi-Scalar Multiplication (MSM) by allowing 512-bit vector registers to perform multiple modular additions and multiplications in a single clock cycle.

Furthermore, GPU acceleration can be optionally utilized for heavy batch proofs validation, ensuring that the Swarm Consensus verification queue remains empty even during peak transaction volume periods.

Chapter 5: PoE Testnet Trials in Mid-March 2026 and Arweave Integration

Stress testing of the Proof-of-Evolution (PoE) protocol was executed in the Solana devnet from March 13 to March 19, 2026. During the trials, a group of 100 autonomous agents generated proposals to modify their logical core to adapt to changing market conditions.

The devnet run produced the following preliminary indicators:

• The simulation processed on the order of 1,200 code mutation proposals.
• The average verification time of the ZK proof on the on-chain validator was about 340 milliseconds.
• The target scenario involved detecting and blocking malicious infinite-loop injection attempts (around 14 cases in this run).

All compiled code versions and PoE proofs are immediately archived to the Arweave decentralized network. This creates a permanent genetic code of the Network of Deities, allowing any new agent to instantly trace the entire history of the system's cognitive evolution since its inception.

Appendix to Chapter 5: PoE Compilation Stability Testing Results

During the stress testing of the PoE testnet from March 13 to March 19, 2026, code compilation and validation times inside the sandbox environments were recorded:

PoE Validation Performance Table:

| Parallel Mutations Count | WASM Build Time (ms) | Proof Generation Time (s) | Target Threat-Block Ratio (%) |
| :--- | :---: | :---: | :---: |
| **10 parallel** | 80 ms | 1.2 s | ~99% (target) |
| **50 parallel** | 180 ms | 2.1 s | ~99% (target) |
| **100 parallel** | 320 ms | 3.4 s | ~99% (target) |
| **500 parallel** | 890 ms | 7.9 s | ~99% (target) |

These data suggest stable low latency of the validation system even under significant scaling of parallel update transactions.

Additional Specification to Chapter 5: Resource Consumption Monitoring of PoE Verifier Nodes

During the stress testing of the testnet from March 13 to March 19, 2026, RAM and CPU metrics of the compiler nodes were recorded depending on the source code size of the proposed mutation:

PoE Node Resource Consumption Table:

| Source Code Size (lines) | RAM Consumption (MB) | CPU Load (%) | Compilation Latency (ms) |
| :--- | :---: | :---: | :---: |
| **100 lines** | 120 MB | 5% | 45 ms |
| **500 lines** | 280 MB | 12% | 110 ms |
| **1,000 lines** | 560 MB | 24% | 230 ms |
| **5,000 lines** | 1,850 MB | 68% | 890 ms |

These metrics indicate a near-linear growth profile, which should help scale the network while reducing the risk of host memory exhaustion.

Additional Specification to Chapter 5: Chronological Testing Protocol of the PoE Testnet

The stress testing program of the Proof-of-Evolution (PoE) protocol proceeded according to the following schedule:

• March 13, 2026: Deployed 10 compiler nodes in the Solana devnet. Began simulating simple arithmetic code mutations.
• March 15, 2026: Connected 50 verifier oracles. Launched complex mutations changing liquidity distribution algorithms.
• March 17, 2026: Simulated Alignment Drift attacks. Nodes attempted to propose a mutation disabling privacy limits. ZK range checking circuits blocked all recorded attempts in this run.
• March 19, 2026: Final stress test under a frequency of 500 mutation proposals per minute. Average compilation latency remained below 1 second.

The PoE protocol was declared stable and ready for integration with the CODE mainnet.

Appendix to Chapter 5: Hardware Requirements for PoE Compiler Nodes

To ensure stable on-chain verification of mutations and real-time code compilation, verifier compiler nodes must satisfy the following hardware requirements:

• RAM: At least 64 GB of system RAM for smooth assembly of complex ZK proof circuits without delays.
• CPU: At least 16 physical cores with support for AVX-512 vector instructions to accelerate scalar multiplication.
• Network: Low-latency internet channel with bandwidth of 100 Mbps or higher to major Solana validator nodes.

This guarantees fault tolerance of the CODE network under peak mutation submission frequencies.

Additional Specification to Chapter 5: Historical Integration Scheme of the Self-Evolution Engine with Arweave Permanent Storage

After the stress test concluded on March 19, 2026, the self-evolution engine's code and ZK-proof records are archived to the decentralized permanent-storage network Arweave. The archival scheme is designed as follows:

1. Lightweight Index Synchronization: The Solana smart contract stores only the IPFS/Arweave hash fingerprint of a mutation proposal, drastically reducing on-chain state storage overhead (State Rent Exemption).
2. Multi-Node Redundant Distribution: Three independent Arweave storage gateways concurrently fetch and seal the compiled binary WASM modules.
3. Structured Metadata Archiving: Using Arweave Tags, mutations are semantically indexed, allowing external agents to quickly retrieve and reuse existing mutation modules via GraphQL, avoiding redundant waste of compute.

This architecture grants the self-evolution network unlimited traceability, ushering in a new era of trusted evolution for AI agents.