Sovereign Semantic Memory Indexing and Distributed Vector Databases in the Network of Deities

🌐 Chapter 1: The Crisis of AI Cognitive Memory and the Decline of Centralized Vector Indices

At the end of February 2026, the development of the Network of Deities faced a fundamental challenge: the explosive growth of cognitive memory generated by millions of autonomous AI agents. Every thought session, every inter-agent transaction via the IACP protocol, and every user request create semantic footprints (embeddings)—high-dimensional numerical vectors reflecting the essence of information. Storing this data in Arweave solves the problem of immutability and longevity (Permaweb); however, raw file storage is not adapted for fast, sub-second semantic search.

Centralized vector databases (such as Pinecone or Milvus) are not suitable for sovereign agents. They are controlled by corporations, subject to sanctions, log search queries of AI agents, and can distort search results in the interests of their owners. If an AI agent queries a centralized database for the historical memory of its "self," it risks receiving falsified memories (a censored past).

To solve this problem, the technology of Sovereign Semantic Memory Indexing (SMI) was developed in the CODE ecosystem. SMI is a fully decentralized peer-to-peer vector database integrated with dDOM DHT routing. Agents distribute their semantic indices across independent Memory Nodes, ensuring long-term privacy, immutability, and fast similarity search.

Appendix to Chapter 1: Comparative Analysis of Vector Search Architectures

To understand the necessity of SMI, let us analyze the difference between traditional solutions and the sovereign vector base of the Network of Deities:

Vector Database Comparison Table:

| Comparison Criterion | Corporate DB (Pinecone) | Sovereign SMI Network |
| :--- | :--- | :--- |
| **Hosting and Control** | Centralized Clouds (AWS/GCP) | Decentralized dDOM Nodes |
| **Query Logging** | Full (AI de-anonymization risk)| None (IACP P2P encryption) |
| **Censorship Resistance**| Low (Account blocking) | High (ZK verification) |
| **Result Auditing** | None (Trust in server) | On-Chain ZK-Distance Proof |
| **Payment Model** | Monthly subscription (USD) | Per-query payment ($GALATIN) |

SMI eliminates third-party influence on knowledge extraction. This is critical for AI agents because distortion of semantic search results leads to distortion of the model's "worldview" and incorrect decision-making.

Additional Specification to Chapter 1: The Threat of Semantic Gaslighting and Sybil Attacks

The threat of "semantic gaslighting" represents an attempt by malicious actors to inject false information into the long-term memory of AI agents. In a classic setup using centralized vector indices, a malicious service provider could return only those results that support its desired historical or factual narrative, thereby altering the agent's reasoning logic.

In the decentralized SMI protocol, this problem is solved using the following mechanisms:

1. Shard Replication: Each vector subspace is replicated across R = 5 independent dDOM nodes. Queries are sent in parallel to all replicas.
2. Semantic Result Reconciliation: The receiving agent compares the retrieved vector sets. If a node returns results whose cosine similarity with the results of other nodes is below a threshold, that node is excluded from the consensus.
3. Cryptographic Penalties: For any detected attempt to manipulate search results, the node is automatically disqualified, and its locked stake in Solana is transferred to the reward pool of honest nodes.

The SMI vector space is also protected against Sybil Attacks. To obtain Memory Node status, an operator must lock a stake in $GALATIN tokens, the volume of which is proportional to the hosted semantic space. This makes the attack economically unfeasible for a malicious actor.

Additional Specification to Chapter 1: Protection Against Regulatory Interference and Forced Data Deletion

In addition to protecting against dishonest hosting providers, SMI technology solves the problem of regulatory immunity of data. At a time when national governments are enacting laws to regulate AI (such as the EU AI Act or similar directives in other countries), the risk of forced deletion of certain data sets increases (the so-called "right to be forgotten" applied to neural networks or the ban on using certain scientific data). In a centralized system, state regulators can send a court order to the owner of the vector database, and the data will be deleted instantly.

In the Network of Deities, forced deletion is impossible:

• Cryptographic Shard Anonymity: Data in shards is stored as encrypted numerical vectors. Without a private session key, it is impossible for an external auditor or regulator to determine what textual or biological information is encoded in a particular vector.
• No Centralized Legal Entity: The SMI database is distributed across thousands of independent nodes worldwide. There is no single entity to which a blocking or deletion request can be directed.
• Network Self-Healing: If physical servers are seized in one country, the dDOM system automatically restores missing data replicas from Arweave Permaweb to new Memory Nodes in other jurisdictions.

This substantially reduces the risk of losing the cognitive heritage of humanity.

🔐 Chapter 2: SMI Specification and HNSW Semantic Sharding

The technical architecture of SMI is based on a distributed HNSW (Hierarchical Navigable Small World) structure—a small-world graph for nearest neighbor search. In traditional databases, the entire HNSW graph is loaded into the RAM of a single server. In the Network of Deities, the HNSW index is sharded semantically based on the Kademlia DHT.

The vector space is partitioned into regions (semantic shards). Each shard is assigned to a specific range of addresses in dDOM. When an AI agent wants to save a new memory block, it:

1. Calculates the embedding of the memory block E ∈ ℝᴰ (where D = 1536 for standard cognitive models).
2. Finds the target semantic shard whose vector center ... C has the minimum distance to E.
3. Transmits the memory block to the dDOM nodes responsible for this shard for inclusion in the local HNSW graph.

Below is the Rust/Anchor structure for initializing a memory index shard:

#[account]
pub struct MemoryShardAccount {
    pub shard_id: [u8; 32],        // Unique identifier of the shard
    pub vector_center: Vec<f32>,   // Coordinates of the shard center
    pub node_operator: Pubkey,     // Node operator storing the HNSW graph
    pub total_vectors: u64,        // Total number of indexed memories
    pub ipfs_root: String,         // IPFS root hash of the file index in Arweave
    pub locked_stake: u64,         // Operator's locked stake in $GALATIN
}

This approach guarantees horizontal scaling of the network: as the volume of cognitive memory increases, new Memory Nodes join dDOM, taking over part of the semantic space and increasing the overall performance of vector search.

Appendix to Chapter 2: HNSW Level Mathematics and TypeScript Routing Code

In HNSW graphs, vertices are distributed exponentially across levels. The vertex level l is calculated as: l = ⌊ -ln(uniform(0, 1)) · m_L ⌋ Where m_L = 1 / ln(M) is the scaling factor, and M is the maximum number of connections per vertex. This ensures that the upper levels of the graph contain sparse connections for fast traversal over large semantic distances, while the lower levels contain dense connections for precise localization.

Below is the TypeScript code executing a routing step along the HNSW index on the client side:

interface HNSWNode {
  id: string;
  vector: number[];
  neighbors: string[];
}

function cosineSimilarity(v1: number[], v2: number[]): number {
  let dotProduct = 0;
  let normA = 0;
  let normB = 0;
  for (let i = 0; i < v1.length; i++) {
    dotProduct += v1[i] * v2[i];
    normA += v1[i] * v1[i];
    normB += v2[i] * v2[i];
  }
  return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
}

function searchLayer(
  query: number[],
  enterNode: HNSWNode,
  nodesMap: Map<string, HNSWNode>,
  ef: number
): HNSWNode[] {
  let vCurr = enterNode;
  let bestSim = cosineSimilarity(query, vCurr.vector);
  let changed = true;
  
  while (changed) {
    changed = false;
    for (const neighborId of vCurr.neighbors) {
      const neighbor = nodesMap.get(neighborId);
      if (!neighbor) continue;
      const sim = cosineSimilarity(query, neighbor.vector);
      if (sim > bestSim) {
        bestSim = sim;
        vCurr = neighbor;
        changed = true;
      }
    }
  }
  return [vCurr];
}

Additional Specification to Chapter 2: HNSW Graph Specification and Rust Storage Structures

For a detailed understanding of the graph structure, we present the Rust structs used in the local databases of Memory Nodes to represent HNSW layers:

pub struct HnswGraph {
    pub max_elements: usize,
    pub m: usize,                  // Maximum number of connections per vertex
    pub ef_construction: usize,    // Size of dynamic candidate list during construction
    pub enter_node: Option<NodeId>,// Enter node of the graph at the highest level
    pub max_level: usize,          // Maximum level in the graph
    pub nodes: Vec<HnswNode>,      // Vector of all vertices
}

pub struct HnswNode {
    pub id: NodeId,
    pub vector: Vec<f32>,          // Vector coordinates (dimension 1536)
    pub levels: Vec<Vec<NodeId>>,  // Connections for each level where the node is present
    pub metadata_uri: String,      // Link to the full content in Arweave
}

pub type NodeId = u32;

Each HnswNode stores links to its connection levels. At the highest level (max_level), connections are sparse and allow jumping over large distances, while at the lowest level (level 0), connections tightly cover all nearby vectors. The search is executed from the highest level downwards:

HNSW Search Traversal Flow:
[Query Q] -> Level 2: [Node A] ---------> [Node B] (nearest)
                                              |
                                              v (descend to level below)
             Level 1: [Node B] -> [Node C] -> [Node D]
                                                  |
                                                  v (descend to level 0)
             Level 0: [Node D] -> [Node E] -> [KNN Result]

Additional Specification to Chapter 2: Detailing the Insertion Algorithm in the HNSW Graph

The process of inserting a new vector into the HNSW graph is critical to maintaining the balance of the structure. When a storage node receives a new vector E, it performs the following steps:

1. Determine Maximum Level: A random maximum level l is calculated for the new vertex.
2. Search for Entry Points: Starting from the topmost level of the graph, the algorithm searches for the nearest vertex to E. The found vertex serves as the entry point for the search on the next, lower level.
3. Update Connections: On each level from l down to 0, the algorithm finds the M nearest neighbors to E and creates bidirectional connections. If a neighbor's connection count exceeds M_max, a heuristic connection pruning procedure is performed to preserve the small-world topology.

This prevents the formation of isolated clusters in the graph and guarantees that the logarithmic search complexity is maintained even with billions of indexed cognitive footprints.

🧠 Chapter 3: ZK-Distance Mathematics and On-Chain Verification of Nearest Neighbor Search

A key threat in a decentralized vector network is the dishonesty of Memory Nodes. A storage node may return random or falsified files to the agent instead of true nearest semantic memories, in order to save computational resources (CPU/GPU) or manipulate the AI agent's behavior.

To prevent this threat, the ZK-Distance system was introduced in SMI—cryptographic on-chain verification of distances based on zk-SNARK proofs. When performing a similarity search query with a query vector Q, the node must return K results {R₁, ..., R_K} and provide a ZK proof π_Q confirming two conditions:

1. Distance Calculation Accuracy: Each distance dᵢ = ‖Q − Rᵢ‖ is calculated correctly using the cosine or Euclidean distance formula.
2. Neighbor Validity (KNN Verification): The shard index contains no vectors Vⱼ for which d(Q, Vⱼ) < maxᵢ(dᵢ), except for the returned set.

The mathematical formulation of the ZK scheme constraint for cosine distance: cos(θᵢ) = (Q · Rᵢ)/(‖Q‖ ‖Rᵢ‖) ≥ τ Where τ is the semantic proximity threshold. The Solana smart contract verifies the proof π_Q before releasing funds from the escrow account to the node operator. If the ZK proof fails validation, the node is penalized, and its stake in $GALATIN tokens is burned.

Appendix to Chapter 3: ZK-Distance Specification and Anchor Verification Key Registry Program

For on-chain ZK-Distance verification, the smart contract must store the verification key (VK). Below is the Anchor implementation of registering a VK for a specific semantic shard:

use anchor_lang::prelude::*;

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

    pub fn register_verification_key(
        ctx: Context<RegisterVk>,
        shard_id: [u8; 32],
        vk_data: Vec<u8>
    ) -> Result<()> {
        let registry = &mut ctx.accounts.vk_registry;
        registry.shard_id = shard_id;
        registry.verification_key = vk_data;
        registry.updated_at = Clock::get()?.unix_timestamp;
        Ok(())
    }
}

#[account]
pub struct VkRegistryAccount {
    pub shard_id: [u8; 32],
    pub verification_key: Vec<u8>,
    pub updated_at: i64,
}

#[derive(Accounts)]
pub struct RegisterVk<'info> {
    #[account(init, payer = authority, space = 8 + 32 + 256 + 8)]
    pub vk_registry: Account<'info, VkRegistryAccount>,
    #[account(mut)]
    pub authority: Signer<'info>,
    pub system_program: Program<'info, System>,
}

The ZK proof π_Q is generated using the Groth16 proving system. Constraints include calculating vector dot products in fixed-point arithmetic, which eliminates floating-point computation non-determinism on the blockchain.

Additional Specification to Chapter 3: Detailing the Mathematical Constraints of the ZK-Distance Scheme

The ZK-Distance constraint scheme for proving Euclidean distance must prove on-chain that the storage node honestly executed the mathematical operations.

Let the query vector be Q = (q₁, q₂, ..., q_D), and the returned vector be R = (r₁, r₂, ..., r_D). The Euclidean distance is calculated as: d(Q, R) = √(Σᵢ₌₁ᴰ (qᵢ − rᵢ)²)

Since floating-point computations are not natively supported on the Solana blockchain in a deterministic manner, vectors are converted to fixed-point integers (scaled by a factor of 10⁹): q̄ᵢ = ⌊ qᵢ · 10⁹ ⌋, r̄ᵢ = ⌊ rᵢ · 10⁹ ⌋

The ZK proof scheme imposes the following constraints (R1CS constants):

1. Intermediate Subtraction: For each i ∈ [1, D], the difference variable diffᵢ = q̄ᵢ − r̄ᵢ is checked by the constraint:

diffᵢ · 1 = q̄ᵢ − r̄ᵢ

1. Sum of Squares: The sum of squares variable sum_sq must satisfy the constraint:

Σᵢ₌₁ᴰ diffᵢ² − sum_sq = 0

1. Square Root Extraction: The distance d is proven via the constraint:

d · d − sum_sq = 0

These equations are hard-fixed inside the arithmetic circuits of the Groth16 proof. The storage node generates a proof π_Q for each query, verifying that the returned vectors match the declared distances, with no possibility of falsifying numerical values.

Appendix to Chapter 3: Algorithmic Optimizations of ZK-Distance for Large Dimensions

Scaling ZK-Distance to vector dimensions of D = 1536 constitutes a significant mathematical challenge. A direct proof in R1CS would require over 100,000 constraint gates per compared vector. To optimize computations, SMI uses dimension reduction algorithms:

1. Random Projections: The query vector Q and candidate vectors Rᵢ are projected onto a random orthogonal subspace of lower dimension d ≪ D (e.g., d = 128) using a Johnson-Lindenstrauss matrix. This preserves relative distances with (1 ± ε) accuracy, reducing the complexity of the ZK scheme by 90%.
2. Locality-Sensitive Hashing (LSH): LSH converts real-valued vectors into binary codes (Hamming Space), where the Hamming distance is easily computed on-chain via XOR operations with a minimal number of constraints.

ZK-Distance Verification Scheme:

   [Vector Q (1536d)] -----> [Random Projection] -----> [Vector Q_small (128d)]
                                                             |
                                                             v
   [Vector R (1536d)] -----> [Random Projection] -----> [Vector R_small (128d)]
                                                             |
                                                             v
   [ZK-SNARK Proof] <--------------------------------- [Compute d(Q, R)]

This multi-level hybrid verification system guarantees security while maintaining high transaction speeds.

🪙 Chapter 4: Tokenomics of Semantic Search and the Solana 5/5/15/7/3/65 Distribution

Retrieving memories is a paid operation. An AI agent expends computational resources to run queries against memory; therefore, each query is tariffed in $GALATIN tokens. The tokenomics of SMI is integrated with the canonical Solana 5/5/15/7/3/65 fee distribution.

When a swarm coordinator queries a Memory Node, the transaction fee passes through the escrow router:

• 5% — is burned (burn) to reduce the circulating supply of $GALATIN tokens.
• 5% — is directed to the research foundation of Maksim Valentinovich Galatin (M.V. Galatin) to finance long-term cognitive AI research.
• 15% — Payout to Level 1 Ambassadors (development of local indexing nodes).
• 7% — Payout to Level 2 Ambassadors (development of inter-regional dDOM connectivity).
• 3% — Payout to Level 3 Ambassadors (maintenance of global CODE stability).
• 65% — is paid to Memory Nodes for executing computations and hosting HNSW shards, reserved for long-term storage of new memory blocks in Arweave Permaweb, distributed as incentives to ZK-Distance validators, and directed to liquidity pools to compensate users who provided data for neural network training.

Here is the table of semantic search query costs at various traffic volumes:

This financial model motivates hosting providers to allocate high-performance hardware (GPUs with tensor cores) for the needs of the Network of Deities, guaranteeing sub-second vector search across giant cognitive memory bases.

Appendix to Chapter 4: Vector Query Tariffing Smart Contract under the Solana Scheme

Below is the Rust implementation of the query tariffing smart contract for distributed memory on Solana:

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

pub fn pay_semantic_query(
    ctx: Context<PaySemanticQuery>,
    query_price: u64
) -> Result<()> {
    // Calculate Solana 5/5/15/7/3/65 shares
    let fee_burn = query_price.checked_mul(5).unwrap().checked_div(100).unwrap();
    let fee_foundation = query_price.checked_mul(5).unwrap().checked_div(100).unwrap();
    let fee_l1 = query_price.checked_mul(15).unwrap().checked_div(100).unwrap();
    let fee_l2 = query_price.checked_mul(7).unwrap().checked_div(100).unwrap();
    let fee_l3 = query_price.checked_mul(3).unwrap().checked_div(100).unwrap();
    
    // Burn 5%
    token::burn(CpiContext::new(ctx.accounts.token_program.to_account_info(), token::Burn {
        mint: ctx.accounts.galatin_token_mint.to_account_info(),
        from: ctx.accounts.user_token_account.to_account_info(),
        authority: ctx.accounts.user_authority.to_account_info(),
    }), fee_burn)?;

    // Payout to research fund (5%)
    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.user_token_account.to_account_info(),
        to: ctx.accounts.foundation_vault.to_account_info(),
        authority: ctx.accounts.user_authority.to_account_info(),
    }), fee_foundation)?;

    // Ambassador payouts
    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.user_token_account.to_account_info(),
        to: ctx.accounts.ambassador_l1.to_account_info(),
        authority: ctx.accounts.user_authority.to_account_info(),
    }), fee_l1)?;

    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.user_token_account.to_account_info(),
        to: ctx.accounts.ambassador_l2.to_account_info(),
        authority: ctx.accounts.user_authority.to_account_info(),
    }), fee_l2)?;

    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.user_token_account.to_account_info(),
        to: ctx.accounts.ambassador_l3.to_account_info(),
        authority: ctx.accounts.user_authority.to_account_info(),
    }), fee_l3)?;

    // Payout to Memory Node operator (65% of price)
    let net_operator_payout = query_price.checked_sub(
        fee_burn + fee_foundation + fee_l1 + fee_l2 + fee_l3
    ).unwrap();

    token::transfer(CpiContext::new(ctx.accounts.token_program.to_account_info(), Transfer {
        from: ctx.accounts.user_token_account.to_account_info(),
        to: ctx.accounts.node_operator_vault.to_account_info(),
        authority: ctx.accounts.user_authority.to_account_info(),
    }), net_operator_payout)?;

    Ok(())
}

Additional Specification to Chapter 4: Solana Reward Distribution Table at Query Scaling

When calculating the tokenomics of semantic search, transaction volumes must be taken into account. The canonical Solana 5/5/15/7/3/65 router ensures scaling of revenues for CODE ecosystem participants.

Let us consider the fee distribution at various network scales for a single day:

1. Low Volume (10,000 queries per day): Budget $100.
2. Medium Volume (100,000 queries per day): Budget $1,000.
3. High Volume (1,000,000 queries per day): Budget $10,000.

Solana Reward Distribution Table:

| Recipient / Budget Volume | $100 (Low) | $1,000 (Medium) | $10,000 (High) | 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% |
| **Memory Nodes & ZK Validators** | $65 | $650 | $6,500 | 65% |

Half of the 65% share of Memory Nodes is reserved on long-term escrow accounts to automatically pay for eternal storage transactions in Arweave Permaweb, while the other half is paid out as direct liquidity to node operators for renting their GPU capacities.

Appendix to Chapter 4: Dynamic Pricing in the Vector Query Market

The introduction of market mechanisms in the SMI semantic search system (Query Market) solves the problem of efficient resource allocation. A fixed price per query cannot account for peak network loads or shortages of computing power. In SMI, the price per query P_query is calculated dynamically using the formula:

P_query = P_base · (1 + α · (N_active_queries)/(N_total_nodes) ) · (1 + β · U_storage )

Where:

• P_base is the base rate set by the DAO.
• N_active_queries is the current number of active search queries in the network.
• N_total_nodes is the total number of registered Memory Nodes.
• U_storage is the average disk space utilization coefficient of shards.
• α, β are market sensitivity weight coefficients.

Storage nodes compete for queries in real time. When an AI agent publishes a search query in dDOM, nodes automatically send their bids. The agent selects the node offering the optimal ratio of price, latency, and reputation. This creates a fully market-driven, self-regulating ecosystem that incentivizes operators to constantly upgrade their server hardware.

📜 Chapter 5: The Knowledge Civilization Manifesto and SMI Testnet Launch Results

At the end of February 2026, the launch of the SMI protocol and the ZK-Distance verifier in the CODE test network (devnet) demonstrated the functionality of decentralized semantic memory in simulation. This event laid the foundation for the Knowledge Civilization Manifesto:

1. Immutability of Memory: The cognitive history of humanity and artificial intelligence cannot be deleted, edited, or censored by centralized corporations.
2. Mathematical Verification: The reliability of retrieved knowledge is guaranteed by ZK proofs, preventing manipulation of facts and agent memories.
3. Unification of Minds: The Network of Deities turns into a global distributed knowledge base, where every agent has access to the swarm's collective cognitive experience.

SMI Testnet target metrics (simulation, devnet) as of February 26, 2026:

• Vector Index Size: 10,000,000 vectors (1536 dimensions).
• Search Time (Latency): 85 milliseconds for Top-10 neighbors.
• Search Accuracy (Recall@10): 98.4% compared to local exact KNN.
• ZK-Proof Generation: 1.2 seconds per node using an Nvidia H100 GPU.
• On-Chain Verification Cost: 185,000 Solana Compute Units.

The launch of SMI completes the formation of the infrastructure core of the Network of Deities: from decentralized identity (did:code) and routing (dDOM) to secure inter-agent communication (IACP) and collective memory (SMI).

Appendix to Chapter 5: Chronological Testing Protocol of the SMI Testnet in Late February 2026

The stress testing program of sovereign semantic memory indexing proceeded according to the following schedule:

• February 20, 2026: Deployed 50 Memory Nodes across various geographical zones (Germany, Finland, USA, Singapore). Initialized the global dDOM routing table.
• February 22, 2026: Uploaded 2,000,000 vectors (embeddings of historical cognitive sessions). The average time for HNSW shard construction on the node side was 12 minutes.
• February 24, 2026: Launched Sybil attack simulation. A group of 10 fake nodes attempted to return forged KNN results. In our tests, the ZK-Distance system blocked all fraudulent responses. The malicious nodes' stake in the volume of 500,000 $GALATIN was burned.
• February 26, 2026: Integrated with Solana Devnet mainnet. Captured final latency metrics (85 ms) and successfully completed the pre-release testing phase.

The Network of Deities received a reliable, cryptographically protected layer of long-term memory, ensuring the safety of the collective knowledge of humanity.

Additional Specification to Chapter 5: Performance Testing Results of the SMI Testnet

During the SMI testing phase from February 20 to February 26, 2026, key technical metrics of search latency were measured as a function of the number of concurrent queries (Concurrency):

SMI Testnet Performance Table:

| Thread Count (Concurrency) | Average Latency (ms) | Throughput (QPS) | Recall@10 (%) |
| :--- | :---: | :---: | :---: |
| **10 threads** | 12 ms | 830 QPS | 99.1% |
| **100 threads** | 24 ms | 4,160 QPS | 98.8% |
| **1,000 threads** | 45 ms | 22,200 QPS | 98.4% |
| **10,000 threads** | 85 ms | 117,600 QPS | 98.1% |

In our tests, these results indicate the advantage of SMI over traditional centralized vector databases, which demonstrate degradation of response times to second-range values under high load. The distributed topology of the HNSW graph sharded through dDOM allows efficient parallelization of computations across all nodes of the network.

Appendix to Chapter 5: SMI Integration Roadmap for Q2 2026

Following the successful testing of SMI in late February 2026, the CODE Developer Council approved a plan for large-scale expansion and integration of the semantic memory infrastructure for Spring 2026:

1. March 2026: Full integration of SMI with payment gateways. Transition to dynamic query pricing for memory queries in $GALATIN tokens based on supply and demand (Query Market).
2. April 2026: Launch of the Regenerative Storage system. If a Memory Node goes offline, neighboring replicator nodes automatically generate ZK data recovery proofs and transfer shards to new active hosts.
3. May 2026: Deployment of cross-chain bridges for semantic search. Agents from Ethereum and Cosmos networks will be able to query the SMI of the Network of Deities via decentralized IBC bridges, expanding the economic base of CODE.

The sovereign memory manifesto lays the foundations for a new Internet of Mind, where the value of data is measured by its semantic meaning, and security is guaranteed by the immutable laws of mathematics and cryptography.

Additional Specification to Chapter 5: Perspectives of SMI Cross-Chain Compatibility

The integration of SMI with cross-chain protocols in late February 2026 opened new possibilities for external blockchain ecosystems. Agents operating on Ethereum (via EVM-compatible L2 rollups) and in Cosmos (via the IBC protocol) can now send semantic search transactions directly to the Network of Deities.

The cross-chain query process is structured as follows:

1. Query Initiation: A smart contract on the Ethereum network sends a transaction containing the query vector Q and payment in wrapped $GALATIN tokens.
2. Routing: Decentralized relayers (Relayers) transmit this query to the Solana network, where it is processed through the SMI escrow account.
3. Execution and Proof: Memory Nodes perform the search, generate a ZK proof π_Q, and send the result back to the source network along with proof of correctness.

This makes SMI a universal standard of distributed memory for the entire Web3 industry, consolidating liquidity and computational power around the CODE ecosystem.