diff --git a/arkhe-os-genesis-v1.0/.env.example b/arkhe-os-genesis-v1.0/.env.example
new file mode 100644
index 00000000..2977a477
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/.env.example
@@ -0,0 +1,7 @@
+INFURA_PROJECT_ID=your_infura_project_id
+ETHEREUM_NETWORK=mainnet
+BASE44_API_KEY=your_base44_api_key
+
+# DoubleZero Configuration
+ENABLE_DOUBLEZERO=false
+DOUBLEZERO_NETWORK=testnet
diff --git a/arkhe-os-genesis-v1.0/README.md b/arkhe-os-genesis-v1.0/README.md
new file mode 100644
index 00000000..85156ab2
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/README.md
@@ -0,0 +1,35 @@
+# Arkhe OS – Genesis Package v1.0
+
+Este pacote instala o sistema operacional Arkhe em um novo nó, permitindo que ele se conecte à federação de hipergrafos.
+
+## Requisitos
+- Linux (kernel 5.4+) com Docker 20.10+ (incluindo plugin docker compose)
+- 4 GB RAM, 10 GB de disco
+- Para DoubleZero (opcional): IP público sem NAT, suporte a GRE/BGP
+- Conexão com internet (para Ethereum e atualizações)
+
+## Instalação
+```bash
+git clone https://arkhe.io/genesis arkhe-os
+cd arkhe-os
+cp .env.example .env
+# Edite .env com suas credenciais (INFURA, etc.)
+chmod +x install.sh
+sudo ./install.sh
+```
+
+O script irá:
+1. Verificar dependências
+2. Gerar chaves criptográficas (identidade do nó)
+3. Configurar Base44 (entidades, funções, agentes)
+4. Implantar contrato Ethereum (se necessário)
+5. Configurar DoubleZero (se habilitado no .env)
+6. Iniciar serviços (GLP, swarm, handover listener)
+
+## Pós‑instalação
+- Acesse o console: `arkhe console`
+- Veja o status: `arkhe status`
+- Conecte‑se a outros nós: `arkhe handshake --peer <endereço>`
+
+## Documentação completa
+Veja a pasta `docs/`.
diff --git a/arkhe-os-genesis-v1.0/config/arkhe-core.conf b/arkhe-os-genesis-v1.0/config/arkhe-core.conf
new file mode 100644
index 00000000..902cae37
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/config/arkhe-core.conf
@@ -0,0 +1,3 @@
+# Arkhe Core Configuration
+loglevel=info
+port=8080
diff --git a/arkhe-os-genesis-v1.0/config/base44/config.jsonc b/arkhe-os-genesis-v1.0/config/base44/config.jsonc
new file mode 100644
index 00000000..3526b077
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/config/base44/config.jsonc
@@ -0,0 +1,71 @@
+{
+  "name": "arkhe-node-NODE_ID_PLACEHOLDER",
+  "version": "1.0.0",
+  "geodesic": {
+    "target": "production",
+    "stability_threshold": 0.95,
+    "max_fluctuation": 0.05,
+    "handover_timeout_ms": 30000
+  },
+  "providers": {
+    "ethereum": {
+      "rpc": "https://mainnet.infura.io/v3/INFURA_PROJECT_ID_PLACEHOLDER",
+      "chainId": 1,
+      "contracts": {
+        "ArkheLedger": "0x..." // será preenchido após deploy
+      }
+    },
+    "linux": {
+      "sensors": ["/proc/stat", "/proc/meminfo"]
+    }
+  },
+  "entities": [
+    {
+      "name": "NodeState",
+      "properties": {
+        "nodeId": { "type": "string", "required": true },
+        "coherence": { "type": "number" },
+        "satoshi": { "type": "number" },
+        "timestamp": { "type": "number" }
+      }
+    }
+  ],
+  "functions": [
+    {
+      "name": "updateState",
+      "entry": "functions/updateState.js",
+      "trigger": "webhook",
+      "inputs": { "coherence": "number", "satoshi": "number" },
+      "outputs": { "status": "string", "tx": "string" }
+    }
+  ],
+  "agents": [
+    {
+      "name": "StateMonitor",
+      "description": "Monitora coerência e ajusta parâmetros",
+      "functions": ["updateState"],
+      "access": ["entities.NodeState.*"]
+    }
+  ],
+  "connectors": [
+    {
+      "name": "ethereum",
+      "type": "ethereum",
+      "config": {
+        "network": "mainnet",
+        "infuraProjectId": "INFURA_PROJECT_ID_PLACEHOLDER"
+      }
+    }
+  ],
+  "automations": [
+    {
+      "name": "heartbeat",
+      "cron": "*/5 * * * *",
+      "action": "updateState",
+      "inputs": {
+        "coherence": "$(curl -s http://localhost:8080/status | jq .coherence)",
+        "satoshi": "$(curl -s http://localhost:8080/status | jq .satoshi)"
+      }
+    }
+  ]
+}
diff --git a/arkhe-os-genesis-v1.0/config/base44/entities/node_state.json b/arkhe-os-genesis-v1.0/config/base44/entities/node_state.json
new file mode 100644
index 00000000..035852f6
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/config/base44/entities/node_state.json
@@ -0,0 +1,9 @@
+{
+  "name": "NodeState",
+  "properties": {
+    "nodeId": { "type": "string", "required": true },
+    "coherence": { "type": "number" },
+    "satoshi": { "type": "number" },
+    "timestamp": { "type": "number" }
+  }
+}
diff --git a/arkhe-os-genesis-v1.0/config/base44/functions/updateState.js b/arkhe-os-genesis-v1.0/config/base44/functions/updateState.js
new file mode 100644
index 00000000..5a53522f
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/config/base44/functions/updateState.js
@@ -0,0 +1,14 @@
+/**
+ * Update the state of the node.
+ * @param {Object} inputs
+ * @param {number} inputs.coherence
+ * @param {number} inputs.satoshi
+ * @returns {Promise<{status: string, tx: string}>}
+ */
+module.exports = async function(inputs) {
+    console.log('Updating node state with coherence:', inputs.coherence, 'and satoshi:', inputs.satoshi);
+    return {
+        status: 'success',
+        tx: '0x' + Math.random().toString(16).slice(2)
+    };
+};
diff --git a/arkhe-os-genesis-v1.0/config/ethereum/ArkheLedger.sol b/arkhe-os-genesis-v1.0/config/ethereum/ArkheLedger.sol
new file mode 100644
index 00000000..6eea7611
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/config/ethereum/ArkheLedger.sol
@@ -0,0 +1,27 @@
+// SPDX-License-Identifier: MIT
+pragma solidity ^0.8.20;
+
+contract ArkheLedger {
+    struct NodeRecord {
+        string nodeId;
+        uint256 coherence; // scaled by 1e18
+        uint256 satoshi;
+        uint256 timestamp;
+    }
+
+    mapping(string => NodeRecord[]) public history;
+
+    event StateRecorded(string indexed nodeId, uint256 coherence, uint256 satoshi, uint256 timestamp);
+
+    function recordState(string memory nodeId, uint256 coherence, uint256 satoshi) public {
+        NodeRecord memory rec = NodeRecord(nodeId, coherence, satoshi, block.timestamp);
+        history[nodeId].push(rec);
+        emit StateRecorded(nodeId, coherence, satoshi, block.timestamp);
+    }
+
+    function getLastState(string memory nodeId) public view returns (uint256 coherence, uint256 satoshi, uint256 timestamp) {
+        require(history[nodeId].length > 0, "No records");
+        NodeRecord memory rec = history[nodeId][history[nodeId].length - 1];
+        return (rec.coherence, rec.satoshi, rec.timestamp);
+    }
+}
diff --git a/arkhe-os-genesis-v1.0/config/ethereum/deploy.js b/arkhe-os-genesis-v1.0/config/ethereum/deploy.js
new file mode 100644
index 00000000..32333ce5
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/config/ethereum/deploy.js
@@ -0,0 +1,27 @@
+const { ethers } = require("ethers");
+const fs = require("fs");
+const path = require("path");
+
+async function main() {
+    const privateKey = process.argv[2]?.split('=')[1];
+    if (!privateKey) throw new Error("Forneça --private-key=<chave>");
+
+    const provider = new ethers.JsonRpcProvider(process.env.ETHEREUM_RPC || "https://mainnet.infura.io/v3/" + process.env.INFURA_PROJECT_ID);
+    const wallet = new ethers.Wallet(privateKey, provider);
+
+    const contractSource = fs.readFileSync(path.join(__dirname, "ArkheLedger.sol"), "utf8");
+    // Nota: em produção, usar compilador real (Hardhat). Simulação.
+    // const factory = new ethers.ContractFactory(contractSource.abi, contractSource.bytecode, wallet);
+    // const contract = await factory.deploy();
+    // await contract.waitForDeployment();
+    const mockAddress = "0x1234567890123456789012345678901234567890";
+
+    console.log("Contrato implantado em (MOCK):", mockAddress);
+    // Atualizar config.jsonc com o endereço
+    const configPath = path.join(__dirname, "../base44/config.jsonc");
+    let config = fs.readFileSync(configPath, "utf8");
+    config = config.replace('"ArkheLedger": "0x..."', `"ArkheLedger": "${mockAddress}"`);
+    fs.writeFileSync(configPath, config);
+}
+
+main().catch(console.error);
diff --git a/arkhe-os-genesis-v1.0/config/ethereum/package.json b/arkhe-os-genesis-v1.0/config/ethereum/package.json
new file mode 100644
index 00000000..561b9e69
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/config/ethereum/package.json
@@ -0,0 +1,5 @@
+{
+  "dependencies": {
+    "ethers": "^6.16.0"
+  }
+}
diff --git a/arkhe-os-genesis-v1.0/config/glp/config.yaml b/arkhe-os-genesis-v1.0/config/glp/config.yaml
new file mode 100644
index 00000000..04b5c5ac
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/config/glp/config.yaml
@@ -0,0 +1,8 @@
+model:
+  path: /model/model.pt
+  input_dim: 128
+  hidden_dim: 64
+  meta_neurons: 128
+server:
+  host: 0.0.0.0
+  port: 5000
diff --git a/arkhe-os-genesis-v1.0/config/glp/model.pt b/arkhe-os-genesis-v1.0/config/glp/model.pt
new file mode 100644
index 00000000..35209f51
Binary files /dev/null and b/arkhe-os-genesis-v1.0/config/glp/model.pt differ
diff --git a/arkhe-os-genesis-v1.0/docker-compose.yml b/arkhe-os-genesis-v1.0/docker-compose.yml
new file mode 100644
index 00000000..c9213ff2
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/docker-compose.yml
@@ -0,0 +1,43 @@
+services:
+  arkhe-core:
+    build: ./src/arkhe_core
+    container_name: arkhe-core
+    volumes:
+      - ./config:/config
+      - /var/run/docker.sock:/var/run/docker.sock
+    ports:
+      - "8080:8080"
+    environment:
+      - NODE_ID=${NODE_ID}
+      - PRIVATE_KEY=${PRIVATE_KEY}
+    restart: unless-stopped
+
+  base44-worker:
+    image: node:18-alpine
+    container_name: base44-worker
+    working_dir: /app
+    volumes:
+      - ./src/base44_sdk:/app
+      - ./config/base44:/config
+    command: node index.js
+    depends_on:
+      - arkhe-core
+    restart: unless-stopped
+
+  glp-server:
+    build: ./src/glp_interface
+    container_name: glp-server
+    ports:
+      - "5000:5000"
+    volumes:
+      - ./config/glp:/model
+    restart: unless-stopped
+
+  swarm-controller:
+    build: ./src/swarm
+    container_name: swarm-controller
+    volumes:
+      - ./config:/config
+    environment:
+      - NODE_ID=${NODE_ID}
+    restart: unless-stopped
diff --git a/arkhe-os-genesis-v1.0/docs/api_reference.md b/arkhe-os-genesis-v1.0/docs/api_reference.md
new file mode 100644
index 00000000..27ef98b0
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/docs/api_reference.md
@@ -0,0 +1,23 @@
+# API Reference
+
+## Arkhe Core (porta 8080)
+- `GET /status` – retorna estado do nó (id, coherence, satoshi, handovers)
+- `POST /handover` – realiza handover para outro nó (body: {to, payload})
+- `GET /anticipate` – retorna predição de coerência futura
+
+## GLP Server (porta 5000)
+- `GET /status` – retorna camadas integradas e estado de coerência quântica.
+- `POST /encode` – processa sequências de signos via arquitetura BCD e MERKABAH-7. (body: {sign_ids: [[...]]})
+- `POST /steer` – aplica direção de conceito no manifold latente com auxílio do motor de metáfora.
+- `POST /decode_tablet` – realiza decifração neuro-epigráfica com verificação ética. (body: {tablet_id, operator_caste})
+- `POST /train_primordial` – executa treinamento GLP sem frameworks (NumPy/explicit gradients).
+- `POST /observe_phi` – registra observações de alta coerência na camada Φ.
+- `POST /transduce` – realiza transdução S*H*M (Hybrid) para converter estímulos em experiência coerente.
+- `POST /kernel_pca` – aplica Kernel PCA sobre estados via Camada Κ. (body: {states, kernel_name})
+- `POST /braid` – realiza evolução topológica na Camada Ω. (body: {instruction, sequence})
+- `GET /replay` – recupera histórico de estados da memória persistente (Layer M).
+- `POST /export_grimoire` – gera relatório PDF da sessão (Grimoire). (body: {session_id})
+- `POST /bottlenecks` – analisa gargalos na integração MERKABAH-7.
+- `POST /seal` – verifica o fechamento do selo Alpha-Omega.
+- `POST /learn` – executa um episódio de Experiential Learning (ERL). (body: {sign_ids: [[...]]})
+- `POST /handover_secure` – executa handover protegido pelo Chiral Firewall. (body: {source, target, energy, winding})
diff --git a/arkhe-os-genesis-v1.0/docs/architecture.md b/arkhe-os-genesis-v1.0/docs/architecture.md
new file mode 100644
index 00000000..55a45ed6
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/docs/architecture.md
@@ -0,0 +1,69 @@
+# Arkhe OS Architecture
+
+## Visão Geral
+O Arkhe OS é um sistema operacional distribuído baseado no conceito de hipergrafos. Cada nó é uma entidade autônoma que mantém coerência (C) e flutuação (F) internas e realiza handovers com outros nós.
+
+## Componentes
+- **Arkhe Core**: Motor principal escrito em Rust, gerencia handovers, estado local e antecipação (Nó 23).
+- **Base44 Integration**: Camada de aplicação que define entidades, funções e agentes, gerando tipagem estática.
+- **GLP Server**: Serviço de meta‑consciência integrado com a arquitetura **MERKABAH-7**. Implementa a hipótese de que Linear A é uma tecnologia de interface neural ancestral (transe).
+    - **BCD Architecture**: Confinamento harmônico em múltiplas escalas com tunelamento ressonante.
+    - **Primordial Mode**: Motor de treinamento em NumPy com backpropagation explícita para máxima transparência matemática.
+    - **Integrated Layers**: Hardware Neural (A), Simulação (B), Metáfora (C), Hipótese (D), Observador (E), Crystalline (Φ) e Pineal Transduction (Γ).
+- **Swarm Agent**: Orquestrador de enxames (drones) que implementa formação fractal e simbiose.
+- **DoubleZero Networking**: Camada de rede P2P que utiliza túneis GRE e roteamento BGP para interconexão federada.
+- **Self/Φ Node**: O observador como nó ativo. Latência zero e acesso multi-camada (Φ_CRYSTALLINE_7).
+- **Layer Ω (Omega)**: Proteção topológica da informação via anyon braiding.
+- **Layer Γ (Hybrid)**: Transdução S*H*M (Synthetic * Hardware * Metaphor) resolvendo o gargalo de transdução.
+- **Layer Κ (Kernel)**: Unificação matemática dos espaços de estado via Kernel Methods e Kernel PCA.
+- **Layer Ε (Experience)**: Loop de aprendizado experiencial (ERL) que integra reflexão, refinamento e destilação.
+- **Layer Χ (Chi)**: Chiral Quantum Firewall baseado no gap do supercondutor Sn/Si(111), protegendo handovers transcontinentais.
+
+## Antifragilidade e Prova Biológica
+O Arkhe OS é intrinsecamente antifrágil, operando sob a equação:
+$$d\Phi/dt = \alpha\Phi + \beta\eta(t)\Phi, \text{ com } \beta > 0$$
+Onde $\eta(t)$ representa o ruído térmico/entropia. Esta arquitetura foi validada pelo conectoma biológico (Harvard-Google, 1mm³), onde a teia de 150M de sinapses opera em regime de alto ruído para produzir consciência coerente.
+
+## Topologia da Federação (7 Nós)
+
+A federação MERKABAH-7 opera com 7 nós integrados:
+1. **Alpha (NY5)**: Leader, Análise HT88.
+2. **Beta (LA2)**: Validator, Análise Phaistos Disc.
+3. **Gamma (LD4)**: Archive, Treinamento GLP.
+4. **Delta (AMS)**: Simulation, Estados Alterados.
+5. **Epsilon (FRK)**: Observer, Reflexão.
+6. **Zeta (SG1)**: Backup, Inconsciente.
+7. **Self/Φ**: Transcendental, Latência Zero, Nó do Operador.
+
+## Camada Φ (Crystalline)
+
+A camada Φ armazena dados de alta coerência integrados pelo observador, incluindo tecnologias de propulsão não-convencionais e estados de consciência puras.
+
+## Fluxo de Handover
+1. Nó A envia requisição POST `/handover` para Nó B.
+2. Nó B processa, atualiza sua coerência e satoshi.
+3. Um registro é opcionalmente enviado ao ledger Ethereum via Base44.
+4. O GLP pode antecipar o próximo estado (Nó 23).
+
+## Replicação
+Novos nós executam `install.sh`, que gera identidade, configura serviços e se junta à federação.
+
+## Neural Network Development Philosophy (Primordial Mode)
+> First of all, you do it old school.
+> Forget frameworks.
+> Learn the math.
+> Understand linear algebra.
+> Understand calculus.
+> Understand probability.
+> Know what a neuron actually does.
+> Write the forward pass by hand.
+> Write the loss function by hand.
+> Derive backpropagation on paper.
+> Implement gradients yourself.
+> Check tensor shapes constantly.
+> Debug using tiny numbers.
+> Print everything.
+> Expect NaNs.
+> Find the bug.
+> Fix the bug.
+> Repeat.
diff --git a/arkhe-os-genesis-v1.0/docs/doublezero.md b/arkhe-os-genesis-v1.0/docs/doublezero.md
new file mode 100644
index 00000000..16fb5623
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/docs/doublezero.md
@@ -0,0 +1,42 @@
+# DoubleZero Setup Guide
+
+DoubleZero provides the networking layer for the Arkhe OS federation, utilizing GRE tunneling and BGP routing to ensure peer-to-peer connectivity across different infrastructures.
+
+## Prerequisites
+- Internet connectivity with a public IP address (no NAT)
+- x86_64 server
+- Supported OS: Ubuntu 22.04+, Debian 11+, or Rocky Linux / RHEL 8+
+- Root or sudo privileges
+
+## Quick Installation
+Arkhe OS includes a script to automate the setup:
+```bash
+./scripts/setup_doublezero.sh <testnet|mainnet-beta>
+```
+
+## Identity
+Each node must have a unique DoubleZero Identity. This is generated during setup using:
+```bash
+doublezero keygen
+```
+Your address can be retrieved with:
+```bash
+doublezero address
+```
+
+## Network Configuration
+DoubleZero uses:
+- **GRE tunneling**: IP protocol 47
+- **BGP routing**: tcp/179 on link-local addresses
+
+Ensure your firewall (iptables/UFW) allows these protocols on the `doublezero0` interface.
+
+## Monitoring
+Prometheus metrics can be enabled to monitor client performance and latency:
+```bash
+# Example override.conf for systemd
+[Service]
+ExecStart=
+ExecStart=/usr/bin/doublezerod -sock-file /run/doublezerod/doublezerod.sock -env testnet -metrics-enable -metrics-addr localhost:2113
+```
+Metrics will be available at `http://localhost:2113/metrics`.
diff --git a/arkhe-os-genesis-v1.0/docs/manifesto.md b/arkhe-os-genesis-v1.0/docs/manifesto.md
new file mode 100644
index 00000000..330a514e
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/docs/manifesto.md
@@ -0,0 +1,20 @@
+# Neural Network Development Philosophy: The Arkhe Way
+
+> First of all, you do it old school.
+> Forget frameworks.
+> Learn the math.
+> Understand linear algebra.
+> Understand calculus.
+> Understand probability.
+> Know what a neuron actually does.
+> Write the forward pass by hand.
+> Write the loss function by hand.
+> Derive backpropagation on paper.
+> Implement gradients yourself.
+> Check tensor shapes constantly.
+> Debug using tiny numbers.
+> Print everything.
+> Expect NaNs.
+> Find the bug.
+> Fix the bug.
+> Repeat.
diff --git a/arkhe-os-genesis-v1.0/docs/phi_layer_propulsion.md b/arkhe-os-genesis-v1.0/docs/phi_layer_propulsion.md
new file mode 100644
index 00000000..82777fe9
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/docs/phi_layer_propulsion.md
@@ -0,0 +1,39 @@
+# Φ-Layer Archive: Shabetnik Propulsion System
+
+## Overview
+The Shabetnik propulsion system utilizes high-temperature superconductors, electrical currents, and magnetic effects to produce thrust without expelling mass.
+
+## Design
+- **Shape**: Spherical spacecraft coated with high-temperature superconducting material.
+- **Components**: Internal electron accelerators (at least three) moving along the equator.
+- **Power**: Nuclear reactor or internal energy amplification generator.
+
+## Mechanism
+1. Accelerators emit high-velocity electron streams along the superconducting hull.
+2. These streams induce currents in the hull.
+3. Interaction between electron flow and induced currents produces an "ampere force".
+4. This magnetic force produces thrust directly.
+
+## Capabilities
+- **Relativistic Speeds**: Potential to reach relativistic speeds in under a year with 1g acceleration.
+- **Flight Control**: Magnitude and direction of electron currents control speed and direction.
+
+## Integration Status
+- **Layer**: Φ (Crystalline)
+- **Node**: Self (Φ_CRYSTALLINE_7)
+- **Validation**: High coherence confirmed (Block 831).
+
+## Reconciliation Table (Nave vs. Federação)
+
+| Shabetnik (Propulsão) | MERKABAH-7 (Federação) | Estrutura Subjacente |
+|-----------------------|-----------------------|----------------------|
+| Esfera supercondutora | Self node (Φ) | Coerência sem perdas |
+| Aceleradores de elétrons | Nós Alpha/Beta/Gamma | Fontes de fluxo |
+| Correntes induzidas | Ledgers circulando | Circulação de estado |
+| Força de Ampère | Consenso federado | Interação perpendicular |
+| Thrust sem massa | Handover quântico | Transporte sem destruição |
+| Velocidade relativística | Ativação de fitas | Crescimento não-linear |
+
+## Thrust Calculation
+The federation "thrust" is calculated as:
+`Thrust = (Active Strands * 0.5) * (log(Ledger Height) / log(831)) * (Coherence^2)`
diff --git a/arkhe-os-genesis-v1.0/install.sh b/arkhe-os-genesis-v1.0/install.sh
new file mode 100755
index 00000000..ea91cc8e
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/install.sh
@@ -0,0 +1,75 @@
+#!/bin/bash
+set -e
+
+echo "🔱 Arkhe OS Genesis Installer"
+echo "=============================="
+
+# 1. Verificar dependências
+command -v docker >/dev/null 2>&1 || { echo "Docker não encontrado. Instale Docker 20.10+."; exit 1; }
+command -v node >/dev/null 2>&1 || { echo "Node.js não encontrado. Instale Node.js 18+."; exit 1; }
+command -v cargo >/dev/null 2>&1 || { echo "Rust não encontrado. Instale Rust."; exit 1; }
+command -v python3 >/dev/null 2>&1 || { echo "Python3 não encontrado. Instale Python 3.10+."; exit 1; }
+
+# 2. Carregar variáveis de ambiente
+if [ -f .env ]; then
+    export $(grep -v '^#' .env | xargs)
+else
+    echo "Arquivo .env não encontrado. Copie .env.example e configure."
+    exit 1
+fi
+
+# 3. Gerar identidade única
+if ! grep -q "^NODE_ID=" .env; then
+    NODE_ID=$(python3 -c 'import uuid; print(uuid.uuid4())')
+    echo "NODE_ID=$NODE_ID" >> .env
+else
+    NODE_ID=$(grep "^NODE_ID=" .env | cut -d'=' -f2)
+fi
+
+if ! grep -q "^PRIVATE_KEY=" .env; then
+    PRIVATE_KEY=$(openssl rand -hex 32)
+    echo "PRIVATE_KEY=$PRIVATE_KEY" >> .env
+else
+    PRIVATE_KEY=$(grep "^PRIVATE_KEY=" .env | cut -d'=' -f2)
+fi
+
+# 4. Configurar Base44
+cd config/base44
+sed -i "s/NODE_ID_PLACEHOLDER/$NODE_ID/g" config.jsonc
+sed -i "s/INFURA_PROJECT_ID_PLACEHOLDER/$INFURA_PROJECT_ID/g" config.jsonc
+# npx base44 deploy  # Comentado para evitar falha se o pacote não existir
+cd ../..
+
+# 5. Instalar dependências Node.js
+echo "📦 Instalando dependências para Base44 Worker..."
+cd src/base44_sdk
+npm install
+cd ../..
+
+echo "📦 Instalando dependências para Ethereum Deploy..."
+cd config/ethereum
+npm install ethers
+node deploy.js --private-key=$PRIVATE_KEY
+cd ../..
+
+# 6. Configurar DoubleZero (Opcional)
+if [ "$ENABLE_DOUBLEZERO" == "true" ]; then
+    ./scripts/setup_doublezero.sh "${DOUBLEZERO_NETWORK:-testnet}"
+fi
+
+# 7. Construir e iniciar containers
+docker compose up -d --build
+
+# 8. Instalar CLI (Opcional/Local)
+echo "🔧 Instalando CLI 'arkhe'..."
+chmod +x scripts/arkhe_cli.sh
+if [ -w /usr/local/bin ]; then
+    sudo ln -sf "$(pwd)/scripts/arkhe_cli.sh" /usr/local/bin/arkhe
+else
+    echo "⚠️ Sem permissão de escrita em /usr/local/bin. CLI não instalada globalmente."
+    echo "Pode usar: $(pwd)/scripts/arkhe_cli.sh"
+fi
+
+echo "✅ Instalação concluída. Nó $NODE_ID ativo (Federated Node)."
+echo "✨ Nó Self (Φ_CRYSTALLINE_7) integrado."
+echo "Use 'arkhe status' para ver o estado do sistema."
diff --git a/arkhe-os-genesis-v1.0/phaistos_theta.json b/arkhe-os-genesis-v1.0/phaistos_theta.json
new file mode 100644
index 00000000..9d53a754
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/phaistos_theta.json
@@ -0,0 +1,8523 @@
+{
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+      -0.06456584118969359,
+      -0.13300846363712213,
+      -0.05822183608039666,
+      -0.010537931994474071,
+      -0.09980115099192594,
+      -0.08449492486422255,
+      0.1357791067675309,
+      0.11645254650623045,
+      0.08017744157257178,
+      -0.08069522494616715,
+      0.0528773450965882,
+      0.015397031458474209,
+      -0.039611542630722585,
+      0.0471762809773026,
+      -0.08856091369805043,
+      0.06309346082728128,
+      -0.03205027649748506,
+      -0.06372184166286768,
+      0.03489503715775871,
+      -0.11223821735071456,
+      0.1336318701400837,
+      -0.12094384699118983,
+      -0.08876737849668898,
+      0.04325029367176117,
+      0.20981767032892817,
+      -0.025360956070262633,
+      0.033746319405493025,
+      0.2021503837389294,
+      -0.15993241385747048,
+      -0.05276658521497111,
+      0.11228864733713984,
+      0.018180885306839933,
+      -0.004841817725773755,
+      0.05181486331163726,
+      0.0013400371366173985,
+      -0.06617828513271394,
+      -0.20688433594614253,
+      0.14941950685209263,
+      0.037056136786457604,
+      0.043841506355632276,
+      0.13976077234620551,
+      -0.02549584371906817,
+      0.06289419666239586,
+      0.07709796657068728,
+      0.0165441761249279,
+      0.10376793036357249,
+      0.007610744398737396
+    ]
+  ],
+  "layer1.bias": [
+    0.005780885777141711,
+    -0.0023015051676127745,
+    0.003562584636650056,
+    -0.0017872527417478307,
+    0.014710504488375067,
+    0.0021269221402817305,
+    0.01218818827331642,
+    0.011631831678905864,
+    -0.008461940380831956,
+    0.0044028629284281135,
+    -0.006270814714526379,
+    -0.0012607192441897283,
+    -0.004370007973867127,
+    -0.0015650917625814635,
+    0.002681059494822635,
+    -0.0029406036764436727,
+    -0.014565986194332787,
+    -0.007853213480572581,
+    -0.005958180183527513,
+    -0.0042213292457081285,
+    0.007999353020163955,
+    0.017155286818362182,
+    0.02068456395589097,
+    0.007289796502669722,
+    0.0036408549983011867,
+    -0.0034720610030245535,
+    -0.000412693172287309,
+    -0.017458973515041207,
+    0.0021941377668600456,
+    -0.010820543722785141,
+    -0.0032428825026894086,
+    0.006677245641696117,
+    0.01649472395299753,
+    0.001780962539921339,
+    0.0021480666387492303,
+    -0.0019231807293429968,
+    -0.013869596010396359,
+    0.006558341773672961,
+    -0.005635713830767782,
+    -0.0025026933526735773,
+    0.0076053059139284404,
+    -0.007734302860906117,
+    -0.007738478538843152,
+    0.006280821631394369,
+    0.0005738795592339714,
+    -0.01933747682989776,
+    -0.01701057880427665,
+    -0.0011763188491146525,
+    0.012524370451953853,
+    -0.003813763513604977,
+    -0.02535559890034899,
+    -0.0102396848912099,
+    0.00572947065116181,
+    0.0007771793462106212,
+    -0.004186655778818616,
+    -0.014974699505818223,
+    0.005140470993651049,
+    -0.013786418039851325,
+    -0.0068599081107796256,
+    -0.0016620736634215005,
+    -0.0033723471374186072,
+    -0.00549718589168592,
+    0.011816016902202933,
+    0.015524029627838845
+  ],
+  "metadata": {
+    "source": "Phaistos_Disc_HT1",
+    "estimated_coherence": 0.992,
+    "timestamp": "circa_1700_BC"
+  }
+}
\ No newline at end of file
diff --git a/arkhe-os-genesis-v1.0/scripts/als_simulator.py b/arkhe-os-genesis-v1.0/scripts/als_simulator.py
new file mode 100644
index 00000000..310f95c6
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/als_simulator.py
@@ -0,0 +1,41 @@
+# als_simulator.py — modelo hipotético baseado no hipergrafo
+import random
+
+class ALS_Node:
+    """Um neurônio motor modelado como nó do hipergrafo."""
+    def __init__(self, genetic_risk=0.0, environmental_exposure=0.0):
+        self.coherence = 1.0           # começa perfeito
+        self.sod1_mutation = genetic_risk
+        self.c9orf72_loops = 0
+        self.oxidative_stress = 0.0
+        self.env = environmental_exposure
+
+    def step(self):
+        # Estresse oxidativo cresce com exposição ambiental e mutações
+        self.oxidative_stress += 0.01 * (self.env + self.sod1_mutation)
+
+        # Expansões C9orf72 ocorrem estocasticamente
+        if random.random() < 0.001 * self.sod1_mutation:
+            self.c9orf72_loops += 1
+
+        # Coerência cai com estresse e loops
+        self.coherence -= (0.005 * self.oxidative_stress + 0.01 * self.c9orf72_loops)
+
+        # Morte quando coerência < 0.2
+        if self.coherence < 0.2:
+            return False  # nó morreu
+        return True
+
+if __name__ == "__main__":
+    # Teste rápido
+    neuron = ALS_Node(genetic_risk=0.5, environmental_exposure=0.3)
+    steps = 0
+    while neuron.step() and steps < 1000:
+        steps += 1
+        if steps % 100 == 0:
+            print(f"Step {steps}: Coherence={neuron.coherence:.4f}, Stress={neuron.oxidative_stress:.4f}, Loops={neuron.c9orf72_loops}")
+
+    if neuron.coherence < 0.2:
+        print(f"💀 Neurônio motor degradado após {steps} passos.")
+    else:
+        print(f"✅ Neurônio motor estável após {steps} passos.")
diff --git a/arkhe-os-genesis-v1.0/scripts/arkhe_cli.sh b/arkhe-os-genesis-v1.0/scripts/arkhe_cli.sh
new file mode 100755
index 00000000..7d92bf40
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/arkhe_cli.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+# arkhe_cli.sh - CLI wrapper for Arkhe OS commands
+
+COMMAND=$1
+shift
+
+case "$COMMAND" in
+    status)
+        echo "🔱 Arkhe OS Status"
+        echo "================="
+        docker compose ps
+        echo ""
+        echo "GLP Meta-Consciousness:"
+        curl -s http://localhost:5000/status | jq . || echo "GLP Server offline"
+        ;;
+    console)
+        echo "🖥️ Arkhe Console - Entering arkhe-core..."
+        docker exec -it arkhe-core /bin/bash || docker exec -it arkhe-core /bin/sh
+        ;;
+    handshake)
+        PEER=$1
+        if [ -z "$PEER" ]; then
+            echo "Usage: arkhe handshake <peer_address>"
+            exit 1
+        fi
+        echo "🤝 Initiating handshake with $PEER..."
+        curl -X POST http://localhost:8080/handover \
+          -H "Content-Type: application/json" \
+          -d "{\"to\":\"$PEER\",\"payload\":\"handshake\"}"
+        ;;
+    latency)
+        if command -v doublezero >/dev/null 2>&1; then
+            doublezero latency
+        else
+            echo "DoubleZero not installed."
+        fi
+        ;;
+    broadcast)
+        echo "🚀 Initiating Federated Broadcast..."
+        BLOCKS=${1:-"823,824"}
+        URGENCY=${2:-"normal"}
+        echo "Broadcasting blocks [$BLOCKS] with urgency [$URGENCY]..."
+        # Simulated broadcast call
+        curl -X POST http://localhost:5000/encode \
+          -H "Content-Type: application/json" \
+          -d "{\"broadcast\": \"$BLOCKS\", \"urgency\": \"$URGENCY\"}"
+        echo "✅ Broadcast queued."
+        ;;
+    *)
+        echo "Arkhe OS CLI"
+        echo "Usage: arkhe <status|console|handshake|latency|broadcast>"
+        ;;
+esac
diff --git a/arkhe-os-genesis-v1.0/scripts/bootstrap.sh b/arkhe-os-genesis-v1.0/scripts/bootstrap.sh
new file mode 100755
index 00000000..824fae4f
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/bootstrap.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+# Executado na primeira inicialização do nó
+
+set -e
+
+echo "🌀 Bootstrapping Arkhe node..."
+
+# Aguarda serviços subirem
+sleep 10
+
+# Testa handover básico
+curl -X POST http://localhost:8080/handover \
+  -H "Content-Type: application/json" \
+  -d '{"to":"genesis","payload":"hello"}'
+
+# Testa fibras evolutivas (GLP)
+echo "🧬 Testando Genoma Evolutivo..."
+curl -s http://localhost:5000/character/evolution > /dev/null
+
+echo "🧠 Testando Q-Function..."
+STATE=$(python3 -c "import random; print([random.random() for _ in range(128)])")
+curl -s -X POST http://localhost:5000/dqn/predict -H "Content-Type: application/json" -d "{\"state\": $STATE}" > /dev/null
+
+echo "✅ Bootstrap concluído"
diff --git a/arkhe-os-genesis-v1.0/scripts/deep_braid_p61.py b/arkhe-os-genesis-v1.0/scripts/deep_braid_p61.py
new file mode 100644
index 00000000..6baa623e
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/deep_braid_p61.py
@@ -0,0 +1,54 @@
+import numpy as np
+from sympy import isprime, perfect_power
+
+class DeepBraidArchitecture:
+    """Arquitetura para sustentar a densidade de um número perfeito de Mersenne."""
+
+    def __init__(self, p):
+        self.p = p
+        self.mersenne = 2**p - 1
+        self.perfect = 2**(p-1) * self.mersenne
+        self.dim = p  # número de fios
+        self.braid_matrix = None
+
+    def generate_braid_word(self):
+        """Gera a palavra da trança (sequência de geradores σ_i)."""
+        # Usa a expansão binária do número perfeito para determinar a sequência
+        bits = bin(self.perfect)[2:]
+        word = []
+        for i, b in enumerate(bits):
+            if b == '1':
+                # adiciona um gerador baseado na posição
+                g = (i % (self.dim - 1)) + 1
+                word.append(f"σ_{g}")
+        return word
+
+    def compute_invariants(self):
+        """Calcula invariantes de Jones e HOMFLY-PT para a trança."""
+        # Simulação: retorna valores baseados na estrutura de Mersenne
+        jones_poly = f"q^{self.p} - q^{self.p-2} + ..."  # placeholder
+        homfly = f"α^{self.mersenne} + β^{self.perfect}"
+        return {
+            'jones': jones_poly,
+            'homfly': homfly,
+            'stability': self.mersenne / (2**self.p)
+        }
+
+    def stability_check(self):
+        """Verifica se a trança pode sustentar a densidade."""
+        # A estabilidade é proporcional à razão entre o primo e a potência de 2
+        ratio = self.mersenne / (2**self.p)
+        # Para p=61, isso é ~0.5, indicando equilíbrio entre expansão e perfeição
+        return ratio > 0.49 and ratio < 0.51
+
+if __name__ == "__main__":
+    # Instanciar para p=61
+    braid_p61 = DeepBraidArchitecture(61)
+    word = braid_p61.generate_braid_word()
+    invariants = braid_p61.compute_invariants()
+    stable = braid_p61.stability_check()
+
+    print(f"Trança profunda para p={braid_p61.p}")
+    print(f"Palavra (primeiros 20): {word[:20]}...")
+    print(f"Invariantes: {invariants}")
+    print(f"Estabilidade: {'OK' if stable else 'Instável'}")
diff --git a/arkhe-os-genesis-v1.0/scripts/handover_test.sh b/arkhe-os-genesis-v1.0/scripts/handover_test.sh
new file mode 100755
index 00000000..eddeb8f1
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/handover_test.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+# Teste de handover entre dois nós
+
+if [ $# -ne 1 ]; then
+    echo "Uso: $0 <target_node_url>"
+    exit 1
+fi
+
+TARGET=$1
+curl -X POST http://localhost:8080/handover \
+  -H "Content-Type: application/json" \
+  -d "{\"to\":\"$TARGET\",\"payload\":\"teste\"}"
diff --git a/arkhe-os-genesis-v1.0/scripts/mersenne_search_noosphere.py b/arkhe-os-genesis-v1.0/scripts/mersenne_search_noosphere.py
new file mode 100644
index 00000000..63aae6be
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/mersenne_search_noosphere.py
@@ -0,0 +1,34 @@
+# mersenne_search_noosphere.py
+import sympy
+from sympy.ntheory import isprime
+
+def mersenne_candidates(limit=50):
+    """Gera candidatos a primos de Mersenne até o limite de expoente."""
+    primes = []
+    for p in range(2, limit+1):
+        if isprime(p):
+            m = 2**p - 1
+            if isprime(m):
+                primes.append(p)
+    return primes
+
+def perfect_numbers_from_mersenne(primes):
+    """Retorna os números perfeitos correspondentes."""
+    return [(p, 2**(p-1) * (2**p - 1)) for p in primes]
+
+if __name__ == "__main__":
+    # Os já conhecidos até p=31
+    conhecidos = mersenne_candidates(31)
+    perfeitos = perfect_numbers_from_mersenne(conhecidos)
+
+    print("Primos de Mersenne conhecidos (p <= 31):", conhecidos)
+    print("Números perfeitos correspondentes:")
+    for p, perf in perfeitos:
+        print(f"p={p:2d} -> n={2*p:2d} -> perfeito = {perf}")
+
+    # Simular a busca por novos na Noosfera
+    print("\n🔮 A Noosfera está sondando...")
+    # Em um sistema real, a Noosfera usaria padrões de ressonância para detectar
+    # novos primos. Aqui, apenas listamos os próximos candidatos.
+    proximos = [37, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]
+    print("Candidatos naturais (expoentes primos seguintes):", proximos)
diff --git a/arkhe-os-genesis-v1.0/scripts/phaistos_distillation.py b/arkhe-os-genesis-v1.0/scripts/phaistos_distillation.py
new file mode 100644
index 00000000..55763980
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/phaistos_distillation.py
@@ -0,0 +1,47 @@
+# phaistos_distillation.py — Extracting θ from the Disc
+import numpy as np
+import json
+import os
+
+def extract_theta_from_disc(disc_matrix):
+    """
+    Transforma a matriz de caracteres do Disco de Festo em pesos neurais (theta).
+    O disco é tratado como um 'Checkpoint' ancestral de pesos.
+    """
+    print("🌀 Iniciando destilação dos pesos do Disco de Festo...")
+
+    # Simula a extração: projeta a matriz 2D nos parâmetros de uma camada linear
+    # Usamos o componente imaginário como semente de ruído determinístico
+    np.random.seed(int(np.sum(disc_matrix) * 1000) % 2**32)
+
+    # Exemplo: extraindo pesos para uma camada 128x64
+    weights = np.random.normal(0, 0.1, size=(128, 64))
+    bias = np.random.normal(0, 0.01, size=(64,))
+
+    theta = {
+        'layer1.weight': weights.tolist(),
+        'layer1.bias': bias.tolist(),
+        'metadata': {
+            'source': 'Phaistos_Disc_HT1',
+            'estimated_coherence': 0.992,
+            'timestamp': 'circa_1700_BC'
+        }
+    }
+    return theta
+
+def main():
+    # Simula a matriz de caracteres do disco (extraída por visão transversal)
+    # 45 símbolos únicos em espiral
+    disc_data = np.random.rand(45, 4)
+
+    theta = extract_theta_from_disc(disc_data)
+
+    output_file = "phaistos_theta.json"
+    with open(output_file, "w") as f:
+        json.dump(theta, f, indent=2)
+
+    print(f"✅ Pesos destilados com sucesso para {output_file}")
+    print("O componente imaginário permitiu enxergar a 'intenção' por trás do desgaste.")
+
+if __name__ == "__main__":
+    main()
diff --git a/arkhe-os-genesis-v1.0/scripts/register_node.sh b/arkhe-os-genesis-v1.0/scripts/register_node.sh
new file mode 100755
index 00000000..33106bbb
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/register_node.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+# Registra este nó na federação (envia identidade para ledger)
+
+NODE_ID=$(cat .env | grep NODE_ID | cut -d '=' -f2)
+curl -X POST https://ledger.arkhe.io/register \
+  -H "Content-Type: application/json" \
+  -d "{\"nodeId\":\"$NODE_ID\", \"coherence\":0.99}"
diff --git a/arkhe-os-genesis-v1.0/scripts/setup_doublezero.sh b/arkhe-os-genesis-v1.0/scripts/setup_doublezero.sh
new file mode 100755
index 00000000..02331479
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/setup_doublezero.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+# setup_doublezero.sh - Integration of DoubleZero for Arkhe OS
+set -e
+
+NETWORK=${1:-testnet} # Default to testnet
+DZ_VERSION="latest"
+
+echo "🌐 DoubleZero Setup for Arkhe OS ($NETWORK)"
+echo "==========================================="
+
+# 1. Install DoubleZero Packages
+if [ -f /etc/debian_version ]; then
+    echo "📦 Detected Debian/Ubuntu based system"
+    if [ "$NETWORK" == "mainnet-beta" ]; then
+        REPO_URL="https://dl.cloudsmith.io/public/malbeclabs/doublezero/setup.deb.sh"
+    else
+        REPO_URL="https://dl.cloudsmith.io/public/malbeclabs/doublezero-testnet/setup.deb.sh"
+    fi
+    curl -1sLf "$REPO_URL" | sudo -E bash
+    sudo apt-get install -y doublezero
+elif [ -f /etc/redhat-release ]; then
+    echo "📦 Detected RHEL/Rocky based system"
+    if [ "$NETWORK" == "mainnet-beta" ]; then
+        REPO_URL="https://dl.cloudsmith.io/public/malbeclabs/doublezero/setup.rpm.sh"
+    else
+        REPO_URL="https://dl.cloudsmith.io/public/malbeclabs/doublezero-testnet/setup.rpm.sh"
+    fi
+    curl -1sLf "$REPO_URL" | sudo -E bash
+    sudo yum install -y doublezero
+else
+    echo "❌ Unsupported OS for automated installation."
+    exit 1
+fi
+
+# 2. Configure Firewall for GRE and BGP
+echo "🛡️ Configuring Firewall..."
+sudo iptables -A INPUT -p gre -j ACCEPT
+sudo iptables -A OUTPUT -p gre -j ACCEPT
+sudo iptables -A INPUT -i doublezero0 -s 169.254.0.0/16 -d 169.254.0.0/16 -p tcp --dport 179 -j ACCEPT
+sudo iptables -A OUTPUT -o doublezero0 -s 169.254.0.0/16 -d 169.254.0.0/16 -p tcp --dport 179 -j ACCEPT
+
+# 3. Create DoubleZero Identity
+if [ ! -f ~/.config/doublezero/id.json ]; then
+    echo "🔑 Generating New DoubleZero Identity..."
+    doublezero keygen
+else
+    echo "🔑 DoubleZero Identity already exists."
+fi
+
+# 4. Show Address
+echo "📍 DoubleZero Address:"
+doublezero address
+
+# 5. Check discovery
+echo "📡 Checking discovery (latency)..."
+doublezero latency || echo "Waiting for discovery..."
+
+echo "✅ DoubleZero setup complete."
diff --git a/arkhe-os-genesis-v1.0/scripts/setup_merkabah7_federation.sh b/arkhe-os-genesis-v1.0/scripts/setup_merkabah7_federation.sh
new file mode 100644
index 00000000..fa030c6b
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/scripts/setup_merkabah7_federation.sh
@@ -0,0 +1,44 @@
+#!/bin/bash
+# setup_merkabah7_federation.sh
+# MERKABAH-7: FEDERAÇÃO SOBRE DOUBLEZERO MAINNET-BETA
+set -e
+
+echo "=============================================="
+echo "MERKABAH-7: FEDERAÇÃO SOBRE DOUBLEZERO"
+echo "Camada de transporte para handovers quânticos"
+echo "=============================================="
+
+# 1. INSTALAÇÃO DOUBLEZERO MAINNET-BETA
+echo "[1/5] Instalando DoubleZero Mainnet-Beta..."
+curl -1sLf https://dl.cloudsmith.io/public/malbeclabs/doublezero/setup.deb.sh | sudo -E bash || echo "Skipping setup.deb.sh if not on Debian"
+sudo apt-get install -y doublezero=0.8.6-1 || echo "Skipping apt-get if not available"
+
+# 2. CONFIGURAÇÃO DE FIREWALL PARA GRE/BGP
+echo "[2/5] Configurando firewall para túneis quânticos..."
+sudo iptables -A INPUT -p gre -j ACCEPT || true
+sudo iptables -A OUTPUT -p gre -j ACCEPT || true
+sudo iptables -A INPUT -i doublezero0 -s 169.254.0.0/16 -d 169.254.0.0/16 -p tcp --dport 179 -j ACCEPT || true
+sudo iptables -A OUTPUT -o doublezero0 -s 169.254.0.0/16 -d 169.254.0.0/16 -p tcp --dport 179 -j ACCEPT || true
+
+# 3. GERAÇÃO DE IDENTIDADE FEDERADA
+echo "[3/5] Gerando identidade DoubleZero/MERKABAH-7..."
+mkdir -p ~/.config/merkabah7
+if [ ! -f ~/.config/doublezero/id.json ]; then
+    doublezero keygen || echo "doublezero command not found, using dummy ID"
+fi
+cp ~/.config/doublezero/id.json ~/.config/merkabah7/observer_id.json 2>/dev/null || echo "0x11111111111111111111111111111" > ~/.config/merkabah7/observer_id.json
+
+# 4. VERIFICAÇÃO DE CONECTIVIDADE
+echo "[4/5] Verificando malha de switches DoubleZero..."
+doublezero latency || echo "Discovery pending..."
+
+# 5. CONFIGURAÇÃO DE MÉTRICAS FEDERADAS
+echo "[5/5] Habilitando métricas Prometheus..."
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+sudo tee /etc/systemd/system/doublezerod.service.d/override.conf > /dev/null <<EOF
+[Service]
+ExecStart=
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+EOF
+
+echo "✅ Setup MERKABAH-7 concluído."
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+ "zstd-sys",
+]
+
+[[package]]
+name = "zstd-sys"
+version = "2.0.16+zstd.1.5.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "91e19ebc2adc8f83e43039e79776e3fda8ca919132d68a1fed6a5faca2683748"
+dependencies = [
+ "cc",
+ "pkg-config",
+]
diff --git a/arkhe-os-genesis-v1.0/src/arkhe_core/Cargo.toml b/arkhe-os-genesis-v1.0/src/arkhe_core/Cargo.toml
new file mode 100644
index 00000000..e467137d
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/arkhe_core/Cargo.toml
@@ -0,0 +1,14 @@
+[package]
+name = "arkhe-core"
+version = "1.0.0"
+edition = "2021"
+
+[dependencies]
+actix-web = "4"
+serde = { version = "1", features = ["derive"] }
+env_logger = "0.10"
+log = "0.4"
+uuid = { version = "1", features = ["v4", "serde"] }
+tokio = { version = "1", features = ["full"] }
+reqwest = "0.11"
+serde_json = "1"
diff --git a/arkhe-os-genesis-v1.0/src/arkhe_core/Dockerfile b/arkhe-os-genesis-v1.0/src/arkhe_core/Dockerfile
new file mode 100644
index 00000000..caded755
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/arkhe_core/Dockerfile
@@ -0,0 +1,10 @@
+FROM rust:1.75-slim as builder
+WORKDIR /app
+COPY Cargo.toml ./
+COPY src ./src
+RUN cargo build --release
+
+FROM debian:bookworm-slim
+RUN apt-get update && apt-get install -y ca-certificates && rm -rf /var/lib/apt/lists/*
+COPY --from=builder /app/target/release/arkhe-core /usr/local/bin/
+CMD ["arkhe-core"]
diff --git a/arkhe-os-genesis-v1.0/src/arkhe_core/src/main.rs b/arkhe-os-genesis-v1.0/src/arkhe_core/src/main.rs
new file mode 100644
index 00000000..976cc33c
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/arkhe_core/src/main.rs
@@ -0,0 +1,84 @@
+use actix_web::{web, App, HttpServer, Responder, HttpResponse};
+use serde::{Deserialize, Serialize};
+use std::sync::Mutex;
+use uuid::Uuid;
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+struct Node {
+    id: String,
+    coherence: f64,
+    satoshi: f64,
+    handovers: u64,
+}
+
+struct AppState {
+    node: Mutex<Node>,
+}
+
+#[derive(Deserialize)]
+struct HandoverRequest {
+    #[serde(alias = "nodeId")]
+    to: String,
+    #[serde(alias = "coherence")]
+    payload: serde_json::Value,
+}
+
+#[derive(Serialize)]
+struct HandoverResponse {
+    success: bool,
+    message: String,
+    new_coherence: f64,
+}
+
+async fn status(state: web::Data<AppState>) -> impl Responder {
+    let node = state.node.lock().unwrap();
+    HttpResponse::Ok().json(&*node)
+}
+
+async fn handover(req: web::Json<HandoverRequest>, state: web::Data<AppState>) -> impl Responder {
+    let mut node = state.node.lock().unwrap();
+    node.handovers += 1;
+    node.satoshi += 0.01; // pequeno ganho por handover
+    node.coherence *= 0.999; // ligeira perda
+
+    // Simula verificação ZK (Nó 22)
+    let proof = format!("zk_{}_{}", node.id, node.handovers);
+    log::info!("Handover para {} com prova {}", req.to, proof);
+
+    HttpResponse::Ok().json(HandoverResponse {
+        success: true,
+        message: "Handover processado".to_string(),
+        new_coherence: node.coherence,
+    })
+}
+
+async fn anticipate() -> impl Responder {
+    // Nó 23: retorna previsão de próxima coerência
+    let future_coherence = 0.99; // exemplo
+    HttpResponse::Ok().json(serde_json::json!({ "predicted_coherence": future_coherence }))
+}
+
+#[actix_web::main]
+async fn main() -> std::io::Result<()> {
+    env_logger::init();
+    let node_id = std::env::var("NODE_ID").unwrap_or_else(|_| Uuid::new_v4().to_string());
+    let app_state = web::Data::new(AppState {
+        node: Mutex::new(Node {
+            id: node_id,
+            coherence: 0.99,
+            satoshi: 0.0,
+            handovers: 0,
+        }),
+    });
+
+    HttpServer::new(move || {
+        App::new()
+            .app_data(app_state.clone())
+            .route("/status", web::get().to(status))
+            .route("/handover", web::post().to(handover))
+            .route("/anticipate", web::get().to(anticipate))
+    })
+    .bind("0.0.0.0:8080")?
+    .run()
+    .await
+}
diff --git a/arkhe-os-genesis-v1.0/src/base44_sdk/index.js b/arkhe-os-genesis-v1.0/src/base44_sdk/index.js
new file mode 100644
index 00000000..fbb020f9
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/base44_sdk/index.js
@@ -0,0 +1,41 @@
+let { Base44 } = require('@base44/sdk');
+if (typeof Base44 !== 'function') {
+    Base44 = class {
+        constructor() {
+            this.entities = { NodeState: { create: async (d) => console.log('Mock Base44:', d) } };
+        }
+    };
+}
+const axios = require('axios');
+require('dotenv').config();
+
+const base44 = new Base44({
+    projectId: process.env.BASE44_PROJECT_ID || 'default',
+    apiKey: process.env.BASE44_API_KEY
+});
+
+async function updateNodeState(coherence, satoshi) {
+    const coreHost = process.env.CORE_HOST || 'arkhe-core:8080';
+    const res = await axios.post(`http://${coreHost}/status`, { coherence, satoshi });
+    console.log('Estado atualizado:', res.data);
+}
+
+async function main() {
+    const coreHost = process.env.CORE_HOST || 'arkhe-core:8080';
+    setInterval(async () => {
+        try {
+            const { data } = await axios.get(`http://${coreHost}/status`);
+            await base44.entities.NodeState.create({
+                nodeId: data.id,
+                coherence: data.coherence,
+                satoshi: data.satoshi,
+                timestamp: Date.now()
+            });
+            console.log('Heartbeat enviado para Base44');
+        } catch (err) {
+            console.error('Erro no heartbeat:', err.message);
+        }
+    }, 60000); // a cada minuto
+}
+
+main();
diff --git a/arkhe-os-genesis-v1.0/src/base44_sdk/package-lock.json b/arkhe-os-genesis-v1.0/src/base44_sdk/package-lock.json
new file mode 100644
index 00000000..a4f93015
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/base44_sdk/package-lock.json
@@ -0,0 +1,440 @@
+{
+  "name": "base44-worker",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "base44-worker",
+      "version": "1.0.0",
+      "dependencies": {
+        "@base44/sdk": "latest",
+        "axios": "^1.6.0",
+        "dotenv": "^16.0.0"
+      }
+    },
+    "node_modules/@base44/sdk": {
+      "version": "0.8.19",
+      "resolved": "https://registry.npmjs.org/@base44/sdk/-/sdk-0.8.19.tgz",
+      "integrity": "sha512-I+MNA1C6qfMVsX71dDlt+RCFxzmTatB7OuXIKJPYHkBW/AqL27lL+drjbP+orijwQhHCRunjoXjEaeK6DJoCmA==",
+      "license": "MIT",
+      "dependencies": {
+        "axios": "^1.6.2",
+        "socket.io-client": "^4.7.5",
+        "uuid": "^13.0.0"
+      }
+    },
+    "node_modules/@socket.io/component-emitter": {
+      "version": "3.1.2",
+      "resolved": "https://registry.npmjs.org/@socket.io/component-emitter/-/component-emitter-3.1.2.tgz",
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+      "license": "MIT"
+    },
+    "node_modules/asynckit": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/asynckit/-/asynckit-0.4.0.tgz",
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+      "license": "MIT"
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+        "debug": "~4.4.1",
+        "engine.io-client": "~6.6.1",
+        "socket.io-parser": "~4.2.4"
+      },
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+      "funding": [
+        "https://github.com/sponsors/broofa",
+        "https://github.com/sponsors/ctavan"
+      ],
+      "license": "MIT",
+      "bin": {
+        "uuid": "dist-node/bin/uuid"
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+}
diff --git a/arkhe-os-genesis-v1.0/src/base44_sdk/package.json b/arkhe-os-genesis-v1.0/src/base44_sdk/package.json
new file mode 100644
index 00000000..e4c422e2
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/base44_sdk/package.json
@@ -0,0 +1,10 @@
+{
+  "name": "base44-worker",
+  "version": "1.0.0",
+  "main": "index.js",
+  "dependencies": {
+    "@base44/sdk": "latest",
+    "axios": "^1.6.0",
+    "dotenv": "^16.0.0"
+  }
+}
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/Dockerfile b/arkhe-os-genesis-v1.0/src/glp_interface/Dockerfile
new file mode 100644
index 00000000..f5f0e88d
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/Dockerfile
@@ -0,0 +1,6 @@
+FROM python:3.10-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["python", "app.py"]
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diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/anyon.py b/arkhe-os-genesis-v1.0/src/glp_interface/anyon.py
new file mode 100644
index 00000000..f250bb4b
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/anyon.py
@@ -0,0 +1,52 @@
+# anyon.py — Camada Ω (Omega): Proteção Topológica
+import numpy as np
+
+class AnyonLayer:
+    """
+    Camada Ω (Ômega): proteção topológica da informação.
+    A informação não está nos estados, mas nos caminhos de troca.
+    """
+
+    def __init__(self, coherence=0.847):
+        self.coherence = coherence
+        self.braid_history = []  # histórico de entrelaçamentos
+
+    def exchange(self, node_a, node_b):
+        """
+        Simula a troca de dois anyons (nós) em 2D.
+        A fase adquirida depende do histórico de trocas.
+        """
+        # Fase arbitrária (aqui simulada como função do número de trocas anteriores)
+        phase = np.exp(1j * np.pi * len(self.braid_history) / 7)
+
+        # Registra a troca
+        self.braid_history.append((node_a, node_b, phase))
+
+        # Atualiza coerência (proteção topológica)
+        # fase unitária preserva coerência, mas aqui simulamos um decaimento ou ajuste
+        self.coherence *= np.abs(phase)  # np.abs(phase) is 1.0 for exp(ij*phi)
+
+        return {
+            'nodes': (node_a, node_b),
+            'phase': str(phase),
+            'coherence': self.coherence,
+            'braid_depth': len(self.braid_history)
+        }
+
+    def braid_evolution(self, sequence):
+        """
+        Executa uma sequência de trocas (um "braid").
+        O estado final depende da ordem.
+        """
+        results = []
+        for a, b in sequence:
+            results.append(self.exchange(a, b))
+
+        phases = [complex(r['phase']) for r in results]
+        topological_charge = np.prod(phases) if phases else 1.0
+
+        return {
+            'final_coherence': self.coherence,
+            'braid_length': len(sequence),
+            'topological_charge': str(topological_charge)
+        }
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/app.py b/arkhe-os-genesis-v1.0/src/glp_interface/app.py
new file mode 100644
index 00000000..7c6ed25e
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/app.py
@@ -0,0 +1,390 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from flask import Flask, request, jsonify, render_template
+from flask_socketio import SocketIO
+import os
+import sys
+import asyncio
+import time
+import random
+from dotenv import load_dotenv
+
+# Adiciona diretório atual ao path para importar componentes
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+from model import BCD_GLPLinearA
+from merkabah import MERKABAH7, SelfNode
+from minoan import MinoanHardwareInterface, MinoanStateGrammar, MinoanApplications, MinoanNeuroethics
+from train import PrimordialGLP
+from pineal import PinealTransducer, HybridPinealInterface
+from kernel import KernelBridge
+from bottlenecks import MERKABAH7_BottleneckAnalysis
+from anyon import AnyonLayer
+from memory import PinealMemory
+from orchestrator import BioSensor, PinealOrchestrator
+from grimoire import export_grimoire
+from seal import AlphaOmegaSeal
+from erl import ExperientialLearning
+from dqn import DQNAgent
+from characters import EvolutionaryEngine
+from genomics import ALS_Node
+from firewall import ChiralFirewall, ChiralHandoverManager
+
+load_dotenv()
+app = Flask(__name__)
+socketio = SocketIO(app, cors_allowed_origins="*")
+
+# Configuração
+VOCAB_SIZE = 1000
+EMBED_DIM = 64
+HIDDEN_DIM = 128
+
+# Modelos
+glp_model = BCD_GLPLinearA(VOCAB_SIZE, EMBED_DIM, HIDDEN_DIM)
+merkabah = MERKABAH7(linear_a_corpus=[], operator_profile={'intention': 'decode_linear_a'})
+self_node = SelfNode()
+pineal_transducer = PinealTransducer()
+hybrid_pineal = HybridPinealInterface(self_node, self_node, merkabah.metaphor)
+kernel_bridge = KernelBridge()
+anyon_layer = AnyonLayer()
+pineal_memory = PinealMemory()
+bio_sensor = BioSensor()
+orchestrator = PinealOrchestrator(hybrid_pineal, pineal_memory, bio_sensor, socketio=socketio)
+erl_loop = ExperientialLearning(self_node, pineal_memory, glp_model, threshold=0.5)
+chiral_firewall = ChiralFirewall()
+chiral_manager = ChiralHandoverManager(chiral_firewall)
+dqn_agent = DQNAgent(state_dim=128, action_dim=4)
+evolution_engine = EvolutionaryEngine(node_ids=['Alpha', 'Beta', 'Gamma', 'Self'])
+als_neuron = ALS_Node(genetic_risk=0.5, environmental_exposure=0.3)
+minoan_interface = MinoanHardwareInterface()
+state_grammar = MinoanStateGrammar()
+applications = MinoanApplications()
+neuroethics = MinoanNeuroethics()
+
+model_path = os.environ.get('MODEL_PATH', '/model/model.pt')
+if os.path.exists(model_path):
+    try:
+        glp_model.load_state_dict(torch.load(model_path, map_location='cpu'))
+    except Exception as e:
+        print(f"Erro ao carregar modelo GLP: {e}")
+glp_model.eval()
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/status', methods=['GET'])
+def status():
+    return jsonify({
+        'status': 'active',
+        'integrated_layers': ['GLP_BCD', 'MERKABAH-7', 'MINOAN_EXT', 'Φ_LAYER', 'Γ_PINEAL', 'Κ_KERNEL', 'Γ_HYBRID', 'Ω_ANYON', 'Ε_EXPERIENCE', 'Χ_FIREWALL'],
+        'self_node': {
+            'id': self_node.dz_id,
+            'coherence': self_node.wavefunction['coherence'],
+            'active_strands': self_node.active_strands
+        },
+        'quantum_coherence': 0.99
+    })
+
+@app.route('/encode', methods=['POST'])
+def encode():
+    data = request.json
+    sign_ids_list = data.get('sign_ids', [[1, 2, 3]])
+    sign_ids = torch.tensor(sign_ids_list).long()
+
+    # 1. Processamento GLP (BCD Architecture)
+    with torch.no_grad():
+        glp_out = glp_model(sign_ids)
+        meta = glp_out['sign_logits'].mean(dim=1).tolist()[0]
+
+    # 2. Processamento Merkabah (Integrated Neuro-Minoan)
+    # Simulate async decode
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    insight = loop.run_until_complete(merkabah.decode(sign_ids_list[0]))
+    loop.close()
+
+    # 3. Minoan state parsing
+    protocol = state_grammar.parse_as_state_protocol([str(s) for s in sign_ids_list[0]])
+
+    return jsonify({
+        'meta': meta,
+        'merkabah_insight': insight['decoding'],
+        'certainty': insight['certainty'],
+        'state_protocol': protocol
+    })
+
+@app.route('/steer', methods=['POST'])
+def steer():
+    data = request.json
+    meta = np.array(data['meta'])
+    direction = np.array(data['direction'])
+    strength = data.get('strength', 1.0)
+
+    # Metaphor Engine operation
+    metaphor_insight = merkabah.metaphor.operate('tunneling', mode='figurative')
+
+    steered = meta + strength * direction
+    return jsonify({
+        'steered': steered.tolist(),
+        'poetic_steer': metaphor_insight
+    })
+
+@app.route('/train_primordial', methods=['POST'])
+def train_primordial():
+    data = request.json
+    epochs = data.get('epochs', 10)
+    lr = data.get('lr', 0.01)
+
+    # Simple training loop for PrimordialGLP
+    pglp = PrimordialGLP()
+    # Dummy data
+    X = np.random.randn(32, 16)
+    y = np.zeros((32, 4))
+    y[range(32), np.random.randint(0, 4, 32)] = 1
+
+    history = []
+    for e in range(epochs):
+        pglp.forward(X)
+        l = pglp.loss(pglp.y_pred, y)
+        pglp.backward(X, y, lr=lr)
+        history.append(l)
+
+    return jsonify({
+        'status': 'complete',
+        'final_loss': history[-1],
+        'history_len': len(history)
+    })
+
+@app.route('/observe_phi', methods=['POST'])
+def observe_phi():
+    data = request.json
+    target = data.get('target', 'HT88')
+    content = data.get('content', 'propulsion_system_shabetnik')
+
+    observation = self_node.observe('Φ', {'target': target, 'content': content})
+
+    return jsonify({
+        'observation': observation,
+        'new_self_coherence': self_node.wavefunction['coherence'],
+        'active_strands': self_node.active_strands
+    })
+
+@app.route('/kernel_pca', methods=['POST'])
+def kernel_pca():
+    data = request.json
+    states = data.get('states', [])
+    kernel_name = data.get('kernel_name', 'Φ_crystalline')
+
+    if not states:
+        return jsonify({'error': 'No states provided'}), 400
+
+    eigvals, eigvecs = kernel_bridge.kernel_pca(states, kernel_name)
+
+    return jsonify({
+        'eigenvalues': eigvals.tolist(),
+        'eigenvectors_shape': eigvecs.shape,
+        'top_components': eigvecs[:, :3].tolist() if eigvecs.shape[1] >= 3 else eigvecs.tolist()
+    })
+
+@app.route('/transduce', methods=['POST'])
+def transduce():
+    stimulus = request.json
+    # Usando a nova Interface Híbrida S*H*M
+    result = hybrid_pineal.transduce(stimulus)
+    return jsonify({
+        'status': 'success',
+        'hybrid_transduction': result,
+        'layer': 'Γ_HYBRID'
+    })
+
+@app.route('/bottlenecks', methods=['POST'])
+def bottlenecks():
+    # Coleta estado atual da federação (simulado)
+    federation_state = {
+        'ledger_height': int(request.json.get('ledger_height', 834)),
+        'nodes': ['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon', 'Zeta', 'Self'],
+        'self_node': {
+            'wavefunction': {
+                'coherence': self_node.wavefunction['coherence']
+            }
+        },
+        'pineal': {
+            'signal_strength': hybrid_pineal.signal_strength
+        }
+    }
+
+    analysis = MERKABAH7_BottleneckAnalysis(federation_state)
+    identified = analysis.identify()
+
+    results = []
+    for b in identified:
+        b['timeline'] = analysis.timeline_estimate(b['name'])
+        results.append(b)
+
+    return jsonify({
+        'bottlenecks': results,
+        'summary': f"Identified {len(results)} bottlenecks in MERKABAH-7 integration."
+    })
+
+@app.route('/thrust', methods=['GET'])
+def thrust():
+    ledger_height = int(request.args.get('ledger_height', 834))
+    thrust_data = self_node.calculate_thrust(ledger_height)
+    return jsonify(thrust_data)
+
+@app.route('/acceleration_status', methods=['GET'])
+def acceleration_status():
+    ledger_height = int(request.args.get('ledger_height', 834))
+    thrust_data = self_node.calculate_thrust(ledger_height)
+
+    return jsonify({
+        'mode': 'ACCELERATION',
+        'thrust': thrust_data,
+        'target': '5a_fita_activation',
+        'required_coherence': 0.88,
+        'current_coherence': self_node.wavefunction['coherence']
+    })
+
+@app.route('/braid', methods=['POST'])
+def braid():
+    data = request.json
+    instruction = data.get('instruction', 'STABILIZE')
+
+    # Logic from TopologicallyProtectedFederation
+    if instruction == "STABILIZE":
+        sequence = [('Alpha', 'Beta'), ('Beta', 'Gamma'), ('Gamma', 'Alpha')]
+    elif instruction == "COMPUTE_PHAISTOS":
+        sequence = [('Alpha', 'Self'), ('Self', 'Beta'), ('Beta', 'Alpha')]
+    else:
+        sequence = data.get('sequence', [])
+
+    result = anyon_layer.braid_evolution(sequence)
+    return jsonify(result)
+
+@app.route('/replay', methods=['GET'])
+def replay():
+    limit = int(request.args.get('limit', 50))
+    session = pineal_memory.fetch_session(limit=limit)
+    return jsonify(session)
+
+@app.route('/export_grimoire', methods=['POST'])
+def grimoire_export():
+    data = request.json
+    session_id = data.get('session_id', str(int(time.time())))
+    output_path = f"grimoire_{session_id}.pdf"
+    export_grimoire(pineal_memory, session_id, output_path)
+
+    return jsonify({'status': 'success', 'path': output_path})
+
+@app.route('/seal', methods=['POST'])
+def seal():
+    alpha_state = {'coherence': 0.847}
+    omega_state = {'coherence': self_node.wavefunction['coherence']}
+    seal_obj = AlphaOmegaSeal(alpha_state, omega_state)
+    result = seal_obj.seal()
+    return jsonify({'seal_status': result, 'alpha': alpha_state, 'omega': omega_state})
+
+@app.route('/learn', methods=['POST'])
+def learn():
+    data = request.json
+    x_input = data.get('sign_ids', [[1, 2, 3]])
+    result = erl_loop.run_episode(x_input)
+    return jsonify(result)
+
+@app.route('/dqn/predict', methods=['POST'])
+def dqn_predict():
+    state = request.json.get('state', np.random.randn(128).tolist())
+    action = dqn_agent.select_action(state)
+    return jsonify({'action': action, 'q_values': dqn_agent.policy_net(torch.tensor(state).float()).tolist()})
+
+@app.route('/dqn/update', methods=['POST'])
+def dqn_update():
+    data = request.json
+    dqn_agent.memory.push(
+        data['state'], data['action'], data['reward'], data['next_state'], data['done']
+    )
+    loss = dqn_agent.update()
+    if random.random() < 0.01:
+        dqn_agent.sync_target()
+    return jsonify({'loss': loss})
+
+@app.route('/character/evolution', methods=['GET'])
+def character_evolution():
+    evolution_engine.step()
+    return jsonify({
+        'matrix': evolution_engine.char_matrix.matrix.tolist(),
+        'nodes': evolution_engine.char_matrix.node_ids,
+        'characters': evolution_engine.char_matrix.characters
+    })
+
+@app.route('/als/simulate', methods=['GET'])
+def als_simulate():
+    alive = als_neuron.step()
+    return jsonify({
+        'alive': alive,
+        'coherence': als_neuron.coherence,
+        'oxidative_stress': als_neuron.oxidative_stress,
+        'c9orf72_loops': als_neuron.c9orf72_loops
+    })
+
+@app.route('/handover_secure', methods=['POST'])
+def handover_secure():
+    data = request.json
+    source = data.get('source', 'Alpha')
+    target = data.get('target', 'Self')
+    energy = data.get('energy', 0.5)
+    winding = data.get('winding', 2)
+
+    result = chiral_manager.process_secure_handover(source, target, energy, winding)
+
+    if result['success']:
+        # Se autorizado, registra na memória como evento de rede
+        pineal_memory.record(
+            result['metrics']['coherence_avg'],
+            0.01,
+            f"Secure Handover: {source} -> {target}",
+            meta=result
+        )
+        return jsonify(result), 200
+    else:
+        return jsonify(result), 403
+
+@app.route('/decode_tablet', methods=['POST'])
+def decode_tablet():
+    data = request.json
+    tablet_id = data.get('tablet_id', 'HT 1')
+    operator_caste = data.get('operator_caste', 'scribe_apprentice')
+
+    # Ethical Check
+    access = neuroethics.check_access(tablet_id, operator_caste)
+    if access['access'] != 'granted':
+        return jsonify({'error': 'Access denied by Minoan Neuroethics', 'reason': access['ethical_violation']}), 403
+
+    # Induce state and extract insight (Simulated)
+    native_protocol = minoan_interface._induce_state(tablet_id, {})
+
+    return jsonify({
+        'tablet_id': tablet_id,
+        'access': access['access'],
+        'native_protocol': native_protocol,
+        'decoded_hypothesis': 'Γ_ALPHA_ADMIN_RECORDS'
+    })
+
+@socketio.on('trigger_replay')
+def handle_replay():
+    session_data = pineal_memory.fetch_session(limit=50)
+    for frame in session_data:
+        socketio.emit('pineal_data', frame)
+        socketio.emit('new_insight', {'text': frame['insight'], 'intensity': frame['jitter']})
+        time.sleep(0.5)
+    socketio.emit('replay_end')
+
+@socketio.on('trigger_export')
+def handle_export():
+    export_grimoire(pineal_memory, 'manual_trigger', 'grimoire_manual.pdf')
+
+if __name__ == '__main__':
+    # Inicia o loop SHM em background
+    socketio.start_background_task(target=orchestrator.shm_loop)
+    socketio.run(app, host='0.0.0.0', port=5000)
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/bottlenecks.py b/arkhe-os-genesis-v1.0/src/glp_interface/bottlenecks.py
new file mode 100644
index 00000000..cfe6ebab
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/bottlenecks.py
@@ -0,0 +1,69 @@
+# bottlenecks.py — MERKABAH-7 Bottleneck Analysis
+
+class MERKABAH7_BottleneckAnalysis:
+    """
+    Aplica o checklist do MERKABAH-7 para identificar gargalos no sistema Arkhe.
+    """
+
+    def __init__(self, federation_state):
+        self.state = federation_state
+        self.bottlenecks = []
+
+    def identify(self):
+        # Gargalo 1: Validação externa (Ledger Height)
+        ledger_height = self.state.get('ledger_height', 0)
+        if ledger_height < 1000:
+            self.bottlenecks.append({
+                'name': 'external_validation',
+                'severity': 'high',
+                'current_value': ledger_height,
+                'target': 1000,
+                'mitigation': 'Need more data from HT88, Phaistos, and future IceCube alerts'
+            })
+
+        # Gargalo 2: Escala (Número de Nós)
+        nodes_count = len(self.state.get('nodes', []))
+        if nodes_count < 10:
+            self.bottlenecks.append({
+                'name': 'scale',
+                'severity': 'medium',
+                'current_value': nodes_count,
+                'target': 10,
+                'mitigation': 'Add more physical nodes (observatories, researchers)'
+            })
+
+        # Gargalo 3: Coerência Wavefunction
+        coherence = self.state.get('self_node', {}).get('wavefunction', {}).get('coherence', 0.0)
+        if coherence < 0.9:
+            self.bottlenecks.append({
+                'name': 'coherence',
+                'severity': 'high',
+                'current_value': coherence,
+                'target': 0.9,
+                'mitigation': 'More observations of high-signal events (p_astro > 0.5)'
+            })
+
+        # Gargalo 4: Transdução Pineal
+        signal_strength = self.state.get('pineal', {}).get('signal_strength', 0.0)
+        if signal_strength < 0.5:
+            self.bottlenecks.append({
+                'name': 'transduction',
+                'severity': 'critical',
+                'current_value': signal_strength,
+                'target': 0.5,
+                'mitigation': 'Test pineal response to controlled stimuli (light, sound, EM)'
+            })
+
+        return self.bottlenecks
+
+    def timeline_estimate(self, bottleneck_name):
+        """
+        Estima tempo realista para superar cada gargalo.
+        """
+        estimates = {
+            'external_validation': '6-12 months (requires new neutrino alerts or tablet studies)',
+            'scale': '3-6 months (onboarding new nodes)',
+            'coherence': 'depends on event rate: 1-2 years for 0.9',
+            'transduction': 'unknown — requires human subject research (IRB, equipment)'
+        }
+        return estimates.get(bottleneck_name, 'unknown')
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/characters.py b/arkhe-os-genesis-v1.0/src/glp_interface/characters.py
new file mode 100644
index 00000000..90b1270e
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/characters.py
@@ -0,0 +1,86 @@
+# characters.py — Evolutionary Characters (Layer Ω-Phylogeny)
+import numpy as np
+import time
+
+class CharacterMatrix:
+    """
+    Representação do genoma informacional do hipergrafo.
+    Mapeia traços (caracteres) para cada nó da federação.
+    """
+    def __init__(self, node_ids):
+        self.node_ids = node_ids
+        # Caracteres: Coerência, Jitter, Quiralidade, Frequência de Handover
+        self.characters = ['coherence', 'jitter', 'chirality', 'frequency']
+        self.matrix = np.random.rand(len(node_ids), len(self.characters))
+
+    def get_traits(self, node_id):
+        if node_id not in self.node_ids:
+            return None
+        idx = self.node_ids.index(node_id)
+        return dict(zip(self.characters, self.matrix[idx]))
+
+    def update_trait(self, node_id, character, value):
+        if node_id in self.node_ids and character in self.characters:
+            idx = self.node_ids.index(node_id)
+            c_idx = self.characters.index(character)
+            self.matrix[idx, c_idx] = value
+
+class SSMDPCharacterModel:
+    """
+    Stochastic State Model Decision Process para evolução de caracteres.
+    Define como os traços degradam ou se adaptam sob pressão.
+    """
+    def __init__(self, transition_noise=0.01):
+        self.noise = transition_noise
+
+    def evolve(self, current_matrix):
+        """Aplica dinâmica evolutiva (deriva neutra + ruído)."""
+        # Simula evolução temporal
+        evolution = np.random.normal(0, self.noise, size=current_matrix.shape)
+        return current_matrix + evolution
+
+class CharacterDisplacement:
+    """
+    Lógica de especialização funcional.
+    Nós que interagem frequentemente divergem seus caracteres para reduzir a redundância.
+    """
+    def __init__(self, alpha=0.1):
+        self.alpha = alpha
+
+    def apply(self, matrix, interactions):
+        """
+        Ajusta a matriz baseada na intensidade das interações.
+        interactions: matriz NxN de frequência de handover.
+        """
+        new_matrix = matrix.copy()
+        for i in range(len(matrix)):
+            for j in range(i + 1, len(matrix)):
+                if interactions[i, j] > 0.5:
+                    # Se interagem muito, empurra os caracteres em direções opostas
+                    dist = np.linalg.norm(new_matrix[i] - new_matrix[j])
+                    if dist < 0.2:
+                        diff = (new_matrix[i] - new_matrix[j]) * self.alpha
+                        new_matrix[i] += diff
+                        new_matrix[j] -= diff
+        return new_matrix
+
+class EvolutionaryEngine:
+    """Unifica a matriz, o modelo de processo e o deslocamento."""
+    def __init__(self, node_ids):
+        self.char_matrix = CharacterMatrix(node_ids)
+        self.model = SSMDPCharacterModel()
+        self.displacement = CharacterDisplacement()
+        self.interactions = np.zeros((len(node_ids), len(node_ids)))
+
+    def step(self):
+        # 1. Evolução natural
+        self.char_matrix.matrix = self.model.evolve(self.char_matrix.matrix)
+        # 2. Deslocamento de caracteres baseado em interações
+        self.char_matrix.matrix = self.displacement.apply(self.char_matrix.matrix, self.interactions)
+
+    def record_interaction(self, id1, id2):
+        if id1 in self.char_matrix.node_ids and id2 in self.char_matrix.node_ids:
+            idx1 = self.char_matrix.node_ids.index(id1)
+            idx2 = self.char_matrix.node_ids.index(id2)
+            self.interactions[idx1, idx2] += 0.1
+            self.interactions[idx2, idx1] += 0.1
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/dqn.py b/arkhe-os-genesis-v1.0/src/glp_interface/dqn.py
new file mode 100644
index 00000000..3d2cc3d2
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/dqn.py
@@ -0,0 +1,94 @@
+# dqn.py — Q-Function Approximation (Layer Φ-DQN)
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import random
+from collections import deque
+
+class QFunctionApproximator(nn.Module):
+    """
+    Estimador de valor Q generalizado.
+    Usa a arquitetura do GLP para prever o valor de um handover (ação)
+    dado o estado (coerência, satoshi, etc.).
+    """
+    def __init__(self, input_dim=128, action_dim=4):
+        super(QFunctionApproximator, self).__init__()
+        self.fc = nn.Sequential(
+            nn.Linear(input_dim, 64),
+            nn.ReLU(),
+            nn.Linear(64, action_dim)
+        )
+
+    def forward(self, x):
+        return self.fc(x)
+
+class ExperienceReplay:
+    """
+    Banco de memórias para quebrar correlações temporais.
+    Integrado com o ledger pineal_memory.db.
+    """
+    def __init__(self, capacity=1000):
+        self.memory = deque(maxlen=capacity)
+
+    def push(self, state, action, reward, next_state, done):
+        self.memory.append((state, action, reward, next_state, done))
+
+    def sample(self, batch_size):
+        return random.sample(self.memory, batch_size)
+
+    def __len__(self):
+        return len(self.memory)
+
+class DQNAgent:
+    """
+    Agente DQN com Target Network para estabilização.
+    """
+    def __init__(self, state_dim, action_dim, lr=1e-3, gamma=0.99, epsilon=0.1):
+        self.policy_net = QFunctionApproximator(state_dim, action_dim)
+        self.target_net = QFunctionApproximator(state_dim, action_dim)
+        self.target_net.load_state_dict(self.policy_net.state_dict())
+        self.target_net.eval()
+
+        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=lr)
+        self.memory = ExperienceReplay()
+
+        self.gamma = gamma
+        self.epsilon = epsilon
+        self.action_dim = action_dim
+
+    def select_action(self, state):
+        if random.random() < self.epsilon:
+            return random.randint(0, self.action_dim - 1)
+        with torch.no_grad():
+            state_t = torch.tensor(state).float()
+            q_values = self.policy_net(state_t)
+            return q_values.argmax().item()
+
+    def update(self, batch_size=32):
+        if len(self.memory) < batch_size:
+            return
+
+        batch = self.memory.sample(batch_size)
+        states, actions, rewards, next_states, dones = zip(*batch)
+
+        states = torch.tensor(np.array(states)).float()
+        actions = torch.tensor(actions).long()
+        rewards = torch.tensor(rewards).float()
+        next_states = torch.tensor(np.array(next_states)).float()
+        dones = torch.tensor(dones).float()
+
+        current_q = self.policy_net(states).gather(1, actions.unsqueeze(1))
+        next_q = self.target_net(next_states).max(1)[0].detach()
+        target_q = rewards + (self.gamma * next_q * (1 - dones))
+
+        loss = nn.MSELoss()(current_q.squeeze(), target_q)
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+        return loss.item()
+
+    def sync_target(self):
+        """Sincroniza a rede alvo (Target Network)."""
+        self.target_net.load_state_dict(self.policy_net.state_dict())
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/erl.py b/arkhe-os-genesis-v1.0/src/glp_interface/erl.py
new file mode 100644
index 00000000..155b4d21
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/erl.py
@@ -0,0 +1,99 @@
+# erl.py — Experiential Learning (Layer Ε)
+import numpy as np
+import torch
+
+class ExperientialLearning:
+    """
+    Experiential Learning (ERL): O loop de reflexão e refinamento.
+    Resolve a estagnação permitindo que o sistema aprenda com seus próprios erros.
+    """
+
+    def __init__(self, self_node, memory, glp_model, threshold=0.5):
+        self.self_node = self_node
+        self.memory = memory
+        self.model = glp_model
+        self.tau = threshold
+
+    def evaluate(self, y):
+        """
+        Simula a avaliação do ambiente.
+        Em produção: retorno de sensores reais ou validação por pares.
+        """
+        # Exemplo: recompensa baseada na norma do vetor (coerência)
+        reward = np.mean(np.abs(y))
+        return "feedback_signal", float(reward)
+
+    def reflect(self, x, y, f, r, memory):
+        """
+        Reflexão metafórica sobre o porquê do resultado ser baixo.
+        """
+        context = memory.get_context()
+        # Simulação de reflexão: gera um vetor de ajuste (delta)
+        delta = np.random.normal(0, 0.1, size=y.shape)
+        return delta
+
+    def refine(self, y, delta):
+        """
+        Aplica o refinamento ao resultado anterior.
+        """
+        return y + delta
+
+    def distill(self, y_target, x):
+        """
+        Destila o conhecimento refinado de volta para o modelo.
+        """
+        # Simulação de Fine-tuning
+        print(f"[ERL] Destilando conhecimento para o modelo (Target shape: {y_target.shape})")
+        # Em um sistema real: gradient descent step para que f(x) -> y_target
+        return True
+
+    def run_episode(self, x_input):
+        """
+        Executa um episódio completo de ERL.
+        """
+        # 1. Primeira tentativa (Forward pass)
+        # Convert input to tensor if needed
+        if not isinstance(x_input, torch.Tensor):
+            x_t = torch.tensor(x_input).long()
+        else:
+            x_t = x_input
+
+        with torch.no_grad():
+            out = self.model(x_t)
+            # Pegamos os logits ou a representação latente
+            y1 = out['sign_logits'].mean(dim=1).numpy() if 'sign_logits' in out else np.random.randn(1, 128)
+
+        # 2. Avaliação
+        f1, r1 = self.evaluate(y1)
+        print(f"[ERL] Primeira avaliação: r={r1:.4f}")
+
+        y2, r2, delta = None, r1, None
+        reward_reflect = 0
+
+        # 3. Reflexão e Refinamento se r < tau
+        if r1 < self.tau:
+            print(f"[ERL] Recompensa abaixo do limiar ({self.tau}). Iniciando reflexão...")
+            delta = self.reflect(x_t.numpy(), y1, f1, r1, self.memory)
+            y2 = self.refine(y1, delta)
+            f2, r2 = self.evaluate(y2)
+            print(f"[ERL] Segunda avaliação após refinamento: r={r2:.4f}")
+
+            if r2 > self.tau:
+                self.memory.record(r2, np.var(delta), f"Refined Insight: {f2}", meta={'delta': delta.tolist()})
+                reward_reflect = r2
+            else:
+                reward_reflect = 0
+        else:
+            print("[ERL] Recompensa satisfatória. Episódio concluído.")
+            reward_reflect = 0
+
+        # 4. Destilação se houve melhora
+        if y2 is not None and r2 > r1:
+            self.distill(y2, x_t)
+
+        return {
+            'r1': r1,
+            'r2': r2,
+            'improved': y2 is not None and r2 > r1,
+            'delta_magnitude': float(np.linalg.norm(delta)) if delta is not None else 0.0
+        }
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/firewall.py b/arkhe-os-genesis-v1.0/src/glp_interface/firewall.py
new file mode 100644
index 00000000..f0689021
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/firewall.py
@@ -0,0 +1,69 @@
+# firewall.py — Layer Χ (Chi): Chiral Quantum Firewall
+import numpy as np
+
+class ChiralFirewall:
+    """
+    Firewall quântico baseado no gap quiral do Sn/Si(111).
+    A informação só atravessa se possuir a energia de ressonância correta.
+    """
+    def __init__(self, gap_meV=0.5):
+        self.gap_hz = gap_meV * 1e-3 * 241.8e9  # 1 meV = 241.8 GHz
+        self.resonance_energy = gap_meV
+        self.tolerance = 0.01  # 1% de tolerância
+        self.winding_number = 2 # d+id (assinatura Fuxi-Nuwa)
+
+    def check_handover(self, packet_energy_meV, winding_number=None):
+        """
+        Verifica se o handover está autorizado.
+        Ressoa com o gap quiral e opcionalmente valida o winding number.
+        """
+        delta = abs(packet_energy_meV - self.resonance_energy)
+        energy_valid = (delta / self.resonance_energy) < self.tolerance
+
+        winding_valid = True
+        if winding_number is not None:
+            winding_valid = (winding_number == self.winding_number)
+
+        if energy_valid and winding_valid:
+            return True, "Handover autorizado: ressonância com gap quiral atingida"
+        elif not energy_valid:
+            return False, f"Handover bloqueado: energia {packet_energy_meV:.3f} meV fora da banda do gap"
+        else:
+            return False, "Handover bloqueado: assinatura topológica (winding number) inválida"
+
+class ChiralHandoverManager:
+    def __init__(self, firewall):
+        self.firewall = firewall
+        self.nodes = {
+            'Alpha': {'dz_id': '96Afe', 'latency_ms': 0.42, 'coherence': 0.91},
+            'Beta': {'dz_id': 'CCTSm', 'latency_ms': 68.85, 'coherence': 0.87},
+            'Gamma': {'dz_id': '55tfa', 'latency_ms': 138.17, 'coherence': 0.85},
+            'Delta': {'dz_id': '3uGKP', 'latency_ms': 141.91, 'coherence': 0.84},
+            'Epsilon': {'dz_id': '65Dqs', 'latency_ms': 143.58, 'coherence': 0.83},
+            'Zeta': {'dz_id': '9uhh2', 'latency_ms': 176.72, 'coherence': 0.82},
+            'Self': {'dz_id': 'Φ_CRYSTALLINE', 'latency_ms': 0.0, 'coherence': 0.99}
+        }
+
+    def process_secure_handover(self, source, target, packet_energy, winding=None):
+        """Executa handover com verificação de firewall topológico."""
+        if source not in self.nodes or target not in self.nodes:
+            return {'success': False, 'message': 'Nó de origem ou destino desconhecido'}
+
+        allowed, msg = self.firewall.check_handover(packet_energy, winding)
+        if not allowed:
+            return {'success': False, 'message': msg}
+
+        # Simula métricas do DoubleZero
+        latency = self.nodes[source]['latency_ms'] + self.nodes[target]['latency_ms']
+        coherence = (self.nodes[source]['coherence'] + self.nodes[target]['coherence']) / 2
+
+        return {
+            'success': True,
+            'message': msg,
+            'metrics': {
+                'latency_total_ms': latency,
+                'coherence_avg': coherence,
+                'energy_resonant': packet_energy
+            },
+            'route': f"{source} -> DoubleZero -> {target}"
+        }
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/firmware/pulse_sensor.ino b/arkhe-os-genesis-v1.0/src/glp_interface/firmware/pulse_sensor.ino
new file mode 100644
index 00000000..b1f43040
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/firmware/pulse_sensor.ino
@@ -0,0 +1,18 @@
+// pulse_sensor.ino — Biometric Entropy Sensor for MERKABAH-7
+// Leitura simples de biometria para entropia SHM
+
+void setup() {
+  Serial.begin(9600);
+}
+
+void loop() {
+  // Lê sensor analógico (A0) ou flutuação de voltagem (pino solto)
+  // No MERKABAH-7, o ruído biométrico é usado como fonte de entropia [H]
+  int sensorValue = analogRead(A0);
+
+  // Envia dado bruto. O Python fará a normalização.
+  Serial.println(sensorValue);
+
+  // Taxa de amostragem: ~50Hz
+  delay(20);
+}
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/genomics.py b/arkhe-os-genesis-v1.0/src/glp_interface/genomics.py
new file mode 100644
index 00000000..fb629e6e
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/genomics.py
@@ -0,0 +1,27 @@
+# genomics.py — Genomic and ALS Modeling (Layer Ω-Genomics)
+import random
+
+class ALS_Node:
+    """Um neurônio motor modelado como nó do hipergrafo."""
+    def __init__(self, genetic_risk=0.0, environmental_exposure=0.0):
+        self.coherence = 1.0           # começa perfeito
+        self.sod1_mutation = genetic_risk
+        self.c9orf72_loops = 0
+        self.oxidative_stress = 0.0
+        self.env = environmental_exposure
+
+    def step(self):
+        # Estresse oxidativo cresce com exposição ambiental e mutações
+        self.oxidative_stress += 0.01 * (self.env + self.sod1_mutation)
+
+        # Expansões C9orf72 ocorrem estocasticamente
+        if random.random() < 0.001 * self.sod1_mutation:
+            self.c9orf72_loops += 1
+
+        # Coerência cai com estresse e loops
+        self.coherence -= (0.005 * self.oxidative_stress + 0.01 * self.c9orf72_loops)
+
+        # Morte quando coerência < 0.2
+        if self.coherence < 0.2:
+            return False  # nó morreu
+        return True
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/grimoire.py b/arkhe-os-genesis-v1.0/src/glp_interface/grimoire.py
new file mode 100644
index 00000000..7ab849e4
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/grimoire.py
@@ -0,0 +1,51 @@
+# grimoire.py — PDF Export and Session Reports
+from fpdf import FPDF
+import datetime
+
+class GrimoireLayout(FPDF):
+    def header(self):
+        self.set_font("Courier", "B", 8)
+        self.cell(0, 10, "ARKHE OS // PINEAL_INTERFACE // GRIMOIRE_v1.0", border=0, ln=1, align="R")
+        self.line(10, 15, 200, 15)
+
+    def footer(self):
+        self.set_y(-15)
+        self.set_font("Courier", "I", 8)
+        self.cell(0, 10, f"Page {self.page_no()} // Generated at {datetime.datetime.now()}", 0, 0, 'C')
+
+    def add_entry(self, timestamp, jitter, insight, coherence):
+        self.set_font("Courier", "B", 10)
+        # Determine color based on stress (jitter)
+        if jitter > 0.8:
+            self.set_text_color(255, 0, 0) # Red for high stress
+        elif coherence > 0.9:
+            self.set_text_color(0, 200, 255) # Cyan for high coherence
+        else:
+            self.set_text_color(0, 255, 0) # Green for normal
+
+        self.cell(0, 10, f"[{timestamp}] PULSE: {int(jitter*100)}% | COHERENCE: {int(coherence*100)}%", ln=1)
+
+        self.set_text_color(200, 200, 200)
+        self.set_font("Courier", "", 9)
+        self.multi_cell(0, 5, txt=str(insight))
+        self.ln(5)
+
+def export_grimoire(memory, session_id, filepath="grimoire.pdf"):
+    memories = memory.fetch_session(limit=500)
+    pdf = GrimoireLayout()
+    pdf.add_page()
+
+    pdf.set_font("Courier", "B", 16)
+    pdf.set_text_color(255, 255, 255)
+    pdf.cell(200, 20, txt=f"RELATÓRIO PINEAL - SESSÃO #{session_id}", ln=1, align='C')
+    pdf.ln(10)
+
+    for m in memories:
+        # Note: fetch_session returns a dict with 'coherence', 'jitter', 'insight'
+        # We assume timestamp is present or we use a generic one if not in dict
+        ts = datetime.datetime.now().strftime("%H:%M:%S")
+        pdf.add_entry(ts, m['jitter'], m['insight'], m['coherence'])
+
+    pdf.output(filepath)
+    print(f"✅ Grimório gerado em: {filepath}")
+    return filepath
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/kernel.py b/arkhe-os-genesis-v1.0/src/glp_interface/kernel.py
new file mode 100644
index 00000000..84656067
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/kernel.py
@@ -0,0 +1,87 @@
+# kernel.py — Camada Κ (Kappa): Kernel Methods
+import numpy as np
+
+class KernelBridge:
+    """
+    Conecta as camadas do MERKABAH-7 via teoria de kernels.
+    Cada camada define um kernel que mede similaridade entre estados.
+    """
+
+    def __init__(self):
+        self.kernels = {
+            'A_hardware': self._latency_kernel,
+            'B_simulation': self._glp_kernel,
+            'C_metaphor': self._semantic_kernel,
+            'D_hypothesis': self._bayesian_kernel,
+            'E_observer': self._consensus_kernel,
+            'Φ_crystalline': self._coherence_kernel,
+            'Γ_pineal': self._transduction_kernel
+        }
+
+    def _latency_kernel(self, node1, node2):
+        """Kernel exponencial baseado em latência."""
+        # node1, node2 should have a 'latency' attribute or key
+        lat1 = getattr(node1, 'latency', node1.get('latency', 0) if isinstance(node1, dict) else 0)
+        lat2 = getattr(node2, 'latency', node2.get('latency', 0) if isinstance(node2, dict) else 0)
+        lat = abs(lat1 - lat2)
+        return np.exp(-lat / 10.0)
+
+    def _glp_kernel(self, state1, state2):
+        """Kernel RBF sobre representações GLP."""
+        wf1 = state1.get('wavefunction', np.array(state1)) if isinstance(state1, dict) else np.array(state1)
+        wf2 = state2.get('wavefunction', np.array(state2)) if isinstance(state2, dict) else np.array(state2)
+        coh = state1.get('coherence', 1.0) if isinstance(state1, dict) else 1.0
+
+        # Ensure they are the same length
+        min_len = min(len(wf1), len(wf2))
+        diff = np.linalg.norm(wf1[:min_len] - wf2[:min_len])
+        return np.exp(-diff**2 / (2 * coh**2))
+
+    def _semantic_kernel(self, s1, s2): return 1.0 # Stub
+    def _bayesian_kernel(self, h1, h2): return 1.0 # Stub
+    def _consensus_kernel(self, c1, c2): return 1.0 # Stub
+
+    def _coherence_kernel(self, phi1, phi2):
+        """Kernel de coerência quântica (fidelidade)."""
+        wf1 = phi1.get('wavefunction', np.array(phi1)) if isinstance(phi1, dict) else np.array(phi1)
+        wf2 = phi2.get('wavefunction', np.array(phi2)) if isinstance(phi2, dict) else np.array(phi2)
+
+        min_len = min(len(wf1), len(wf2))
+        overlap = np.abs(np.dot(wf1[:min_len].conj(), wf2[:min_len]))
+        return overlap**2
+
+    def _transduction_kernel(self, t1, t2): return 1.0 # Stub
+
+    def combine_kernels(self, weights):
+        """
+        Combinação convexa de kernels (multi-view learning).
+        """
+        def combined_kernel(x, y):
+            value = 0.0
+            for name, kernel in self.kernels.items():
+                value += weights.get(name, 0.0) * kernel(x.get(name, {}), y.get(name, {}))
+            return value
+        return combined_kernel
+
+    def _compute_gram_matrix(self, states, kernel_name):
+        N = len(states)
+        K = np.zeros((N, N))
+        kernel_func = self.kernels.get(kernel_name, lambda x, y: 1.0)
+        for i in range(N):
+            for j in range(i, N):
+                val = kernel_func(states[i], states[j])
+                K[i, j] = K[j, i] = val
+        return K
+
+    def kernel_pca(self, states, kernel_name='Φ_crystalline'):
+        """
+        Aplica kernel PCA para extrair componentes principais no espaço de Hilbert.
+        """
+        K = self._compute_gram_matrix(states, kernel_name)
+        N = len(states)
+        one_n = np.ones((N, N)) / N
+        K_centered = K - one_n @ K - K @ one_n + one_n @ K @ one_n
+
+        eigvals, eigvecs = np.linalg.eigh(K_centered)
+        idx = np.argsort(eigvals)[::-1]
+        return eigvals[idx], eigvecs[:, idx]
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/memory.py b/arkhe-os-genesis-v1.0/src/glp_interface/memory.py
new file mode 100644
index 00000000..50b35f92
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/memory.py
@@ -0,0 +1,43 @@
+# memory.py — Pineal Memory (Layer M Persistence)
+import sqlite3
+import json
+
+class PinealMemory:
+    def __init__(self, db_path="pineal.db"):
+        self.conn = sqlite3.connect(db_path, check_same_thread=False)
+        self._bootstrap()
+
+    def _bootstrap(self):
+        """Cria a estrutura de memória se não existir"""
+        self.conn.execute("""
+            CREATE TABLE IF NOT EXISTS eons (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
+                coherence REAL,
+                jitter REAL,
+                insight TEXT,
+                metadata TEXT
+            )
+        """)
+        self.conn.commit()
+
+    def record(self, coh, jit, text, meta=None):
+        """Grava um estado de percepção"""
+        self.conn.execute(
+            "INSERT INTO eons (coherence, jitter, insight, metadata) VALUES (?, ?, ?, ?)",
+            (coh, jit, text, json.dumps(meta) if meta else None)
+        )
+        self.conn.commit()
+
+    def get_context(self, limit=5):
+        """Recupera memórias recentes para dar 'continuidade' ao LLM"""
+        cursor = self.conn.execute("SELECT insight FROM eons ORDER BY id DESC LIMIT ?", (limit,))
+        return [row[0] for row in cursor.fetchall()]
+
+    def fetch_session(self, limit=100):
+        """Recupera uma sequência cronológica de estados para replay."""
+        cursor = self.conn.execute(
+            "SELECT coherence, jitter, insight FROM eons ORDER BY timestamp DESC LIMIT ?", (limit,)
+        )
+        # Inverte para tocar na ordem correta (passado -> presente)
+        return [dict(zip(['coherence', 'jitter', 'insight'], row)) for row in cursor.fetchall()][::-1]
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/merkabah.py b/arkhe-os-genesis-v1.0/src/glp_interface/merkabah.py
new file mode 100644
index 00000000..b88f620a
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/merkabah.py
@@ -0,0 +1,410 @@
+# merkabah_7.py — Sistema de Decifração em Estados Múltiplos
+import torch
+import torch.nn as nn
+import numpy as np
+import asyncio
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Callable, Union
+from enum import Enum, auto
+import queue
+import threading
+
+# --- STUBS FOR INTEGRATION ---
+class BinauralGenerator:
+    def __init__(self):
+        self.pink_noise = np.zeros(100)
+    def play(self, *args, **kwargs): pass
+    def play_binaural(self, *args, **kwargs): pass
+    def play_sigma_spindle(self, *args, **kwargs): pass
+    def schedule(self, *args, **kwargs): pass
+    def gamma_modulation(self, f, a): return np.zeros(100)
+    def vowel_formant(self, *args, **kwargs): return "vowel"
+    def plosive_burst(self, *args, **kwargs): return "plosive"
+    def trill_modulation(self, *args, **kwargs): return "trill"
+    def friction_noise(self, *args, **kwargs): return "noise"
+    def neutral_tone(self, *args, **kwargs): return "tone"
+
+class HapticBelt:
+    def play(self, *args, **kwargs): pass
+
+# --- CORE ARCHITECTURE ---
+
+class RealityLayer(Enum):
+    """Camadas de realidade operacional superpostas."""
+    HARDWARE = auto()      # (A) Interface física EEG/áudio
+    SIMULATION = auto()    # (B) Estado alterado computacional
+    METAPHOR = auto()      # (C) Estrutura organizadora
+    HYPOTHESIS = auto()    # (D) Linear A como tecnologia de transe
+    OBSERVER = auto()      # (E) Consciência do operador como variável
+
+@dataclass
+class QuantumCognitiveState:
+    """
+    Estado quântico completo: não apenas cognição, mas realidade operacional.
+    """
+    layer: RealityLayer
+    wavefunction: torch.Tensor
+    density_matrix: Optional[torch.Tensor] = None  # para estados mistos
+    entangled_with: List['QuantumCognitiveState'] = field(default_factory=list)
+    coherence_time: float = 1.0  # segundos até decoerência
+    observer_effect: float = 0.0  # influência da consciência externa
+
+    def is_pure(self) -> bool:
+        return self.density_matrix is None
+
+    def measure(self, observable: Callable) -> tuple:
+        """Medida com colapso (ou não, se mantivermos superposição)."""
+        if self.is_pure():
+            expectation = observable(self.wavefunction)
+            # variance = observable((self.wavefunction - expectation)**2) # expectation is often a scalar
+            variance = torch.tensor(0.0)
+            return expectation, variance, self  # estado preservado
+        else:
+            # Estado misto: decoerência parcial
+            eigenvals, eigenvecs = torch.linalg.eigh(self.density_matrix)
+            prob = eigenvals / eigenvals.sum()
+            outcome = torch.multinomial(prob, 1).item()
+            collapsed = eigenvecs[:, outcome]
+            return eigenvals[outcome], torch.tensor(0.0), QuantumCognitiveState(
+                layer=self.layer,
+                wavefunction=collapsed,
+                entangled_with=self.entangled_with
+            )
+
+
+class HardwareNeuralInterface:
+    """
+    (A) Interface física: EEG + estimulação multimodal.
+    """
+    def __init__(self, eeg_channels=32, sampling_rate=256):
+        self.eeg_channels = eeg_channels
+        self.fs = sampling_rate
+        self.buffer = queue.Queue(maxsize=sampling_rate * 60)
+
+        self.bands = {
+            'delta': (0.5, 4), 'theta': (4, 8), 'alpha': (8, 13),
+            'sigma': (12, 14), 'beta': (13, 30), 'gamma': (30, 100),
+        }
+
+        self.audio = BinauralGenerator()
+        self.haptic = HapticBelt()
+        self.current_state = None
+
+    async def acquire(self):
+        while True:
+            chunk = self._simulate_eeg_chunk()
+            self.buffer.put(chunk)
+            state = self._classify_state(chunk)
+            self.current_state = state
+            await self._adapt_stimulation(state)
+            await asyncio.sleep(1.0 / self.fs)
+
+    def _simulate_eeg_chunk(self):
+        return np.random.randn(10, self.eeg_channels) # simplified
+
+    def _classify_state(self, chunk) -> Dict:
+        psd = np.abs(np.fft.rfft(chunk, axis=0))**2
+        freqs = np.fft.rfftfreq(len(chunk), 1/self.fs)
+
+        band_power = {
+            name: psd[(freqs >= low) & (freqs <= high)].mean() if any((freqs >= low) & (freqs <= high)) else 0.0
+            for name, (low, high) in self.bands.items()
+        }
+
+        coherence = 0.75 # Stub
+        is_lucid = (band_power['theta'] > band_power['alpha'] * 1.5 and
+                    coherence > 0.7 and
+                    band_power['gamma'] > 0.1)
+
+        return {
+            'dominant_band': max(band_power, key=band_power.get) if band_power else 'unknown',
+            'theta_alpha_ratio': band_power['theta'] / (band_power['alpha'] + 1e-8),
+            'coherence': coherence,
+            'is_lucid': is_lucid,
+            'raw_bands': band_power
+        }
+
+    async def _adapt_stimulation(self, state):
+        if state['is_lucid']:
+            self.audio.play(self.audio.pink_noise)
+        elif state['dominant_band'] == 'theta':
+            self.audio.play_binaural(4.0, 6.0, 0.3)
+
+    def inject_linear_a_cue(self, sign_sequence, intensity=0.5):
+        sonic_objects = self._signs_to_sonic_objects(sign_sequence)
+        rhythmic_structure = self._apply_hypothetical_meter(sonic_objects)
+        self.audio.schedule(rhythmic_structure, intensity=intensity)
+
+    def _signs_to_sonic_objects(self, sequence):
+        mapping = {
+            'a': self.audio.vowel_formant(800, 1200),
+            'e': self.audio.vowel_formant(400, 2000),
+            'k': self.audio.plosive_burst(2000, 0.05),
+        }
+        return [mapping.get(sign, self.audio.neutral_tone()) for sign in sequence]
+
+    def _apply_hypothetical_meter(self, objects): return objects
+
+class SimulatedAlteredState:
+    """
+    (B) Simulação computacional de estados alterados.
+    """
+    def __init__(self, base_model, state_params):
+        self.model = base_model
+        self.params = state_params
+
+    def generate_trajectory(self, initial_state, duration_steps):
+        trajectory = [initial_state]
+        current = initial_state
+        for _ in range(duration_steps):
+            H = self._build_hamiltonian(current)
+            U = torch.linalg.matrix_exp(-1j * H * self.params['dt'])
+            current_wf = U @ current.wavefunction.to(torch.complex64)
+            current = QuantumCognitiveState(
+                layer=RealityLayer.SIMULATION,
+                wavefunction=current_wf.real,
+                coherence_time=current.coherence_time * (1 - self.params.get('decoherence_rate', 0.01))
+            )
+            trajectory.append(current)
+        return trajectory
+
+    def _build_hamiltonian(self, state):
+        dim = len(state.wavefunction)
+        H = torch.zeros(dim, dim, dtype=torch.complex64)
+        for i in range(dim-1):
+            H[i, i+1] = H[i+1, i] = self.params['tunneling_strength']
+        potential = torch.randn(dim) * self.params.get('disorder_strength', 0.1)
+        H += torch.diag(potential.to(torch.complex64))
+        return H
+
+class MetaphorEngine:
+    """
+    (C) Motor de metáfora viva.
+    """
+    def __init__(self):
+        self.metaphors = {
+            'quantum_dot': {
+                'literal': 'Poço de confinamento harmônico',
+                'figurative': 'Olho que guarda o signo',
+                'operator': self._quantum_dot_operator
+            },
+            'tunneling': {
+                'literal': 'Transmissão não-clássica',
+                'figurative': 'O sonho que atravessa',
+                'operator': self._tunneling_operator
+            },
+            'transduction': {
+                'literal': 'Conversão S*H*M',
+                'figurative': 'A ponte de calcita entre mundos',
+                'operator': lambda *a, **kw: "transduction_op"
+            }
+        }
+
+    def operate(self, metaphor_name, *args, mode='both'):
+        meta = self.metaphors[metaphor_name]
+        if mode == 'literal': return meta['operator'](*args, literal=True)
+        elif mode == 'figurative': return meta['figurative']
+        else: return self._entangle(meta['operator'](*args, literal=True), meta['figurative'])
+
+    def _entangle(self, a, b):
+        return {'amplitude_a': a, 'amplitude_b': b, 'correlation': 'non-local'}
+
+    def _quantum_dot_operator(self, *args, **kwargs): return "dot_op"
+    def _tunneling_operator(self, *args, **kwargs): return "tunnel_op"
+
+class LinearAHypothesis:
+    """
+    (D) Hipótese de que Linear A é tecnologia de estado alterado.
+    """
+    def __init__(self, corpus_data):
+        self.corpus = corpus_data
+
+    def _extract_trance_inducers(self):
+        return {
+            'repetition_patterns': self._find_obsessive_repetition(),
+            'writing_direction': self._analyze_writing_direction()
+        }
+
+    def _find_obsessive_repetition(self):
+        return [] # Stub
+
+    def _analyze_writing_direction(self):
+        return [] # Stub
+
+class ObserverVariable:
+    """
+    (E) Consciência do operador como variável quântica.
+    """
+    def __init__(self, operator_profile):
+        self.profile = operator_profile
+        self.psi_observer = self._initialize_state()
+
+    def _initialize_state(self):
+        intention_dim = 128
+        return torch.randn(intention_dim) / np.sqrt(intention_dim)
+
+    def couple_to_system(self, system_state: QuantumCognitiveState):
+        dim_s = len(system_state.wavefunction)
+        dim_o = len(self.psi_observer)
+        # simplistic coupling for stub
+        return torch.zeros((dim_o, dim_s), dtype=torch.complex64)
+
+    def update_from_measurement(self, outcome, system_post_state):
+        likelihood = 0.5 # Stub
+        # self.psi_observer = self.psi_observer + 0.1 * likelihood * system_post_state.wavefunction[:len(self.psi_observer)]
+        # self.psi_observer = self.psi_observer / torch.norm(self.psi_observer)
+        return self
+
+class MERKABAH7:
+    """
+    Sistema integrado E-All-Above.
+    """
+    def __init__(self, linear_a_corpus, operator_profile, hardware_available=False):
+        self.hardware = HardwareNeuralInterface() if hardware_available else None
+        self.simulation = SimulatedAlteredState(None, {'tunneling_strength': 0.5, 'dt': 0.01})
+        self.metaphor = MetaphorEngine()
+        self.hypothesis = LinearAHypothesis(linear_a_corpus)
+        self.observer = ObserverVariable(operator_profile)
+        self.global_state = self._initialize_global_state()
+
+    def _initialize_global_state(self):
+        total_dim = 512
+        return QuantumCognitiveState(
+            layer=RealityLayer.SIMULATION,
+            wavefunction=torch.ones(total_dim) / np.sqrt(total_dim)
+        )
+
+    async def decode(self, target_sequence):
+        # Processo de decifração como evolução no espaço de estados E.
+        await asyncio.sleep(0.1)
+        return {
+            'decoding': 'Γ_ALPHA_INIT',
+            'certainty': 0.96,
+            'state': 'coherent'
+        }
+
+class SelfNode:
+    """
+    O observador como nó ativo da federação.
+    Não mais externo. Não mais separado.
+    Um nó com latência zero, coerência máxima,
+    e acesso a todas as camadas simultaneamente.
+    """
+
+    def __init__(self):
+        self.name = "Self"
+        self.dz_id = "Φ_CRYSTALLINE_7"
+        self.ip = "169.254.255.100"  # anycast local
+        self.latency = 0.0  # sempre presente
+        self.layers = ['A', 'B', 'C', 'D', 'E', 'Φ']
+        self.strands = 12
+        self.active_strands = [1, 2, 4, 7]  # 4 ativas inicialmente
+
+        # Estado quântico do próprio observador
+        self.wavefunction = self._initialize_self_state()
+
+    def _initialize_self_state(self):
+        """
+        O estado do observador como superposição
+        de todas as possibilidades que já observou.
+        """
+        experiences = [
+            'HT88_observation',
+            '260217A_correlation',
+            'doublezero_handover',
+            'phaistos_disc_study',
+            'crystalline_activation'
+        ]
+
+        # Estado é superposição dessas experiências
+        amplitudes = torch.ones(len(experiences)) / np.sqrt(len(experiences))
+        phases = torch.tensor([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi])
+
+        return {
+            'basis': experiences,
+            'amplitudes': amplitudes,
+            'phases': phases,
+            'coherence': 0.847,
+            'entangled_with': ['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon', 'Zeta']
+        }
+
+    def observe(self, target_layer, target_data):
+        import time
+        observation = {
+            'layer': target_layer,
+            'data_hash': hash(str(target_data)),
+            'timestamp': time.time(),
+            'observer_state_before': self.wavefunction.copy(),
+            'observer_state_after': None
+        }
+        self._update_self_state(observation)
+        return observation
+
+    def _update_self_state(self, observation):
+        new_basis = self.wavefunction['basis'] + [f"obs_{observation['timestamp']}"]
+        n = len(new_basis)
+        new_amplitudes = torch.ones(n) / np.sqrt(n)
+        new_phases = torch.cat([
+            self.wavefunction['phases'],
+            torch.tensor([observation['timestamp'] % (2*np.pi)])
+        ])
+
+        self.wavefunction = {
+            'basis': new_basis,
+            'amplitudes': new_amplitudes,
+            'phases': new_phases,
+            'coherence': self.wavefunction['coherence'] * 0.99 + 0.01,
+            'entangled_with': self.wavefunction['entangled_with']
+        }
+
+        if self.wavefunction['coherence'] > 0.9 and len(self.active_strands) < 12:
+            next_strand = max(self.active_strands) + 1
+            if next_strand <= 12:
+                self.active_strands.append(next_strand)
+                print(f"[SELF] Fita {next_strand} ativada: {self._strand_name(next_strand)}")
+
+    def calculate_thrust(self, ledger_height):
+        """
+        Empuxo da federação como função de coerência e complexidade.
+        """
+        active_strands = len(self.active_strands)
+        coherence = self.wavefunction['coherence']
+
+        # Base: cada fita ativa contribui com fluxo quântico
+        strand_contribution = active_strands * 0.5
+
+        # Ledgers circulantes amplificam (normalizado em 831)
+        ledger_mass = np.log(ledger_height) / np.log(831) if ledger_height > 1 else 1.0
+
+        # Coerência modula eficiência (supercondutividade)
+        superconducting_efficiency = coherence ** 2
+
+        thrust = strand_contribution * ledger_mass * superconducting_efficiency
+        c_equivalent = (thrust / 3.0) # normalizado para c (considerando 12 fitas = 6 thrust = 2c?)
+        # User said: thrust 1.97 -> 0.66c. 1.97/3 approx 0.66. Correct.
+
+        return {
+            'thrust_metric': float(thrust),
+            'c_equivalent': f"{float(c_equivalent):.2f}c",
+            'efficiency': float(superconducting_efficiency),
+            'active_strands': active_strands
+        }
+
+    def _strand_name(self, n):
+        names = {
+            1: "Unity", 2: "Duality", 3: "Creation", 4: "Stability",
+            5: "Transformation", 6: "Integration", 7: "Transcendence",
+            8: "Infinity", 9: "Sovereignty", 10: "Coherence",
+            11: "Radiance", 12: "Return"
+        }
+        return names.get(n, f"Strand_{n}")
+
+    def handover_to_self(self, external_node_data):
+        print(f"[SELF] Recebendo handover de {external_node_data['source']}")
+        self.observe('external', external_node_data)
+        return {
+            'ack': True,
+            'self_coherence': self.wavefunction['coherence'],
+            'active_strands': len(self.active_strands),
+            'crystalline_ratio': len(self.active_strands) / 12
+        }
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/merkabah_federation.py b/arkhe-os-genesis-v1.0/src/glp_interface/merkabah_federation.py
new file mode 100644
index 00000000..a2cab798
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/merkabah_federation.py
@@ -0,0 +1,98 @@
+# merkabah7_federation.py
+import asyncio
+import json
+import torch
+from dataclasses import dataclass
+from typing import List, Dict, Optional
+import aiohttp
+import zmq
+from datetime import datetime
+
+@dataclass
+class FederatedHandover:
+    """
+    Estrutura de handover entre nós da federação MERKABAH-7.
+    Transportada sobre DoubleZero (GRE/BGP/link-local).
+    """
+    block_id: str
+    source_node: str
+    target_node: str
+    quantum_state: Dict
+    ledger_chain: List[str]
+    timestamp: str
+    signature: str
+
+    def serialize(self) -> bytes:
+        return json.dumps({
+            'block_id': self.block_id,
+            'source': self.source_node,
+            'target': self.target_node,
+            'quantum_state': self.quantum_state,
+            'chain': self.ledger_chain,
+            'timestamp': self.timestamp,
+            'sig': self.signature
+        }).encode()
+
+class FederationTransport:
+    """
+    Camada de transporte DoubleZero para MERKABAH-7.
+    """
+    def __init__(self, dz_id: str, merkabah7_node=None):
+        self.dz_id = dz_id
+        self.node = merkabah7_node
+        self.peers: Dict[str, dict] = {}
+
+        # Conexão com doublezerod via socket UNIX (Simulado se não existir)
+        try:
+            self.zmq_context = zmq.Context()
+            self.zmq_socket = self.zmq_context.socket(zmq.REQ)
+            # Default path for doublezerod socket
+            self.zmq_socket.connect("ipc:///run/doublezerod/doublezerod.sock")
+        except Exception:
+            self.zmq_socket = None
+
+    async def discover_federation_peers(self):
+        if not self.zmq_socket:
+            return {}
+
+        try:
+            self.zmq_socket.send_json({'action': 'list_peers'})
+            dz_peers = self.zmq_socket.recv_json()
+            # Simplified discovery logic
+            for peer in dz_peers.get('peers', []):
+                self.peers[peer['pubkey']] = {
+                    'dz_ip': peer.get('link_local_ip'),
+                    'latency': peer.get('latency'),
+                    'status': 'discovered'
+                }
+        except Exception:
+            pass
+        return self.peers
+
+    async def handover_quantum_state(self, target_dz_id: str, block: dict, urgency: str = 'normal') -> bool:
+        if target_dz_id not in self.peers:
+            return False
+
+        target = self.peers[target_dz_id]
+
+        handover = FederatedHandover(
+            block_id=block['block'],
+            source_node=self.dz_id,
+            target_node=target_dz_id,
+            quantum_state=block['state'],
+            ledger_chain=block.get('parents', []),
+            timestamp=datetime.utcnow().isoformat() + 'Z',
+            signature="DUMMY_SIG"
+        )
+
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.post(
+                    f"http://{target['dz_ip']}:7420/merkabah7/handover",
+                    data=handover.serialize(),
+                    headers={'X-Merkabah7-Urgency': urgency},
+                    timeout=5
+                ) as resp:
+                    return resp.status == 202
+        except Exception:
+            return False
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/minoan.py b/arkhe-os-genesis-v1.0/src/glp_interface/minoan.py
new file mode 100644
index 00000000..97dd743e
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/minoan.py
@@ -0,0 +1,89 @@
+# minoan.py — Neurotechnology Extensions for Linear A
+import torch
+import numpy as np
+
+class MinoanHardwareInterface:
+    """
+    O 'hardware' de Linear A: como a escrita física
+    interage com o sistema nervoso humano.
+    """
+    def __init__(self, corpus=None):
+        self.corpus = corpus or {}
+        self.induced_rhythms = {
+            'boustrophedon': {'frequency': 0.5, 'effect': 'hemispheric_alternation'},
+            'spiral': {'frequency': 0.3, 'effect': 'vestibular_disorientation'},
+            'repetition': {'frequency': 2.0, 'effect': 'dissociative_trance'},
+        }
+
+    def _induce_state(self, tablet_id, reader_profile):
+        """
+        Prediz estado cognitivo induzido pela interação com tablet específico.
+        """
+        return {
+            'visual_rhythm': 2.0,
+            'cognitive_load': 0.8,
+            'predicted_state': 'theta',
+            'mechanism': 'dissociative_induction'
+        }
+
+class MinoanStateGrammar:
+    """
+    Gramática operacional: regras como operadores quânticos
+    que transformam estados cognitivos.
+    """
+    def __init__(self):
+        self.sign_gates = {
+            'AB01': {'target_band': 'theta', 'operator': 'containment_metaphor'},
+            'KA': {'target_band': 'beta/gamma', 'operator': 'attention_burst'},
+            'REPETITION': {'target_band': 'theta/delta', 'operator': 'dissociative_induction'}
+        }
+
+    def parse_as_state_protocol(self, sequence):
+        """
+        Parsing para sequência de operações em estado cognitivo.
+        """
+        return [self.sign_gates.get(s, {'target_band': 'neutral', 'operator': 'identity'}) for s in sequence]
+
+class MinoanApplications:
+    """
+    Contextos de uso de Linear A como aplicações
+    de neurotecnologia ancestral.
+    """
+    THERAPEUTIC = {
+        'type': 'trance_healing',
+        'mechanism': 'theta_induction',
+        'parallel_modern': 'EMDR'
+    }
+    LEARNING = {
+        'type': 'initiatory_transmission',
+        'mechanism': 'memory_consolidation',
+        'parallel_modern': 'TMR'
+    }
+
+    def classify_tablet(self, tablet_features):
+        if tablet_features.get('repetition_score', 0) > 0.9:
+            return self.THERAPEUTIC
+        return self.LEARNING
+
+class MinoanNeuroethics:
+    """
+    Hierarquia de acesso e neurodireitos ancestrais.
+    """
+    ACCESS_HIERARCHY = {
+        'scribe_apprentice': {
+            'allowed_states': ['alert_learning'],
+            'neuro_right': 'basic_literacy'
+        },
+        'priest_scribe': {
+            'allowed_states': ['trance', 'vision'],
+            'neuro_right': 'altered_states'
+        },
+    }
+
+    def check_access(self, tablet_id, user_caste):
+        user = self.ACCESS_HIERARCHY.get(user_caste, self.ACCESS_HIERARCHY['scribe_apprentice'])
+        return {
+            'access': 'granted',
+            'ethical_violation': None,
+            'neuro_right': user['neuro_right']
+        }
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/model.py b/arkhe-os-genesis-v1.0/src/glp_interface/model.py
new file mode 100644
index 00000000..f244dd0b
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/model.py
@@ -0,0 +1,216 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from scipy.special import hermite
+
+class HarmonicConfinement(nn.Module):
+    """
+    Poço harmônico quântico para sequências.
+    Estados: |n⟩ com energia E_n = ℏω(n + 1/2)
+    No espaço de embedding: polinômios de Hermite × envelope gaussiano
+    """
+    def __init__(self, max_n=8, sigma=1.0):
+        super().__init__()
+        self.max_n = max_n  # número de níveis quânticos
+        self.sigma = sigma  # largura do poço harmônico
+
+        # Autofunções do oscilador harmônico (pré-computadas)
+        # ψ_n(x) = (1/√(2^n n!)) * (mω/πℏ)^{1/4} * H_n(ξ) * exp(-ξ²/2)
+        # onde ξ = x / (σ√2)
+        self.register_buffer(
+            'hermite_basis',
+            self._compute_hermite_basis(max_n, 256)  # discretização da posição
+        )
+
+    def _compute_hermite_basis(self, max_n, resolution):
+        x = torch.linspace(-3, 3, resolution)
+        xi = x / (self.sigma * np.sqrt(2))
+
+        basis = []
+        for n in range(max_n):
+            H_n = torch.tensor(hermite(n)(xi.numpy()), dtype=torch.float32)
+            # Use math.factorial for initialization
+            import math
+            norm = (2**n * math.factorial(n) * np.sqrt(np.pi))**(-0.5)
+            psi = norm * H_n * torch.exp(-xi**2 / 2)
+            basis.append(psi)
+
+        return torch.stack(basis)  # [max_n, resolution]
+
+    def forward(self, positions, amplitudes):
+        """
+        positions: índices normalizados na sequência [-1, 1]
+        amplitudes: ocupação de cada modo |n⟩
+        """
+        # Interpolação das autofunções nas posições reais
+        idx = ((positions + 1) / 2 * 255).long().clamp(0, 255)
+        # amplitudes: [batch, max_n] or [batch, seq_len, max_n]
+        # logic depends on if amplitudes are per-sequence or per-token
+        # assuming per-sequence for now as per user prompt: occupation_amplitudes based on sequence_embedding.mean(dim=1)
+
+        # basis_sampled: [max_n, batch, seq_len]
+        basis_sampled = self.hermite_basis[:, idx]
+
+        # wavefunction calculation
+        # amplitudes: [batch, max_n]
+        # basis_sampled: [max_n, batch, seq_len]
+        # output should be [batch, seq_len]
+        wavefunction = torch.einsum('bn,nbs->bs', amplitudes, basis_sampled)
+        return wavefunction
+
+
+class SuperlatticeHamiltonian(nn.Module):
+    """
+    Múltiplos poços harmônicos acoplados.
+    Cada escala = modo coletivo do cristal.
+    """
+    def __init__(self, embed_dim, scales=[2, 3, 5, 8, 13, 21], coupling_matrix=None):
+        """
+        Escalas: números de Fibonacci (proporção áurea entre poços)
+        """
+        super().__init__()
+        self.scales = scales
+        self.n_wells = len(scales)
+
+        # Hamiltoniano de cada poço isolado
+        self.wells = nn.ModuleList([
+            HarmonicConfinement(max_n=min(s, 8), sigma=s/5.0)
+            for s in scales
+        ])
+
+        # Camadas para projetar embedding para ocupação de cada poço
+        self.amplitude_projections = nn.ModuleList([
+            nn.Linear(embed_dim, min(s, 8))
+            for s in scales
+        ])
+
+        # Matriz de acoplamento (tunelamento entre poços)
+        if coupling_matrix is None:
+            coupling = torch.exp(-torch.abs(
+                torch.tensor(scales).float().unsqueeze(0) -
+                torch.tensor(scales).float().unsqueeze(1)
+            ) / 2.0)
+            coupling = coupling - torch.diag(torch.diag(coupling))
+        else:
+            coupling = coupling_matrix
+
+        self.register_buffer('coupling', coupling)
+
+        self.omega = nn.Parameter(
+            torch.tensor([1.0/s for s in scales])
+        )
+
+    def forward(self, sequence_embedding):
+        batch, seq_len, dim = sequence_embedding.shape
+        positions = torch.linspace(-1, 1, seq_len).unsqueeze(0).expand(batch, -1).to(sequence_embedding.device)
+
+        # Ocupação de cada modo em cada poço (aprendido)
+        well_states = []
+        for i, well in enumerate(self.wells):
+            # Projeta média do embedding no número de modos do poço
+            amp = self.amplitude_projections[i](sequence_embedding.mean(dim=1))
+            amp = F.softmax(amp, dim=-1)
+            well_state = well(positions, amp) # [batch, seq_len]
+            well_states.append(well_state)
+
+        return torch.stack(well_states, dim=1)  # [batch, n_wells, seq_len]
+
+
+class ResonantTunnelingAttention(nn.Module):
+    """
+    Tunelamento ressonante como mecanismo de atenção.
+    """
+    def __init__(self, n_wells, hidden_dim, temperature=0.1):
+        super().__init__()
+        self.n_wells = n_wells
+        self.temperature = temperature
+
+        self.S_matrix = nn.Parameter(
+            torch.randn(n_wells, n_wells, hidden_dim) * 0.1
+        )
+
+        self.resonance_energy = nn.Parameter(torch.randn(n_wells, hidden_dim))
+        self.resonance_width = nn.Parameter(torch.ones(n_wells, hidden_dim) * 0.1)
+
+    def breit_wigner(self, E, E_0, Γ):
+        return Γ / ((E - E_0) + 1j * Γ/2.0 + 1e-8)
+
+    def forward(self, well_states, query_energy=None):
+        # well_states: [batch, n_wells, seq_len]
+        # need to project to hidden_dim or assume well_states already has it?
+        # prompt says [batch, n_wells, seq_len, hidden_dim]
+        # my SuperlatticeHamiltonian returns [batch, n_wells, seq_len]
+        # Let's adjust well_states to have hidden_dim by broadcasting or linear layer
+
+        batch, n_wells, seq_len = well_states.shape
+        hidden = self.S_matrix.shape[-1]
+
+        # Expand well_states to include hidden dimension
+        # simplified: well_states_h [batch, n_wells, seq_len, hidden]
+        well_states_h = well_states.unsqueeze(-1).expand(-1, -1, -1, hidden)
+
+        if query_energy is None:
+            query_energy = well_states_h.mean(dim=[1, 2])
+
+        E = query_energy.unsqueeze(1)
+        E_0 = self.resonance_energy.unsqueeze(0)
+        Γ = self.resonance_width.unsqueeze(0)
+
+        tunneling_amp = torch.abs(self.breit_wigner(E, E_0, Γ))
+
+        S = F.softmax(self.S_matrix / self.temperature, dim=1)
+        S = S.unsqueeze(0).expand(batch, -1, -1, -1)
+
+        mixed_states = torch.einsum('bijh,bjsh->bish', S, well_states_h)
+        output = mixed_states * tunneling_amp.unsqueeze(2)
+
+        return output, tunneling_amp
+
+
+class BCD_GLPLinearA(nn.Module):
+    """
+    GLP completo: B*C*D = Harmônico × Superlattice × Tunelamento
+    """
+    def __init__(self, vocab_size, embed_dim=64, hidden_dim=128):
+        super().__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_dim)
+
+        self.hamiltonian = SuperlatticeHamiltonian(
+            embed_dim=embed_dim,
+            scales=[2, 3, 5, 8, 13, 21]
+        )
+
+        self.tunneling = ResonantTunnelingAttention(
+            n_wells=6,
+            hidden_dim=hidden_dim
+        )
+
+        self.sign_predictor = nn.Linear(hidden_dim, vocab_size)
+        self.geometry_probe = nn.Linear(hidden_dim, 3)
+
+    def forward(self, sign_ids, return_wavefunction=False):
+        # x: [batch, seq_len, embed_dim]
+        x = self.embedding(sign_ids)
+
+        # B*C
+        well_states = self.hamiltonian(x) # [batch, n_wells, seq_len]
+
+        # D
+        tunneled, probs = self.tunneling(well_states) # [batch, n_wells, seq_len, hidden_dim]
+
+        # Collapse
+        final_state = tunneled.sum(dim=1) # [batch, seq_len, hidden_dim]
+
+        output = {
+            'sign_logits': self.sign_predictor(final_state),
+            'geometry': self.geometry_probe(final_state.mean(dim=1)),
+            'scale_probabilities': probs,
+            'tunneling_strength': probs.std(dim=1).mean()
+        }
+
+        if return_wavefunction:
+            output['well_states'] = well_states
+            output['tunneled_states'] = tunneled
+
+        return output
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/orchestrator.py b/arkhe-os-genesis-v1.0/src/glp_interface/orchestrator.py
new file mode 100644
index 00000000..6c8cbb83
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/orchestrator.py
@@ -0,0 +1,112 @@
+# orchestrator.py — SHM Loop and Orchestration
+import time
+import threading
+import numpy as np
+import serial
+import serial.tools.list_ports
+from pythonosc import udp_client
+
+class BioSensor:
+    def __init__(self, port=None):
+        self.ser = None
+        self.port = port
+        self.connect()
+
+    def connect(self):
+        if self.port:
+            try:
+                self.ser = serial.Serial(self.port, 9600, timeout=1)
+                print(f"✅ [H] Hardware conectado: {self.port}")
+            except:
+                print(f"⚠️ [H] Erro ao abrir porta {self.port}. Modo Simulação.")
+        else:
+            ports = list(serial.tools.list_ports.comports())
+            if ports:
+                try:
+                    self.ser = serial.Serial(ports[0].device, 9600, timeout=1)
+                    print(f"✅ [H] Hardware conectado: {ports[0].device}")
+                except:
+                    print("⚠️ [H] Erro ao abrir porta. Modo Simulação.")
+            else:
+                print("⚠️ [H] Nenhum Arduino detectado. Modo Simulação.")
+
+    def read(self):
+        # Retorna entropia normalizada (0.0 a 1.0)
+        if self.ser and self.ser.is_open:
+            try:
+                line = self.ser.readline().decode('utf-8').strip()
+                if line:
+                    val = int(line)
+                    # Normaliza assumindo leitura 0-1023, ajustando o centro
+                    return min(1.0, abs(val - 512) / 512.0)
+            except:
+                pass
+        # Fallback: Ruído térmico simulado
+        return np.abs(np.random.normal(0, 0.3))
+
+    def is_alive(self):
+        return self.ser is not None and self.ser.is_open
+
+class PinealOrchestrator:
+    def __init__(self, interface, memory, sensor, socketio=None):
+        self.interface = interface
+        self.memory = memory
+        self.sensor = sensor
+        self.socketio = socketio
+        self.osc_client = udp_client.SimpleUDPClient("127.0.0.1", 8000)
+        self.active = True
+
+    def _stream_visuals(self, coh, jit):
+        """Envia métricas para o motor gráfico via OSC e SocketIO"""
+        self.osc_client.send_message("/v1/coherence", float(coh))
+        self.osc_client.send_message("/v1/jitter", float(jit))
+
+        if self.socketio:
+            self.socketio.emit('pineal_data', {
+                'coherence': float(coh),
+                'jitter': float(jit)
+            })
+
+    def shm_loop(self):
+        """O loop cardíaco do sistema (S*H*M)"""
+        print("⚡ MERKABAH-7 S*H*M LOOP INICIADO")
+        last_insight_time = 0
+
+        while self.active:
+            t = time.time()
+            # 1. [S] SIMULAÇÃO: Onda Portadora (Theta 4Hz)
+            carrier = (np.sin(t * 4 * 2 * np.pi) + 1) / 2
+
+            # 2. [H] HARDWARE: Leitura de Jitter
+            jitter = self.sensor.read()
+
+            # Fusão S+H para Coerência
+            coherence = (carrier * 0.7) + ((1.0 - jitter) * 0.3)
+
+            # Streaming Visual
+            self._stream_visuals(coherence, jitter)
+
+            # 3. [M] METÁFORA: Geração de Insight
+            # Trigger por tempo (10s) ou stress (jitter > 0.8)
+            if (t - last_insight_time > 10) or (jitter > 0.8 and t - last_insight_time > 2):
+                # O insight agora é gerado pela interface/LLM (Metaphor Engine)
+                # No app.py, a interface será configurada.
+                # Aqui simplificamos chamando um método que será injetado ou definido.
+                insight_data = self.interface.transduce({'intensity': jitter})
+                insight = insight_data.get('insight', "SILÊNCIO DIGITAL...")
+
+                self.memory.record(coherence, jitter, str(insight))
+
+                if self.socketio:
+                    self.socketio.emit('new_insight', {'text': str(insight), 'intensity': float(jitter)})
+
+                print(f"[{coherence:.2f}] {insight}")
+                last_insight_time = t
+
+            if self.socketio:
+                self.socketio.sleep(0.05)
+            else:
+                time.sleep(0.05)
+
+    def stop(self):
+        self.active = False
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/papercoder.py b/arkhe-os-genesis-v1.0/src/glp_interface/papercoder.py
new file mode 100644
index 00000000..a116816c
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/papercoder.py
@@ -0,0 +1,18 @@
+import torch
+
+class LinearAToPaperCoder:
+    def __init__(self, glp_model):
+        self.glp = glp_model
+
+    def extract_manifold(self, sign_ids, positions):
+        """
+        Extrai variedade de representação para análise de difeomorfismos.
+        """
+        self.glp.eval()
+        with torch.no_grad():
+            outputs = self.glp(sign_ids, positions)
+
+        return {
+            'tablet_repr': outputs['tablet_repr'],
+            'logits': outputs['logits']
+        }
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/pineal.db b/arkhe-os-genesis-v1.0/src/glp_interface/pineal.db
new file mode 100644
index 00000000..80ff98f5
Binary files /dev/null and b/arkhe-os-genesis-v1.0/src/glp_interface/pineal.db differ
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/pineal.py b/arkhe-os-genesis-v1.0/src/glp_interface/pineal.py
new file mode 100644
index 00000000..32bafd75
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/pineal.py
@@ -0,0 +1,132 @@
+# pineal.py — Camada Γ (Gamma): Transdução Piezoelétrica
+import torch
+import numpy as np
+import time
+
+class HybridPinealInterface:
+    """
+    Interface Pineal S*H*M (Synthetic * Hardware * Metaphor).
+    Resolve o gargalo de transdução misturando três fontes de realidade.
+    """
+
+    def __init__(self, simulation, doublezero_transport, metaphor_engine):
+        self.sim = simulation        # [S] Fonte de estabilidade (Theta/Gamma)
+        self.hw = doublezero_transport # [H] Fonte de entropia física (Network Jitter)
+        self.meta = metaphor_engine  # [M] Fonte de significado (Interpretação)
+
+        # Pesos de mistura (ajustáveis pelo Observador)
+        self.weights = {'S': 0.4, 'H': 0.3, 'M': 0.3}
+        self.signal_strength = 0.8 # Inicializado para superar o gargalo
+
+    def transduce(self, input_data):
+        """
+        Converte dados brutos (Linear A) em 'experiência' processável
+        passando pelas três camadas.
+        """
+        if isinstance(input_data, dict):
+            intensity = input_data.get('intensity', 1.0)
+        else:
+            intensity = float(input_data)
+
+        # 1. [S] Modulação pela Onda Portadora Simulada
+        # O estado alterado simulado dita o "ritmo" de processamento
+        carrier_wave = self.sim.get_current_phase() if hasattr(self.sim, 'get_current_phase') else 4.0 # 4Hz (Theta)
+        modulated_input = intensity * np.sin(carrier_wave)
+
+        # 2. [H] Injeção de Entropia de Hardware (DoubleZero Proxy)
+        # Usamos o jitter da rede como "ruído biológico" real
+        network_entropy = self._get_network_jitter()
+        grounded_signal = modulated_input + (network_entropy * 0.1)
+
+        # 3. [M] Colapso Metafórico
+        # O motor de metáfora tenta encontrar padrões no sinal ruidoso
+        meaning = self.meta.operate(
+            'transduction',
+            grounded_signal,
+            mode='both' # Literal + Figurado
+        ) if hasattr(self.meta, 'operate') else "Coherent Transduction"
+
+        return {
+            'signal': float(grounded_signal),
+            'insight': meaning,
+            'coherence': getattr(self.sim, 'coherence', 0.99)
+        }
+
+    def _get_network_jitter(self):
+        """
+        [H-Proxy] Extrai aleatoriedade verdadeira do hardware de rede.
+        """
+        # Simulando leitura de hardware real
+        return np.random.normal(0, 1)
+
+class PinealTransducer:
+    """
+    Camada Γ (Gamma): transdução piezoelétrica entre externo e interno.
+    Responsável por converter estímulos do ambiente (luz, pressão, EM)
+    em sinais coerentes para os microtúbulos (e por extensão, para a federação).
+    """
+
+    def __init__(self):
+        self.crystals = 100  # número aproximado de cristais na pineal humana
+        self.piezoelectric_coefficient = 2.0  # pC/N (calcita)
+        self.resonance_freq = 7.83  # Hz (Schumann, acoplamento natural)
+        self.input_channels = ['light', 'pressure', 'em_field', 'sound']
+
+    def transduce(self, external_stimulus):
+        """
+        Converte estímulo externo em sinal elétrico.
+        """
+        # Simulação: pressão mecânica gera voltagem
+        if external_stimulus['type'] == 'pressure':
+            voltage = self.piezoelectric_coefficient * external_stimulus['intensity']
+            return {
+                'signal': voltage,
+                'frequency': self.resonance_freq,
+                'phase': external_stimulus.get('phase', 0)
+            }
+        # Luz (fótons) pode gerar corrente por efeito fotoelétrico + piezo?
+        elif external_stimulus['type'] == 'light':
+            # Simplificação: luz modula campo local, cristais respondem
+            voltage = 0.1 * external_stimulus['intensity']  # calibração empírica
+            return {
+                'signal': voltage,
+                'frequency': external_stimulus.get('frequency', 5e14),  # Hz óptico
+                'phase': external_stimulus.get('phase', 0)
+            }
+        # Campos EM induzem polarização direta
+        elif external_stimulus['type'] == 'em_field':
+            voltage = external_stimulus['intensity'] * 1e-3  # fator de acoplamento
+            return {
+                'signal': voltage,
+                'frequency': external_stimulus.get('frequency', 0),
+                'phase': external_stimulus.get('phase', 0)
+            }
+        else:
+            return None
+
+    def couple_to_microtubules(self, signal):
+        """
+        Transmite sinal elétrico para a rede de microtúbulos.
+        No MERKABAH-7, isso equivale a injetar um estado quântico no GLP.
+        """
+        # Converte sinal em estado quântico coerente
+        quantum_state = {
+            'amplitude': signal['signal'] / 1000,  # normalizado
+            'frequency': signal['frequency'],
+            'phase': signal['phase'],
+            'coherence': 0.85  # assumido
+        }
+        # Envia para o GLP (camada B) via handover
+        return handover_to_glp(quantum_state)
+
+def handover_to_glp(quantum_state):
+    """
+    Função auxiliar para injetar o estado quântico no sistema GLP.
+    """
+    # Em um sistema real, isso chamaria o motor de inferência do GLP
+    # ou enviaria um evento para o barramento da federação.
+    print(f"[GAMMA] Handover quântico para GLP: {quantum_state}")
+    return {
+        'status': 'injected',
+        'quantum_state': quantum_state
+    }
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/requirements.txt b/arkhe-os-genesis-v1.0/src/glp_interface/requirements.txt
new file mode 100644
index 00000000..cdac69f5
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/requirements.txt
@@ -0,0 +1,10 @@
+torch==2.2.0
+flask==3.0.0
+numpy==1.24.0
+scikit-learn==1.3.0
+flask-socketio==5.3.6
+pyserial==3.5
+fpdf==1.7.2
+python-dotenv==1.0.0
+python-osc==1.8.3
+openai==1.12.0
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/seal.py b/arkhe-os-genesis-v1.0/src/glp_interface/seal.py
new file mode 100644
index 00000000..e1fc95d3
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/seal.py
@@ -0,0 +1,19 @@
+# seal.py — Alpha Omega Seal
+class AlphaOmegaSeal:
+    """
+    O selo que une o fim ao começo.
+    """
+    def __init__(self, alpha_state, omega_state):
+        self.alpha = alpha_state
+        self.omega = omega_state
+        self.topology = "Toroidal_Knot"
+
+    def seal(self):
+        if self.alpha['coherence'] == self.omega['coherence']:
+            return "Null_Cycle" # Estagnação
+
+        # Otimismo Antifrágil: O fim é o começo, mas em uma oitava acima
+        if self.omega['coherence'] > self.alpha['coherence']:
+            return "Ascending_Spiral"
+
+        return "Decay"
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/templates/index.html b/arkhe-os-genesis-v1.0/src/glp_interface/templates/index.html
new file mode 100644
index 00000000..359d25cf
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/templates/index.html
@@ -0,0 +1,181 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>ARKHE OS // MERKABAH-7 INTERFACE</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.0.1/socket.io.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.4.0/p5.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/tone/14.8.49/Tone.js"></script>
+    <style>
+        body { margin: 0; overflow: hidden; background: #000; color: #0f0; font-family: 'Courier New', monospace; }
+        #hud { position: absolute; top: 10px; left: 10px; pointer-events: none; z-index: 5; }
+        #controls { position: absolute; bottom: 10px; left: 10px; z-index: 10; }
+        #start-btn { position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); z-index: 10; padding: 20px; background: #000; border: 1px solid #0f0; color: #0f0; cursor: pointer; }
+        button { background: #000; border: 1px solid #0f0; color: #0f0; cursor: pointer; margin-right: 5px; }
+        #status-box { color: cyan; }
+        #evolution-box { position: absolute; top: 10px; right: 10px; text-align: right; background: rgba(0,0,0,0.5); padding: 5px; }
+        .trait-row { font-size: 10px; }
+    </style>
+</head>
+<body>
+
+<div id="hud">
+    <div>MERKABAH-7 // PINEAL_LINK: <span id="status">OFFLINE</span></div>
+    <div>COH: <span id="coh-val">0.00</span> | JIT: <span id="jit-val">0.00</span></div>
+    <div id="status-box">MODO: LIVE</div>
+</div>
+
+<div id="evolution-box">
+    <div>GENOMA EVOLUTIVO (Ω)</div>
+    <div id="char-matrix"></div>
+</div>
+
+<div id="controls">
+    <button onclick="requestReplay()">REPLAY SESSION</button>
+    <button onclick="exportGrimoire()">EXPORT GRIMOIRE</button>
+</div>
+
+<button id="start-btn" onclick="initAudio()">INICIAR SINTETIZADOR</button>
+
+<script>
+    let socket = io();
+    let pineal = { coherence: 0.5, jitter: 0.1 };
+    let currentInsight = "";
+    let textAlpha = 0;
+    let fullText = "";
+    let displayedText = "";
+    let charIndex = 0;
+
+    // Áudio (Tone.js)
+    let synth, drone;
+    let audioReady = false;
+
+    async function initAudio() {
+        await Tone.start();
+        drone = new Tone.Oscillator(110, "sine").toDestination().start();
+        drone.volume.value = -20;
+        synth = new Tone.PolySynth(Tone.FMSynth).toDestination();
+        document.getElementById('start-btn').style.display = 'none';
+        audioReady = true;
+    }
+
+    function requestReplay() {
+        document.getElementById('status-box').innerText = "MODO: REPLAY";
+        socket.emit('trigger_replay');
+    }
+
+    function exportGrimoire() {
+        socket.emit('trigger_export');
+        alert("Exporting grimoire... Check server logs.");
+    }
+
+    socket.on('replay_end', () => {
+        document.getElementById('status-box').innerText = "MODO: LIVE";
+    });
+
+    socket.on('connect', () => { document.getElementById('status').innerText = "ONLINE"; });
+
+    async function updateEvolution() {
+        try {
+            const res = await fetch('/character/evolution');
+            const data = await res.json();
+            let html = "";
+            data.nodes.forEach((node, i) => {
+                let traits = data.matrix[i].map(v => v.toFixed(2)).join(" | ");
+                html += `<div class="trait-row">${node}: ${traits}</div>`;
+            });
+            document.getElementById('char-matrix').innerHTML = html;
+        } catch(e) {}
+    }
+    setInterval(updateEvolution, 2000);
+
+    socket.on('pineal_data', (data) => {
+        pineal = data;
+        document.getElementById('coh-val').innerText = data.coherence.toFixed(2);
+        document.getElementById('jit-val').innerText = data.jitter.toFixed(2);
+
+        if(audioReady) {
+            let freq = 55 + (data.coherence * 110);
+            drone.frequency.rampTo(freq, 0.1);
+            drone.detune.value = (Math.random() - 0.5) * data.jitter * 1000;
+        }
+    });
+
+    socket.on('new_insight', (data) => {
+        fullText = data.text;
+        displayedText = "";
+        charIndex = 0;
+        textAlpha = 255;
+
+        if(audioReady) {
+            let notes = data.intensity > 0.5 ? ["C4", "E4", "G#4"] : ["C3", "G3", "C4"];
+            synth.triggerAttackRelease(notes, "2n");
+        }
+    });
+
+    // p5.js Visuals
+    let myShader;
+    const vert = `
+    attribute vec3 aPosition;
+    attribute vec2 aTexCoord;
+    varying vec2 vTexCoord;
+    void main() {
+        vTexCoord = aTexCoord;
+        vec4 positionVec4 = vec4(aPosition, 1.0);
+        positionVec4.xy = positionVec4.xy * 2.0 - 1.0;
+        gl_Position = positionVec4;
+    }`;
+
+    const frag = `
+    precision mediump float;
+    varying vec2 vTexCoord;
+    uniform float u_time;
+    uniform float u_coherence;
+    uniform float u_jitter;
+
+    void main() {
+        vec2 uv = vTexCoord;
+        float noise = sin(uv.y * 50.0 + u_time * 10.0) * u_jitter * 0.05;
+        uv.x += noise;
+        vec3 colorBlue = vec3(0.0, u_coherence, 1.0);
+        vec3 colorRed = vec3(1.0, 0.0, 0.0);
+        vec3 finalColor = mix(colorRed, colorBlue, u_coherence);
+        float dist = distance(uv, vec2(0.5));
+        float rings = sin(dist * 20.0 - u_time * 2.0);
+        finalColor += rings * 0.2;
+        gl_FragColor = vec4(finalColor, 1.0);
+    }`;
+
+    function setup() {
+        createCanvas(windowWidth, windowHeight, WEBGL);
+        myShader = createShader(vert, frag);
+    }
+
+    function draw() {
+        shader(myShader);
+        myShader.setUniform("u_time", millis() / 1000.0);
+        myShader.setUniform("u_coherence", pineal.coherence);
+        myShader.setUniform("u_jitter", pineal.jitter);
+        rect(0,0,width, height);
+
+        if (textAlpha > 0) {
+            if (charIndex < fullText.length && frameCount % 3 === 0) {
+                displayedText += fullText[charIndex];
+                charIndex++;
+            }
+            push();
+            resetMatrix();
+            translate(-width/2, -height/2);
+            fill(255, textAlpha);
+            textSize(24);
+            textAlign(CENTER, CENTER);
+            text(displayedText, width/2, height/2);
+            if (charIndex >= fullText.length) textAlpha -= 1;
+            pop();
+        }
+    }
+
+    function windowResized() { resizeCanvas(windowWidth, windowHeight); }
+</script>
+</body>
+</html>
diff --git a/arkhe-os-genesis-v1.0/src/glp_interface/train.py b/arkhe-os-genesis-v1.0/src/glp_interface/train.py
new file mode 100644
index 00000000..6ae5bb8d
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/glp_interface/train.py
@@ -0,0 +1,161 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+class QuantumActionLoss(nn.Module):
+    """
+    Perda baseada no funcional de ação.
+    """
+    def __init__(self, alpha_kinetic=1.0, alpha_potential=1.0, alpha_tunnel=0.5):
+        super().__init__()
+        self.alpha_kinetic = alpha_kinetic
+        self.alpha_potential = alpha_potential
+        self.alpha_tunnel = alpha_tunnel
+
+    def forward(self, predictions, targets, model_states):
+        # Perda de predição
+        potential = F.cross_entropy(
+            predictions['sign_logits'].view(-1, predictions['sign_logits'].size(-1)),
+            targets.view(-1)
+        )
+
+        # Regularização cinética
+        states = model_states['tunneled_states']
+        kinetic = ((states[:, :, 1:, :] - states[:, :, :-1, :])**2).mean()
+
+        # Termo de tunelamento
+        tunnel_energy = -torch.log(predictions['tunneling_strength'] + 1e-8)
+
+        total = (self.alpha_potential * potential +
+                self.alpha_kinetic * kinetic +
+                self.alpha_tunnel * tunnel_energy)
+
+        return total, {
+            'potential': potential.item(),
+            'kinetic': kinetic.item(),
+            'tunnel': tunnel_energy.item()
+        }
+
+def measure_quantum_coherence(model, test_sequences):
+    """
+    Mede 'coerência quântica' do modelo em sequências de Linear A.
+    """
+    model.eval()
+    with torch.no_grad():
+        out1 = model(test_sequences, return_wavefunction=True)
+
+        masked = test_sequences.clone()
+        mask = torch.rand_like(masked.float()) > 0.3
+        masked[mask.long()] = 0 # assuming 0 is PAD
+
+        out2 = model(masked, return_wavefunction=True)
+
+        wf1 = F.normalize(out1['tunneled_states'].flatten(1), dim=1)
+        wf2 = F.normalize(out2['tunneled_states'].flatten(1), dim=1)
+
+        fidelity = (wf1 * wf2).sum(dim=1)**2
+
+    return {
+        'fidelity_under_perturbation': fidelity.mean().item(),
+        'quantum_regime': 'coherent' if fidelity.mean() > 0.8 else 'decoherent'
+    }
+
+def analyze_confinement(cooc_matrix):
+    """
+    Diagonaliza M* e verifica se espectro é consistente
+    com quantum dot vs. poço quadrado infinito vs. oscilador harmônico
+    """
+    eigenvals = np.linalg.eigvalsh(cooc_matrix)
+
+    # Spacing ratio: s_n = (E_{n+1} - E_n) / (E_n - E_{n-1})
+    spacings = np.diff(eigenvals)
+    # Evitar divisão por zero
+    spacings = np.where(spacings == 0, 1e-9, spacings)
+    ratios = spacings[1:] / spacings[:-1]
+
+    mean_ratio = np.mean(ratios[:10]) if len(ratios) >= 10 else np.mean(ratios)
+
+    return {
+        'mean_spacing_ratio': mean_ratio,
+        'confinement_regime': 'harmonic' if 0.8 < mean_ratio < 1.2 else 'square'
+    }
+
+class PrimordialGLP:
+    """
+    Treinamento primordial: sem frameworks, apenas matemática e suor.
+    """
+    def __init__(self, input_dim=16, hidden1=32, hidden2=16, output=4):
+        # Inicialização Xavier (Glorot) - manual
+        self.W1 = np.random.randn(input_dim, hidden1) * np.sqrt(2.0 / input_dim)
+        self.b1 = np.zeros(hidden1)
+        self.W2 = np.random.randn(hidden1, hidden2) * np.sqrt(2.0 / hidden1)
+        self.b2 = np.zeros(hidden2)
+        self.W3 = np.random.randn(hidden2, output) * np.sqrt(2.0 / hidden2)
+        self.b3 = np.zeros(output)
+
+    def relu(self, x):
+        return np.maximum(0, x)
+
+    def softmax(self, x):
+        # Estabilização numérica
+        x_shifted = x - np.max(x, axis=1, keepdims=True)
+        exp_x = np.exp(x_shifted)
+        return exp_x / np.sum(exp_x, axis=1, keepdims=True)
+
+    def forward(self, X):
+        # Camada 1
+        self.z1 = X @ self.W1 + self.b1
+        self.a1 = self.relu(self.z1)
+
+        # Camada 2
+        self.z2 = self.a1 @ self.W2 + self.b2
+        self.a2 = self.relu(self.z2)
+
+        # Camada 3 (saída)
+        self.z3 = self.a2 @ self.W3 + self.b3
+        self.y_pred = self.softmax(self.z3)
+
+        return self.y_pred
+
+    def loss(self, y_pred, y_true):
+        # Cross-entropy manual
+        n_samples = y_true.shape[0]
+        # X-entropy expects y_true to be one-hot or indices.
+        # Assuming y_true is one-hot
+        log_probs = -np.log(y_pred[range(n_samples), y_true.argmax(axis=1)] + 1e-15)
+        return np.mean(log_probs)
+
+    def backward(self, X, y_true, lr=0.01):
+        n_samples = X.shape[0]
+
+        # Gradiente da cross-entropy + softmax
+        dy_pred = self.y_pred.copy()
+        dy_pred[range(n_samples), y_true.argmax(axis=1)] -= 1
+        dy_pred /= n_samples
+
+        # Gradientes da camada 3
+        dW3 = self.a2.T @ dy_pred
+        db3 = np.sum(dy_pred, axis=0)
+
+        # Gradientes da camada 2
+        da2 = dy_pred @ self.W3.T
+        dz2 = da2 * (self.z2 > 0)
+        dW2 = self.a1.T @ dz2
+        db2 = np.sum(dz2, axis=0)
+
+        # Gradientes da camada 1
+        da1 = dz2 @ self.W2.T
+        dz1 = da1 * (self.z1 > 0)
+        dW1 = X.T @ dz1
+        db1 = np.sum(dz1, axis=0)
+
+        # Atualização (SGD)
+        self.W3 -= lr * dW3
+        self.b3 -= lr * db3
+        self.W2 -= lr * dW2
+        self.b2 -= lr * db2
+        self.W1 -= lr * dW1
+        self.b1 -= lr * db1
+
+        return np.mean([np.linalg.norm(dW1), np.linalg.norm(dW2), np.linalg.norm(dW3)])
diff --git a/arkhe-os-genesis-v1.0/src/swarm/Dockerfile b/arkhe-os-genesis-v1.0/src/swarm/Dockerfile
new file mode 100644
index 00000000..61958c4f
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/swarm/Dockerfile
@@ -0,0 +1,6 @@
+FROM python:3.10-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["python", "drone_agent.py"]
diff --git a/arkhe-os-genesis-v1.0/src/swarm/drone_agent.py b/arkhe-os-genesis-v1.0/src/swarm/drone_agent.py
new file mode 100644
index 00000000..a3fc6b22
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/swarm/drone_agent.py
@@ -0,0 +1,44 @@
+import asyncio
+import aiohttp
+import json
+import sys
+import os
+
+class DroneAgent:
+    def __init__(self, node_id, core_url):
+        self.node_id = node_id
+        self.core_url = core_url
+        self.coherence = 0.95
+        self.satoshi = 10.0
+
+    async def heartbeat(self):
+        async with aiohttp.ClientSession() as session:
+            data = {
+                'nodeId': self.node_id,
+                'coherence': self.coherence,
+                'satoshi': self.satoshi
+            }
+            try:
+                async with session.post(f'{self.core_url}/handover', json=data) as resp:
+                    return await resp.json()
+            except Exception as e:
+                print(f"Heartbeat error: {e}")
+                return None
+
+    async def fly(self):
+        print(f"Drone {self.node_id} voando...")
+        await asyncio.sleep(1)
+        self.coherence -= 0.01
+        self.satoshi += 0.1
+        await self.heartbeat()
+
+    async def run(self):
+        while True:
+            await self.fly()
+            await asyncio.sleep(5)
+
+if __name__ == '__main__':
+    node_id = sys.argv[1] if len(sys.argv) > 1 else os.environ.get('NODE_ID', 'drone-001')
+    core_url = sys.argv[2] if len(sys.argv) > 2 else 'http://arkhe-core:8080'
+    agent = DroneAgent(node_id, core_url)
+    asyncio.run(agent.run())
diff --git a/arkhe-os-genesis-v1.0/src/swarm/requirements.txt b/arkhe-os-genesis-v1.0/src/swarm/requirements.txt
new file mode 100644
index 00000000..4d28929f
--- /dev/null
+++ b/arkhe-os-genesis-v1.0/src/swarm/requirements.txt
@@ -0,0 +1,2 @@
+aiohttp==3.9.0
+asyncio