Architecture

Architecture Guide

System Overview

The Deep Learning Protocol is a hierarchical reasoning engine with built-in data protection. It processes information through multiple layers, each adding context and depth.

┌─────────────────────────────────────────────────────────┐
│              Program (Interactive Menu)                 │
│         • FAQ Display • Protocol Runner                 │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│            DeepLearningProtocol (Orchestrator)          │
│  SetAim() → ProcessAtDepth() → PursueAim()             │
└────────────────────┬────────────────────────────────────┘
                     │
    ┌────────────────┼────────────────┐
    │                │                │
┌───▼──┐      ┌─────▼────┐    ┌─────▼──┐
│ Aim  │      │ Depth    │    │ State  │
│Store │      │Process   │    │Manage  │
└──────┘      └──────────┘    └────────┘
    │                │                │
    └────────────────┼────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│            AbstractCore (Processing Layer)              │
│    ProcessCoreReasoning() — Wraps input in context     │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│         DataLossPrevention (Protection Layer)           │
│   IsSuspiciousContent() • BackupState()                │
│   Detects memes, binary data, large payloads           │
└─────────────────────────────────────────────────────────┘

Components

1. Program Class (User Interface)

Responsibility: Manage user interaction and menu system

Key Methods:

• Main() — Menu loop with 3 options (Protocol, FAQ, Exit)
• RunInteractiveProtocol() — Collect user input (question, goal, depth)
• DisplayFAQ() — Browse 10 FAQ Q&A pairs

Code Location: Program.cs, lines 1-160

2. AbstractCore Class (Base Processing)

Responsibility: Fundamental reasoning layer

Interface: None (standalone base class)

Key Method:

public virtual string ProcessCoreReasoning(string input)
{
    return $"[Abstract Core] Deep abstract processing: {input}";
}

Purpose: Wrap input in context layer (used recursively for depth processing)

Code Location: Program.cs, lines 162-172

3. IStateInterface

Responsibility: State management and retrieval

Method Signatures:

string GetCurrentState();
void UpdateState(string newState);

Used By: DeepLearningProtocol to track processing state

Code Location: Program.cs, lines 174-179

4. IAimInterface

Responsibility: Goal-directed processing

Method Signatures:

void SetAim(string aim);
string PursueAim();

Purpose:

• SetAim() — Store user's goal
• PursueAim() — Retrieve aim with formatting

Code Location: Program.cs, lines 181-186

5. IDepthInterface

Responsibility: Hierarchical depth-based processing

Method Signatures:

string ProcessAtDepth(string input, int depth);

Purpose: Recursively apply AbstractCore reasoning N times

Example:

Depth 1: [Abstract Core] input
Depth 2: [Abstract Core] [Abstract Core] input
Depth 3: [Abstract Core] [Abstract Core] [Abstract Core] input

Code Location: Program.cs, lines 188-193

6. DataLossPrevention Class (DLP)

Responsibility: Content protection and state preservation

Key Methods:

public bool IsSuspiciousContent(string content)
{
    // Detects:
    // - Image files (.png, .jpg, .jpeg)
    // - Base64 encoded data
    // - Keywords ("meme")
    // - Large single-line payloads (>200 chars, no newlines)
    
    return isSuspicious;
}

public void BackupState(string currentState)
{
    // Saves to: ./.dlp_backups/{timestamp}.txt
    // Preserves state history for recovery
}

Protection Logic:

1. On UpdateState() call, DLP checks content
2. If suspicious, backs up current state
3. Blocks update → state becomes [DLP-BLOCKED]
4. Otherwise, allows normal update

Code Location: Program.cs, lines 195-240

7. DeepLearningProtocol Class (Orchestrator)

Responsibility: Combine all interfaces and coordinate workflow

Inherits: IStateInterface, IAimInterface, IDepthInterface, AbstractCore

Key Methods:

SetAim(string aim)

• Stores goal
• Updates state: "Aiming: {aim}"
• Protected by DLP

ProcessAtDepth(string input, int depth)

• Recursively calls ProcessCoreReasoning() depth times
• Each call adds [Abstract Core] layer

PursueAim()

• Returns formatted string: "[Aim Pursuit] {currentProcessing}"
• Shows how aim influenced processing

ExecuteProtocol(string input, string goal, int depth)

• Complete workflow:
  1. SetAim(goal) — Store goal
  2. ProcessAtDepth(input, depth) — Apply depth processing
  3. PursueAim() — Return formatted result
  4. UpdateState() — Log completion
• Returns: Formatted result string

Code Location: Program.cs, lines 242-330

Data Flow

Scenario: User Input Processing

User Input:
Question: "How can I optimize my code?"
Goal: "Performance improvement"
Depth: 2

     ↓

Program.Main()
  └─ RunInteractiveProtocol() collects input
       └─ Calls: DeepLearningProtocol.ExecuteProtocol()

     ↓

DeepLearningProtocol.ExecuteProtocol()
  1. SetAim("Performance improvement")
     └─ UpdateState() → [DLP checks] → State updated
  
  2. ProcessAtDepth("How can I optimize my code?", 2)
     └─ Recursively call AbstractCore.ProcessCoreReasoning()
        Iteration 1: "[Abstract Core] How can I optimize my code?"
        Iteration 2: "[Abstract Core] [Abstract Core] How can I optimize my code?"
  
  3. PursueAim()
     └─ Returns: "[Aim Pursuit] {processed_result}"
  
  4. UpdateState() → [DLP checks] → Final state saved

     ↓

Program.Main()
  └─ Displays formatted result to user
     └─ Shows state tracking

Interface Hierarchy

┌────────────────────────────────────────┐
│      DeepLearningProtocol              │
│  (Implements all interfaces below)     │
└────────────────────────────────────────┘
         │             │             │
         │             │             │
    ┌────▼─────┐  ┌───▼────┐  ┌────▼──────┐
    │ IAimInterface      │IStateInterface   │IDepthInterface │
    │ SetAim()  │  │GetCurrentState() │ProcessAtDepth()    │
    │ PursueAim()│  │UpdateState()    │                  │
    └───────────┘  └──────────┘  └─────────┘
         │             │             │
         └─────────────┼─────────────┘
                       │
                   Extends
                       │
         ┌─────────────▼──────────────┐
         │      AbstractCore          │
         │ ProcessCoreReasoning()     │
         └────────────────────────────┘

Execution Flow Diagram

Start
  │
  ├─ Main Loop
  │  ├─ Display Menu
  │  └─ Get User Choice
  │
  ├─ [Choice 1] Protocol
  │  └─ RunInteractiveProtocol()
  │     ├─ Prompt: Question
  │     ├─ Prompt: Goal
  │     ├─ Prompt: Depth (1-10)
  │     └─ Call ExecuteProtocol()
  │        ├─ SetAim()
  │        ├─ ProcessAtDepth()
  │        ├─ PursueAim()
  │        └─ UpdateState()
  │           └─ [DLP Protection]
  │
  ├─ [Choice 2] FAQ
  │  └─ DisplayFAQ()
  │     ├─ Show Questions 1-10
  │     └─ Display Selected Answer
  │
  └─ [Choice 3] Exit → End Program

Key Design Patterns

1. Layered Architecture

• UI Layer — Program.cs Main/menu methods
• Business Layer — DeepLearningProtocol orchestration
• Core Layer — AbstractCore reasoning
• Protection Layer — DataLossPrevention

2. Interface Segregation

• Each interface has single responsibility:
  • IStateInterface — State only
  • IAimInterface — Goal only
  • IDepthInterface — Depth processing only

3. Composition over Inheritance

• DeepLearningProtocol inherits AbstractCore
• Implements multiple interfaces
• Composes with DataLossPrevention

4. Protection by Default

• All state updates protected by DLP
• Backups created before blocking
• User can recover from ./.dlp_backups/

State Management

State Transitions

Initial State
    │
    ├─ User runs protocol
    │
    └─ SetAim("goal")
       └─ State: "Aiming: goal"
          [DLP: Check] → Allowed or Blocked
       
       └─ ProcessAtDepth()
          └─ State: "Depth N processed"
             [DLP: Check] → Allowed or Blocked
       
       └─ Complete
          └─ Final State: "Depth N processed"
             or "[DLP-BLOCKED]" if suspicious

Backup Mechanism

When DLP blocks an update:

Current State: "Aiming: goal"
    │
    ├─ New Update: suspicious_content
    │
    ├─ DLP.IsSuspiciousContent() → true
    │
    ├─ DLP.BackupState("Aiming: goal")
    │  └─ Saves to: ./.dlp_backups/2025-12-18_14-30-45-123.txt
    │
    └─ State → "[DLP-BLOCKED]"

Extension Points

Add New Processing Layer

// In Program.cs, extend AbstractCore:
public class SemanticLayer : AbstractCore
{
    public override string ProcessCoreReasoning(string input)
    {
        var baseProcessing = base.ProcessCoreReasoning(input);
        return $"[Semantic] {baseProcessing}";
    }
}

Add New Validation Rule to DLP

// In DataLossPrevention.IsSuspiciousContent():
if (content.Contains("forbiddenKeyword"))
{
    return true; // Block this content
}

Add Custom Interface

public interface ICustomInterface
{
    void CustomMethod();
}

// Implement in DeepLearningProtocol class

Performance Considerations

Time Complexity

• ProcessAtDepth(input, depth) — O(depth) because each iteration adds one layer
• ExecuteProtocol() — O(depth) (dominated by ProcessAtDepth)
• IsSuspiciousContent() — O(n) where n = content length

Space Complexity

• State storage — O(1) (single string)
• Backups — O(n × m) where n = number of backups, m = average backup size
• Depth processing — O(depth) call stack

Optimization Tips

1. Limit depth — Keep depth ≤ 10 for reasonable performance
2. DLP checks — Backups only on blocked content, not every update
3. FAQ caching — Dictionary is static, no runtime overhead

Testing Architecture

Test Categories

1. State Tests — Verify state get/update
2. Aim Tests — Verify goal setting/pursuing
3. Depth Tests — Verify recursive layering (theory with depths 0, 1, 2)
4. Integration Tests — Full ExecuteProtocol workflow

See Testing Guide for details.

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Next: Read DLP Guide to understand data protection in detail.