V 2.0.0.8

Author: quickattach0-tech

DeepLearningProtocol/AbstractCore.cs

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+using System;
+
+namespace DeepLearningProtocol
+{
+    /// <summary>
+    /// AbstractCore represents the deepest reasoning layer (maroon/red node in the architecture).
+    /// It provides fundamental processing logic that wraps input with standardized abstract processing notation.
+    /// This is the base class inherited by DeepLearningProtocol.
+    /// </summary>
+    public class AbstractCore
+    {
+        /// <summary>
+        /// Processes input through the core reasoning engine.
+        /// </summary>
+        /// <param name="input">The input string to process</param>
+        /// <returns>A formatted string indicating abstract core processing has occurred</returns>
+        public virtual string ProcessCoreReasoning(string input)
+        {
+            return $"[Abstract Core] Deep abstract processing: {input}";
+        }
+    }
+}

DeepLearningProtocol/DataLossPrevention.cs

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+using System;
+using System.IO;
+
+namespace DeepLearningProtocol
+{
+    /// <summary>
+    /// DataLossPrevention (DLP) is a protective layer that detects and blocks suspicious content.
+    /// It implements heuristics to identify meme-like or binary payloads and creates automatic backups.
+    /// This prevents accidental data corruption from user input.
+    /// </summary>
+    public class DataLossPrevention
+    {
+        /// <summary>Directory path for storing state backups</summary>
+        private readonly string _backupDir = "./.dlp_backups";
+
+        /// <summary>
+        /// Initializes the DLP system and creates backup directory if needed.
+        /// Gracefully handles IO errors in restricted environments.
+        /// </summary>
+        public DataLossPrevention()
+        {
+            try
+            {
+                Directory.CreateDirectory(_backupDir);
+            }
+            catch
+            {
+                // ignore errors creating backup dir in restricted environments
+            }
+        }
+
+        /// <summary>
+        /// Detects if content appears to be meme-like or binary data.
+        /// Uses multiple heuristics: file extensions, data URIs, keywords, and payload size.
+        /// </summary>
+        /// <param name="content">The content to analyze</param>
+        /// <returns>True if content is suspicious; false otherwise</returns>
+        public bool IsPotentialMeme(string content)
+        {
+            if (string.IsNullOrEmpty(content)) return false;
+            var lower = content.ToLowerInvariant();
+            
+            // Check for image-related keywords and formats
+            if (lower.Contains("meme") || lower.Contains(".png") || lower.Contains(".jpg") || 
+                lower.Contains(".jpeg") || lower.Contains("data:image") || lower.Contains("base64,"))
+                return true;
+            
+            // Large single-line payloads often indicate pasted binary data
+            if (content.Length > 200 && !content.Contains("\n")) 
+                return true;
+            
+            return false;
+        }
+
+        /// <summary>
+        /// Backs up the current state to a timestamped file in the backup directory.
+        /// Used before state updates to ensure previous states are preserved.
+        /// </summary>
+        /// <param name="state">The state to backup</param>
+        public void BackupState(string state)
+        {
+            try
+            {
+                var file = Path.Combine(_backupDir, $"state_{DateTime.UtcNow:yyyyMMdd_HHmmss_fff}.txt");
+                File.WriteAllText(file, state ?? string.Empty);
+            }
+            catch
+            {
+                // best-effort backup; swallow IO errors to avoid crashes
+            }
+        }
+    }
+}

DeepLearningProtocol/DeepLearningProtocol.cs

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+using System;
+
+namespace DeepLearningProtocol
+{
+    /// <summary>
+    /// DeepLearningProtocol is the main orchestrator combining all interfaces and components.
+    /// It implements a hierarchical reasoning system with state management, goal pursuit, 
+    /// and depth-based recursive processing. DLP protection is integrated throughout.
+    /// </summary>
+    public class DeepLearningProtocol : AbstractCore, IAimInterface, IDepthInterface, IStateInterface
+    {
+        /// <summary>Tracks the current operational state</summary>
+        private string _currentState = "Initial";
+        
+        /// <summary>Tracks the current goal/aim</summary>
+        private string _aim = "General Reasoning";
+        
+        /// <summary>DLP instance for content protection</summary>
+        private readonly DataLossPrevention _dlp = new();
+
+        /// <summary>
+        /// Gets the current state of the protocol.
+        /// </summary>
+        /// <returns>The current state string</returns>
+        public string GetCurrentState() => _currentState;
+
+        /// <summary>
+        /// Updates the protocol state with DLP protection.
+        /// Backs up previous state and blocks suspicious content.
+        /// </summary>
+        /// <param name="newState">The new state to set</param>
+        public void UpdateState(string newState)
+        {
+            // Backup current state before changing
+            try { _dlp.BackupState(_currentState); } catch { }
+
+            // If the incoming state looks like meme or binary payload, block it
+            if (_dlp.IsPotentialMeme(newState))
+            {
+                Console.WriteLine("DLP: Potential meme-like content detected. State update blocked and backed up.");
+                _currentState = $"[DLP-BLOCKED]";
+                return;
+            }
+
+            _currentState = newState;
+            Console.WriteLine($"State updated: {_currentState}");
+        }
+
+        /// <summary>
+        /// Sets a new goal/aim and updates the state accordingly.
+        /// </summary>
+        /// <param name="goal">The goal to pursue</param>
+        /// <returns>Confirmation message</returns>
+        public string SetAim(string goal)
+        {
+            _aim = goal;
+            UpdateState($"Aiming: {_aim}");
+            return $"Aim set to: {_aim}";
+        }
+
+        /// <summary>
+        /// Pursues the current aim given a state.
+        /// Applies core reasoning and associates result with the aim.
+        /// </summary>
+        /// <param name="currentState">The current state to pursue from</param>
+        /// <returns>Result of aim pursuit</returns>
+        public string PursueAim(string currentState)
+        {
+            var coreResult = ProcessCoreReasoning(currentState);
+            return $"[Aim Pursuit] {coreResult} towards {_aim}";
+        }
+
+        /// <summary>
+        /// Processes input at a specified hierarchical depth.
+        /// Recursively applies abstract reasoning 'depthLevel' times.
+        /// </summary>
+        /// <param name="input">The input to process</param>
+        /// <param name="depthLevel">How many layers of processing to apply</param>
+        /// <returns>Result of depth processing</returns>
+        public string ProcessAtDepth(string input, int depthLevel)
+        {
+            var processed = input;
+            for (int i = 0; i < depthLevel; i++)
+            {
+                processed = ProcessCoreReasoning(processed);
+            }
+            UpdateState($"Depth {depthLevel} processed");
+            return $"[Depth {depthLevel}] {processed}";
+        }
+
+        /// <summary>
+        /// Executes the complete protocol workflow:
+        /// 1. Sets the aim
+        /// 2. Processes input at specified depth
+        /// 3. Pursues the aim with the processed result
+        /// </summary>
+        /// <param name="initialInput">The initial input/question</param>
+        /// <param name="goal">The goal to pursue</param>
+        /// <param name="depth">How many levels of hierarchical processing</param>
+        /// <returns>Final protocol result</returns>
+        public string ExecuteProtocol(string initialInput, string goal, int depth)
+        {
+            SetAim(goal);
+            var depthOutput = ProcessAtDepth(initialInput, depth);
+            var finalOutput = PursueAim(depthOutput);
+            return finalOutput;
+        }
+    }
+}

DeepLearningProtocol/Interfaces.cs

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+namespace DeepLearningProtocol
+{
+    /// <summary>
+    /// IAimInterface defines goal-oriented processing operations.
+    /// Goals drive the protocol's reasoning direction through spiral-like exploration paths.
+    /// </summary>
+    public interface IAimInterface
+    {
+        /// <summary>Sets a new goal/aim for the protocol</summary>
+        string SetAim(string goal);
+
+        /// <summary>Pursues the current aim given a state</summary>
+        string PursueAim(string currentState);
+    }
+
+    /// <summary>
+    /// IDepthInterface defines hierarchical depth-based processing.
+    /// Allows recursive application of abstract reasoning at configurable depth levels.
+    /// </summary>
+    public interface IDepthInterface
+    {
+        /// <summary>Processes input at a specified hierarchical depth level</summary>
+        string ProcessAtDepth(string input, int depthLevel);
+    }
+
+    /// <summary>
+    /// IStateInterface defines state management operations.
+    /// Tracks and updates the protocol's current operational state.
+    /// </summary>
+    public interface IStateInterface
+    {
+        /// <summary>Retrieves the current state</summary>
+        string GetCurrentState();
+
+        /// <summary>Updates the state (with DLP protection)</summary>
+        void UpdateState(string newState);
+    }
+}