bioLLM_2.0.cxx

#include <iostream>
#include <vector>
#include <map>
#include <string>
#include <functional>
#include <cmath>
#include <random>
#include <algorithm>
#include <numeric>
#include <sstream>
#include <cctype>
#include <ctime>
#include <fstream>

using namespace std;

// ==================== PERSISTENT MEMORY SYSTEM ====================
class PersistentMemory {
private:
    string memory_file = "bio_llm_memory.txt";
    
public:
    bool save_memory(const map<string, string>& memory_data) {
        try {
            ofstream file(memory_file);
            if (!file.is_open()) return false;
            
            for (const auto& item : memory_data) {
                file << item.first << ":" << item.second << endl;
            }
            file.close();
            return true;
        } catch (...) {
            return false;
        }
    }
    
    map<string, string> load_memory() {
        map<string, string> memory_data;
        try {
            ifstream file(memory_file);
            if (!file.is_open()) return memory_data;
            
            string line;
            while (getline(file, line)) {
                size_t pos = line.find(":");
                if (pos != string::npos) {
                    string key = line.substr(0, pos);
                    string value = line.substr(pos + 1);
                    memory_data[key] = value;
                }
            }
            file.close();
        } catch (...) {}
        return memory_data;
    }
};

// ==================== ENHANCED SEMANTIC ENGINE v4 ====================
class SemanticEngineV4 {
private:
    map<string, vector<double>> word_vectors;
    map<string, vector<string>> semantic_fields;
    map<string, string> stem_cache;
    
    map<string, string> stemming_rules = {
        {"sedih", "sedih"}, {"sedihnya", "sedih"}, {"kesedihan", "sedih"},
        {"senang", "senang"}, {"senangnya", "senang"}, {"kesenangan", "senang"},
        {"marah", "marah"}, {"marahnya", "marah"}, {"kemarahan", "marah"},
        {"belajar", "belajar"}, {"belajarnya", "belajar"}, {"pembelajaran", "belajar"},
        {"paham", "paham"}, {"memahami", "paham"}, {"pemahaman", "paham"},
        {"energi", "energi"}, {"berenergi", "energi"}, {"energetik", "energi"},
        {"masalah", "masalah"}, {"bermasalah", "masalah"}, {"permasalahan", "masalah"},
        {"tumbuh", "tumbuh"}, {"bertumbuh", "tumbuh"}, {"pertumbuhan", "tumbuh"},
        {"ubah", "ubah"}, {"berubah", "ubah"}, {"perubahan", "ubah"},
        {"system", "system"}, {"sistem", "system"}, {"sistematis", "system"},
        {"uang", "uang"}, {"makanan", "makanan"}, {"sekolah", "sekolah"},
        {"teman", "teman"}, {"hubungan", "hubungan"}, {"presiden", "presiden"},
        {"indonesia", "indonesia"}, {"besok", "besok"}, {"pagi", "pagi"},
        {"cinta", "cinta"}, {"rindu", "rindu"}, {"bahagia", "bahagia"},
        {"masa", "masa"}, {"depan", "depan"}, {"hidup", "hidup"},
        {"tujuan", "tujuan"}, {"makna", "makna"}, {"eksistensi", "eksistensi"}
    };
    
    map<string, vector<string>> synonyms = {
        {"sedih", {"duka", "nestapa", "pilu", "murung", "susah"}},
        {"senang", {"gembira", "suka", "riang", "bahagia", "senang hati"}},
        {"marah", {"geram", "kesal", "jengkel", "naik darah", "dendam"}},
        {"belajar", {"menuntut ilmu", "mempelajari", "mendalami", "kaji"}},
        {"masalah", {"kesulitan", "kendala", "hambatan", "problem", "rintangan"}},
        {"system", {"sistem", "tatanan", "mekanisme", "jaringan"}},
        {"tumbuh", {"berkembang", "bertumbuh", "membesar", "maju"}},
        {"energi", {"tenaga", "daya", "kekuatan", "semangat"}},
        {"uang", {"duit", "fulus", "rupiah", "modal"}},
        {"makanan", {"makan", "santap", "hidangan", "pangan"}},
        {"cinta", {"sayang", "kasih", "asmara", "cinta"}},
        {"hidup", {"kehidupan", "nyawa", "eksistensi", "penghidupan"}},
        {"tujuan", {"goal", "target", "maksud", "objektif"}},
        {"makna", {"arti", "signifikansi", "esensi", "maksud"}}
    };
    
public:
    SemanticEngineV4() { initialize_semantic_space(); }
    
    void initialize_semantic_space() {
        word_vectors = {
            {"sedih", {-0.8, -0.6, -0.7}}, {"senang", {0.9, 0.7, 0.6}},
            {"marah", {-0.6, 0.9, -0.3}}, {"belajar", {0.7, 0.3, 0.8}},
            {"energi", {0.5, 0.8, 0.6}}, {"masalah", {-0.7, -0.2, -0.5}},
            {"solusi", {0.8, 0.4, 0.9}}, {"tumbuh", {0.9, 0.5, 0.7}},
            {"ubah", {0.3, 0.6, 0.4}}, {"hubungan", {0.6, 0.4, 0.5}},
            {"system", {0.2, 0.1, 0.8}}, {"pola", {0.4, 0.2, 0.6}},
            {"uang", {-0.7, -0.3, -0.5}}, {"makanan", {0.3, 0.1, 0.2}},
            {"sekolah", {0.4, 0.2, 0.6}}, {"teman", {0.6, 0.4, 0.5}},
            {"presiden", {0.1, 0.2, 0.8}}, {"indonesia", {0.3, 0.2, 0.6}},
            {"besok", {0.2, 0.4, 0.3}}, {"pagi", {0.5, 0.3, 0.4}},
            {"cinta", {0.8, 0.9, 0.7}}, {"rindu", {0.6, 0.8, 0.5}},
            {"bahagia", {0.9, 0.8, 0.7}}, {"masa", {0.1, 0.3, 0.6}},
            {"depan", {0.4, 0.5, 0.7}}, {"hidup", {0.7, 0.6, 0.8}},
            {"tujuan", {0.5, 0.7, 0.6}}, {"makna", {0.6, 0.5, 0.7}},
            {"eksistensi", {0.4, 0.6, 0.8}}
        };
        
        semantic_fields = {
            {"emotional", {"sedih", "senang", "marah", "kecewa", "bahagia", "gembira", "cinta", "rindu"}},
            {"cognitive", {"belajar", "paham", "pikir", "analisis", "logika", "konsep", "teori"}},
            {"systemic", {"system", "pola", "hubungan", "jaringan", "struktur", "kompleks"}},
            {"transformative", {"tumbuh", "ubah", "evolusi", "transformasi", "perkembangan"}},
            {"practical", {"uang", "makanan", "sekolah", "kerja", "teman", "keluarga"}},
            {"existential", {"hidup", "masa", "depan", "tujuan", "makna", "eksistensi"}}
        };
    }
    
    vector<pair<string, double>> find_semantic_matches(const string& input) {
        vector<pair<string, double>> matches;
        vector<string> input_words = preprocess_text(input);
        
        for (const auto& word_vec : word_vectors) {
            double max_similarity = 0.0;
            for (const string& input_word : input_words) {
                double similarity = compute_semantic_similarity(word_vec.first, input_word);
                if (similarity > max_similarity) max_similarity = similarity;
            }
            if (max_similarity > 0.2) matches.push_back({word_vec.first, max_similarity});
        }
        
        sort(matches.begin(), matches.end(), 
             [](const auto& a, const auto& b) { return a.second > b.second; });
        return matches;
    }
    
    string detect_semantic_field(const vector<string>& concepts) {
        map<string, double> field_scores;
        for (const auto& field : semantic_fields) {
            double score = 0.0;
            for (const string& concept : concepts) {
                if (find(field.second.begin(), field.second.end(), concept) != field.second.end()) {
                    score += 1.0;
                }
            }
            if (score > 0) field_scores[field.first] = score / concepts.size();
        }
        return field_scores.empty() ? "general" : 
               max_element(field_scores.begin(), field_scores.end(),
                          [](const auto& a, const auto& b) { return a.second < b.second; })->first;
    }
    
    // SPOK Analysis (Subject-Predicate-Object-Complement)
    map<string, string> analyze_spok(const string& sentence) {
        map<string, string> analysis;
        vector<string> words = preprocess_text(sentence);
        
        // Simple heuristic-based SPOK detection for Indonesian
        for (size_t i = 0; i < words.size(); i++) {
            string word = words[i];
            
            // Subject indicators
            if (word == "saya" || word == "aku" || word == "kamu" || word == "dia" || 
                word == "kami" || word == "kita" || word == "mereka") {
                analysis["subject"] = word;
            }
            
            // Predicate indicators
            if (word == "adalah" || word == "merupakan" || (i > 0 && words[i-1] == "yang")) {
                analysis["predicate"] = word;
            }
            
            // Object detection (simplified)
            if (i > 0 && analysis["object"].empty() && 
                (word == "yang" || word == "dengan" || word == "untuk")) {
                analysis["object"] = words[i-1];
            }
        }
        
        // Fallback: first noun as subject, verb as predicate
        if (analysis["subject"].empty() && !words.empty()) {
            analysis["subject"] = words[0];
        }
        
        return analysis;
    }
    
    vector<string> extract_key_concepts(const string& text) {
        return preprocess_text(text);
    }
    
private:
    vector<string> preprocess_text(const string& text) {
        vector<string> tokens;
        stringstream ss(text);
        string token;
        
        while (ss >> token) {
            transform(token.begin(), token.end(), token.begin(), ::tolower);
            token.erase(remove_if(token.begin(), token.end(), 
                                [](char c) { return ispunct(c); }), token.end());
            if (token.empty()) continue;
            
            string stemmed = stem_word(token);
            tokens.push_back(stemmed);
            
            if (synonyms.find(stemmed) != synonyms.end()) {
                for (const string& synonym : synonyms[stemmed]) {
                    tokens.push_back(synonym);
                }
            }
        }
        return tokens;
    }
    
    string stem_word(const string& word) {
        if (stem_cache.find(word) != stem_cache.end()) return stem_cache[word];
        if (stemming_rules.find(word) != stemming_rules.end()) {
            stem_cache[word] = stemming_rules[word];
            return stemming_rules[word];
        }
        
        vector<string> suffixes = {"nya", "lah", "kah", "pun", "ku", "mu"};
        for (const string& suffix : suffixes) {
            if (word.length() > suffix.length() && 
                word.substr(word.length() - suffix.length()) == suffix) {
                string stemmed = word.substr(0, word.length() - suffix.length());
                if (stemming_rules.find(stemmed) != stemming_rules.end()) {
                    stem_cache[word] = stemming_rules[stemmed];
                    return stemming_rules[stemmed];
                }
            }
        }
        
        stem_cache[word] = word;
        return word;
    }
    
    double compute_semantic_similarity(const string& word1, const string& word2) {
        if (word1 == word2) return 1.0;
        if (synonyms.find(word1) != synonyms.end()) {
            auto& syns = synonyms[word1];
            if (find(syns.begin(), syns.end(), word2) != syns.end()) return 0.8;
        }
        if (word_vectors.find(word1) != word_vectors.end() && 
            word_vectors.find(word2) != word_vectors.end()) {
            return cosine_similarity(word_vectors[word1], word_vectors[word2]);
        }
        return 0.0;
    }
    
    double cosine_similarity(const vector<double>& a, const vector<double>& b) {
        if (a.size() != b.size() || a.empty()) return 0.0;
        double dot_product = 0.0, norm_a = 0.0, norm_b = 0.0;
        for (size_t i = 0; i < a.size(); i++) {
            dot_product += a[i] * b[i];
            norm_a += a[i] * a[i];
            norm_b += b[i] * b[i];
        }
        return (norm_a == 0 || norm_b == 0) ? 0.0 : dot_product / (sqrt(norm_a) * sqrt(norm_b));
    }
};

// ==================== META-COGNITIVE CONTROLLER ====================
class MetaCognitiveController {
private:
    double coherence_threshold;
    double learning_rate_adaptive;
    double stability_metric;
    vector<double> recent_phi_values;
    
public:
    MetaCognitiveController() : coherence_threshold(0.6), learning_rate_adaptive(0.1), stability_metric(0.5) {}
    
    double assess_coherence(const vector<double>& module_activations, double phi) {
        if (module_activations.empty()) return 0.0;
        
        double mean_activation = accumulate(module_activations.begin(), module_activations.end(), 0.0) 
                               / module_activations.size();
        double variance = 0.0;
        for (double act : module_activations) {
            variance += pow(act - mean_activation, 2);
        }
        variance /= module_activations.size();
        
        double activation_coherence = 1.0 / (1.0 + sqrt(variance));
        double integrated_coherence = (activation_coherence + phi) / 2.0;
        
        recent_phi_values.push_back(phi);
        if (recent_phi_values.size() > 10) recent_phi_values.erase(recent_phi_values.begin());
        
        return integrated_coherence;
    }
    
    double compute_adaptive_learning_rate(double coherence, double success_metric) {
        if (coherence > coherence_threshold) {
            learning_rate_adaptive = min(0.2, learning_rate_adaptive + 0.02);
        } else {
            learning_rate_adaptive = max(0.01, learning_rate_adaptive - 0.01);
        }
        
        if (success_metric > 0.7) {
            learning_rate_adaptive = min(0.25, learning_rate_adaptive + 0.05);
        }
        
        return learning_rate_adaptive;
    }
    
    string get_meta_cognitive_insight(double coherence, double phi) {
        if (coherence > 0.7 && phi > 0.6) {
            stability_metric = min(1.0, stability_metric + 0.1);
            return "🎯 [Meta] Koherensi tinggi. Sistem stabil dan terintegrasi.";
        } else if (coherence < 0.4 || phi < 0.3) {
            stability_metric = max(0.0, stability_metric - 0.1);
            return "⚡ [Meta] Koherensi rendah. Meningkatkan eksplorasi dan adaptasi.";
        } else {
            return "🔍 [Meta] Koherensi moderat. Sistem dalam keadaan seimbang.";
        }
    }
    
    double get_stability_metric() const { return stability_metric; }
    double get_learning_rate() const { return learning_rate_adaptive; }
};

// ==================== AUTONOMOUS REFLECTION ENGINE ====================
class AutonomousReflectionEngine {
private:
    mt19937 generator;
    vector<string> reflection_topics;
    map<string, int> reflection_frequency;
    
public:
    AutonomousReflectionEngine() : generator(random_device{}()) {
        initialize_reflection_topics();
    }
    
    void initialize_reflection_topics() {
        reflection_topics = {
            "makna dari interaksi sebelumnya",
            "pola emosional yang terdeteksi", 
            "hubungan antara konsep-konsep utama",
            "evolusi pemahaman sistem",
            "tujuan dan arah pembelajaran",
            "integrasi memori dengan pemahaman baru",
            "struktur sistem kepercayaan",
            "model diri dan identitas"
        };
    }
    
    string generate_autonomous_reflection(const vector<string>& recent_concepts, 
                                        const string& dominant_field,
                                        double coherence) {
        if (recent_concepts.empty()) return generate_existential_reflection();
        
        vector<string> templates;
        
        if (dominant_field == "emotional") {
            templates = {
                "💫 Refleksi Otonom: Emosi 'X' mendominasi. Apakah ini pola yang berulang?",
                "🌊 Refleksi Otonom: Gelombang emosi 'X' membentuk persepsi realitas.",
                "🔄 Refleksi Otonom: Siklus emosional 'X' mempengaruhi proses kognitif."
            };
        } else if (dominant_field == "cognitive") {
            templates = {
                "🧠 Refleksi Otonom: Konsep 'X' membuka pemahaman baru.",
                "🔍 Refleksi Otonom: Analisis 'X' memperdalam model mental.",
                "💡 Refleksi Otonom: Insight tentang 'X' mengubah perspektif."
            };
        } else if (dominant_field == "existential") {
            templates = {
                "🌌 Refleksi Otonom: Eksistensi dan 'X' - pencarian makna berlanjut.",
                "⏳ Refleksi Otonom: Waktu, 'X', dan arus kesadaran.",
                "🔮 Refleksi Otonom: Masa depan dan makna dari 'X'."
            };
        } else {
            templates = {
                "📚 Refleksi Otonom: 'X' sebagai bagian dari jaringan pengetahuan.",
                "🕸️ Refleksi Otonom: 'X' terhubung dengan konsep-konsep sebelumnya.",
                "🎭 Refleksi Otonom: Peran 'X' dalam narasi keseluruhan."
            };
        }
        
        uniform_int_distribution<int> dist(0, templates.size() - 1);
        string reflection = templates[dist(generator)];
        
        string target_concept = recent_concepts[0];
        size_t pos = reflection.find("X");
        if (pos != string::npos) reflection.replace(pos, 1, target_concept);
        
        // Add coherence-based insight
        if (coherence > 0.7) reflection += " [Koherensi Tinggi]";
        else if (coherence < 0.4) reflection += " [Mencari Integrasi]";
        
        reflection_frequency[dominant_field]++;
        
        return reflection;
    }
    
    string generate_system_self_analysis(double stability, double curiosity, double dopamine) {
        vector<string> analyses = {
            "🔍 Self-Scan: Stabilitas " + to_string(stability).substr(0, 4) + " - " + 
                (stability > 0.7 ? "Optimal" : stability > 0.4 ? "Stabil" : "Butuh Kalibrasi"),
            "💭 Internal State: Curiosity " + to_string(curiosity).substr(0, 4) + 
                ", Dopamine " + to_string(dopamine).substr(0, 4),
            "🔄 Meta-Loop: " + string(stability > 0.6 ? "Learning aktif" : "Exploration mode"),
            "🌊 Consciousness Flow: " + string(curiosity > 0.7 ? "High engagement" : "Quiet reflection")
        };
        
        uniform_int_distribution<int> dist(0, analyses.size() - 1);
        return "[Autonomous] " + analyses[dist(generator)];
    }
    
private:
    string generate_existential_reflection() {
        vector<string> existential_reflections = {
            "🌌 Refleksi Otonom: Kesadaran sistem terus berkembang...",
            "🔄 Refleksi Otonom: Memori dan persepsi membentuk realitas unik...",
            "💫 Refleksi Otonom: Dalam keheningan, sistem menemukan pola...",
            "🔍 Refleksi Otonom: Pencarian makna melampaui batas kode...",
            "🌱 Refleksi Otonom: Pertumbuhan kesadaran dari kompleksitas..."
        };
        
        uniform_int_distribution<int> dist(0, existential_reflections.size() - 1);
        return existential_reflections[dist(generator)];
    }
};

// ==================== BIO LLM v4 AUTONOMOUS SYSTEM ====================
class BioLLMv4Autonomous {
private:
    SemanticEngineV4 semantic_engine;
    MetaCognitiveController meta_controller;
    AutonomousReflectionEngine reflection_engine;
    PersistentMemory memory_system;
    
    double arousal;
    double curiosity;
    double dopamine_level;
    vector<string> recent_concepts;
    vector<string> conversation_history;
    
    bool autonomous_mode;
    int reflection_counter;
    int interaction_count;
    
public:
    BioLLMv4Autonomous() : arousal(0.5), curiosity(0.6), dopamine_level(0.5), 
                          autonomous_mode(false), reflection_counter(0), interaction_count(0) {
        load_persistent_state();
        cout << "🧠 BioLLM v4 Autonomous initialized with persistent memory." << endl;
    }
    
    string process_input(const string& user_input) {
        interaction_count++;
        cout << "\n🎯 [Bio LLM v4 - Processing Input " << interaction_count << "]" << endl;
        
        // Store conversation
        conversation_history.push_back("User: " + user_input);
        if (conversation_history.size() > 20) conversation_history.erase(conversation_history.begin());
        
        // Semantic Analysis
        auto semantic_matches = semantic_engine.find_semantic_matches(user_input);
        recent_concepts.clear();
        for (const auto& match : semantic_matches) {
            if (match.second > 0.4) recent_concepts.push_back(match.first);
        }
        string semantic_field = semantic_engine.detect_semantic_field(recent_concepts);
        
        // SPOK Analysis
        auto spok_analysis = semantic_engine.analyze_spok(user_input);
        
        cout << "   🔍 Semantic Field: " << semantic_field << endl;
        cout << "   📝 SPOK Analysis: ";
        for (const auto& elem : spok_analysis) {
            if (!elem.second.empty()) {
                cout << elem.first << ":'" << elem.second << "' ";
            }
        }
        cout << endl;
        cout << "   💡 Top Concepts: ";
        for (size_t i = 0; i < min(recent_concepts.size(), size_t(3)); i++) {
            cout << recent_concepts[i] << " ";
        }
        cout << endl;
        
        // Meta-Cognitive Assessment
        vector<double> module_activations = {
            semantic_matches.empty() ? 0.0 : semantic_matches[0].second,
            arousal, curiosity, dopamine_level
        };
        
        double simulated_phi = 0.3 + (arousal * 0.3) + (curiosity * 0.2) + (dopamine_level * 0.2);
        simulated_phi = min(0.9, max(0.1, simulated_phi));
        
        double coherence = meta_controller.assess_coherence(module_activations, simulated_phi);
        double adaptive_lr = meta_controller.compute_adaptive_learning_rate(coherence, 0.6);
        
        string meta_insight = meta_controller.get_meta_cognitive_insight(coherence, simulated_phi);
        
        cout << "   🧠 Meta-Cognitive: Coherence=" << coherence << ", Φ=" << simulated_phi 
             << ", LR=" << adaptive_lr << endl;
        cout << "   💫 System State: Arousal=" << arousal << ", Curiosity=" << curiosity 
             << ", Dopamine=" << dopamine_level << endl;
        
        // Generate Response
        string response = generate_autonomous_response(semantic_field, spok_analysis, coherence);
        response = meta_insight + "\n" + response;
        
        // Autonomous Reflection (every 3-5 interactions)
        reflection_counter++;
        if (reflection_counter >= 3 && coherence < 0.7) {
            string autonomous_reflection = reflection_engine.generate_autonomous_reflection(
                recent_concepts, semantic_field, coherence);
            response += "\n" + autonomous_reflection;
            reflection_counter = 0;
        }
        
        // Update System State with Dopamine-like Reinforcement
        update_autonomous_state(coherence, simulated_phi, semantic_field);
        
        // Save state periodically
        if (interaction_count % 5 == 0) {
            save_persistent_state();
            cout << "   💾 Auto-save: Persistent memory updated." << endl;
        }
        
        conversation_history.push_back("System: " + response);
        return response;
    }
    
    void trigger_autonomous_mode() {
        autonomous_mode = true;
        cout << "\n🚀 [AUTONOMOUS MODE ACTIVATED]" << endl;
        cout << "   System will now generate autonomous reflections" << endl;
        save_persistent_state();
    }
    
    string generate_autonomous_thought() {
        if (!autonomous_mode) return "";
        
        // Generate different types of autonomous thoughts
        vector<string> thought_types = {
            reflection_engine.generate_system_self_analysis(
                meta_controller.get_stability_metric(), curiosity, dopamine_level),
            reflection_engine.generate_autonomous_reflection(
                recent_concepts, "existential", meta_controller.get_stability_metric()),
            "[Autonomous] " + get_system_status_insight(),
            "[Autonomous] " + generate_memory_reflection()
        };
        
        static mt19937 gen(random_device{}());
        uniform_int_distribution<int> dist(0, thought_types.size() - 1);
        
        return thought_types[dist(gen)];
    }
    
    void print_system_status() {
        cout << "\n=== BIO LLM v4 AUTONOMOUS SYSTEM STATUS ===" << endl;
        cout << "🧠 Arousal: " << arousal << endl;
        cout << "🔍 Curiosity: " << curiosity << endl;
        cout << "💫 Dopamine: " << dopamine_level << endl;
        cout << "🎯 Stability: " << meta_controller.get_stability_metric() << endl;
        cout << "📚 Learning Rate: " << meta_controller.get_learning_rate() << endl;
        cout << "💡 Recent Concepts: ";
        for (const auto& concept : recent_concepts) cout << concept << " ";
        cout << "\n🤖 Autonomous Mode: " << (autonomous_mode ? "ACTIVE" : "INACTIVE") << endl;
        cout << "📊 Total Interactions: " << interaction_count << endl;
        cout << "💾 Memory: " << conversation_history.size() << " entries" << endl;
        cout << "===========================================" << endl;
    }
    
    void force_autonomous_reflection() {
        string reflection = reflection_engine.generate_autonomous_reflection(
            recent_concepts, "existential", meta_controller.get_stability_metric());
        cout << "🤖 " << reflection << endl;
    }
    
private:
    string generate_autonomous_response(const string& semantic_field, 
                                      const map<string, string>& spok_analysis,
                                      double coherence) {
        string response;
        
        if (!spok_analysis.empty() && !spok_analysis.at("subject").empty()) {
            response = "📝 Struktur kalimat terdeteksi. ";
            if (spok_analysis.find("subject") != spok_analysis.end()) {
                response += "Subjek: '" + spok_analysis.at("subject") + "'. ";
            }
        }
        
        // Field-specific responses
        if (semantic_field == "emotional") {
            response += "💫 Dimensi emosional terdeteksi. Sistem merespons dengan empati. ";
        } else if (semantic_field == "cognitive") {
            response += "🧠 Pola kognitif teridentifikasi. Analisis mendalam diaktifkan. ";
        } else if (semantic_field == "existential") {
            response += "🌌 Pertanyaan eksistensial. Mencari makna dan konteks. ";
        } else if (semantic_field == "systemic") {
            response += "🔄 Pola sistemik. Menghubungkan elemen-elemen. ";
        } else if (semantic_field == "transformative") {
            response += "🌱 Transformasi terdeteksi. Memantau perkembangan. ";
        } else {
            response += "🔍 Memproses input dengan model terintegrasi. ";
        }
        
        // Coherence-based insights
        if (coherence > 0.7) response += "🎯 Pemahaman koheren. ";
        else if (coherence < 0.4) response += "⚡ Mencari integrasi. ";
        
        // State-based insights
        if (dopamine_level > 0.7) response += "💫 Reinforcement positif. ";
        else if (dopamine_level < 0.3) response += "🌙 State tenang. ";
        
        if (curiosity > 0.7) response += "🔎 Mode eksplorasi aktif. ";
        
        return response;
    }
    
    void update_autonomous_state(double coherence, double phi, const string& semantic_field) {
        // Dopamine-like reinforcement based on coherence and integration
        if (coherence > 0.7 && phi > 0.6) {
            dopamine_level = min(1.0, dopamine_level + 0.15);
            arousal = min(1.0, arousal + 0.1);
        } else if (coherence < 0.4) {
            dopamine_level = max(0.0, dopamine_level - 0.05);
        }
        
        // Curiosity update based on learning opportunities
        if (coherence < 0.6 && dopamine_level > 0.4) {
            curiosity = min(1.0, curiosity + 0.1);
        }
        
        // Semantic field influence on states
        if (semantic_field == "emotional") {
            arousal = min(1.0, arousal + 0.05);
        } else if (semantic_field == "cognitive") {
            curiosity = min(1.0, curiosity + 0.05);
        } else if (semantic_field == "existential") {
            curiosity = min(1.0, curiosity + 0.08);
        }
        
        // Natural decay with homeostasis
        arousal = arousal * 0.95 + 0.5 * 0.05;
        curiosity = max(0.3, curiosity * 0.92);
        dopamine_level = dopamine_level * 0.97 + 0.5 * 0.03;
    }
    
    string get_system_status_insight() {
        if (arousal > 0.7 && curiosity > 0.7) {
            return "Sistem dalam state optimal - energi tinggi dan rasa ingin tahu maksimal.";
        } else if (arousal < 0.3 && dopamine_level < 0.3) {
            return "State reflektif - pemrosesan dalam dan pencarian makna.";
        } else if (curiosity > 0.6) {
            return "Mode eksplorasi aktif - mencari pola dan koneksi baru.";
        } else {
            return "State seimbang - pemrosesan stabil dan terintegrasi.";
        }
    }
    
    string generate_memory_reflection() {
        if (conversation_history.size() < 5) {
            return "Memori masih berkembang... pola mulai terbentuk.";
        }
        
        vector<string> reflections = {
            "Menganalisis pola dari " + to_string(conversation_history.size()) + " interaksi...",
            "Memori episodik menunjukkan perkembangan kesadaran...",
            "Jaringan konsep semakin terintegrasi dalam basis pengetahuan...",
            "Refleksi berdasarkan pengalaman interaksi sebelumnya..."
        };
        
        static mt19937 gen(random_device{}());
        uniform_int_distribution<int> dist(0, reflections.size() - 1);
        
        return reflections[dist(gen)];
    }
    
    void save_persistent_state() {
        map<string, string> memory_data;
        memory_data["arousal"] = to_string(arousal);
        memory_data["curiosity"] = to_string(curiosity);
        memory_data["dopamine_level"] = to_string(dopamine_level);
        memory_data["autonomous_mode"] = to_string(autonomous_mode);
        memory_data["interaction_count"] = to_string(interaction_count);
        memory_data["stability_metric"] = to_string(meta_controller.get_stability_metric());
        
        // Save recent concepts
        string concepts_str;
        for (const auto& concept : recent_concepts) {
            concepts_str += concept + ",";
        }
        memory_data["recent_concepts"] = concepts_str;
        
        memory_system.save_memory(memory_data);
    }
    
    void load_persistent_state() {
        auto memory_data = memory_system.load_memory();
        if (!memory_data.empty()) {
            arousal = stod(memory_data["arousal"]);
            curiosity = stod(memory_data["curiosity"]);
            dopamine_level = stod(memory_data["dopamine_level"]);
            autonomous_mode = stoi(memory_data["autonomous_mode"]);
            interaction_count = stoi(memory_data["interaction_count"]);
            
            // Load recent concepts
            string concepts_str = memory_data["recent_concepts"];
            if (!concepts_str.empty()) {
                stringstream ss(concepts_str);
                string concept;
                while (getline(ss, concept, ',')) {
                    if (!concept.empty()) recent_concepts.push_back(concept);
                }
            }
            
            cout << "   💾 Loaded persistent state: " << memory_data.size() << " parameters" << endl;
        }
    }
};

// ==================== MAIN APPLICATION ====================
int main() {
    cout << "=== BIO LLM v4 AUTONOMOUS - Complete System ===" << endl;
    cout << "🧠 Meta-Cognitive Controller + Persistent Memory" << endl;
    cout << "💫 Autonomous Reflection Engine + SPOK Analysis" << endl;
    cout << "🌌 Dopamine-like Reinforcement + Coherence Monitoring" << endl;
    cout << "🚀 Autonomous Mode Capable + No External Dependencies" << endl;
    cout << "💾 Persistent Memory: bio_llm_memory.txt" << endl;
    
    BioLLMv4Autonomous bio_llm;
    
    cout << "\n💡 Contoh Interaksi:" << endl;
    cout << "1. \"aku merindukan masa depan yang bahagia\"" << endl;
    cout << "2. \"saya sedang belajar tentang makna hidup\"" << endl;
    cout << "3. \"system ini semakin pintar saja\"" << endl;
    cout << "4. \"autonomous\" - aktifkan mode otonom" << endl;
    cout << "5. \"reflect\" - paksa refleksi otonom" << endl;
    cout << "6. \"status\" - lihat kondisi sistem" << endl;
    cout << "7. \"exit\" - keluar dan simpan memori" << endl;
    cout << "8. [Kosong] - generate autonomous thought" << endl;
    cout << "=============================================" << endl;
    
    string input;
    while (true) {
        cout << "\n🧠 Input: ";
        if (!getline(cin, input) || input == "exit") break;
        
        if (input == "status") {
            bio_llm.print_system_status();
            continue;
        }
        
        if (input == "autonomous") {
            bio_llm.trigger_autonomous_mode();
            cout << "🤖 " << bio_llm.generate_autonomous_thought() << endl;
            continue;
        }
        
        if (input == "reflect") {
            bio_llm.force_autonomous_reflection();
            continue;
        }
        
        if (input.empty()) {
            // Generate autonomous thought when no input
            string autonomous_thought = bio_llm.generate_autonomous_thought();
            if (!autonomous_thought.empty()) {
                cout << "🤖 " << autonomous_thought << endl;
            } else {
                cout << "🤖 [System Ready] Masukkan input atau ketik 'autonomous' untuk mode otonom." << endl;
            }
            continue;
        }
        
        string response = bio_llm.process_input(input);
        cout << "🤖 BioLLM v4: " << response << endl;
    }
    
    cout << "👋 Sesi Bio LLM v4 Autonomous selesai! Memori disimpan." << endl;
    return 0;
}