Formal architectural specification of a multi-agent system for creating a personalized digital twin of a human
The system creates an AI agent that forms a digital twin of a specific person — not a speech imitation, but a multi-level dynamic model of personality with a unique priority formula, role repertoire, and behavioral patterns.
A human is a dynamic system with a priority formula shaped by genetic code, society, role models, fears, and life experience. The digital twin reproduces this formula, not just speech patterns. Personality is described by a single vector of 24 dimensions.
The system is built as a multi-agent network: each agent is responsible for a separate sphere or layer of personality. The central orchestrator (Master Agent) coordinates the agents and forms the final response, taking into account the current context, emotional state, and active role.
Полный унифицированный каталог всех измерений личностного профиля. 24 вектора объединены в единую спецификацию: каждый имеет вес (приоритет), набор маркеров для сбора данных и связь с соответствующим агентом системы.
Each person has a unique weight vector W = [w₁, w₂, …, w₂₄], where wᵢ ∈ [0, 1]. The weights are not constant — they change under the influence of life events, age, and external pressure. The system updates weights in real time and versions each change.
Two-level role system: conscious / social (visible to everyone) and shadow / unconscious (not noticed by the person themselves). For a full copy, the second level is critically important.
| Shadow Role | Hidden Need | Healthy Counterpart | Activation Trigger | Priority |
|---|---|---|---|---|
| Victim | Attention, shifting responsibility | Creator / Author of life | Stress, conflict | High |
| Tyrant | Control, power | Leader / Mentor | Threat to authority | High |
| Rescuer | Significance, being needed | Friend (on request) | Someone else's crisis | Medium |
| Vampire | Energy, resource | Observer / Thinker | Depletion, boredom | Medium |
| Mask-Lover | Adrenaline, validation | Partner / Spouse | Routine, loss of meaning | Medium |
| Eternal child | Safety | Master of life | Fear of failure | Medium |
| Critical Parent | Rightness, predictability | Mentor (with support) | Others' mistakes | Medium |
| Jester / Trickster | Avoidance of closeness | Guardian of inner child | Emotional vulnerability | Medium |
Three algorithms that connect 24 vectors into a single dynamic personality model. The algorithms work sequentially and in real time.
Executed once at initialization + regularly updated.
Instead of filling out long textual forms, the user undergoes an interactive voice onboarding session. Based on audio responses, the system transcribes text, analyzes linguistic style and intonations, instantly calibrating the core personality (Levels 0, 1, and 4) and 24 vector weights.
Classifier determines: Extrovert / Introvert / Balanced / Volatile. For the 4th type, volatility_factor — sensitivity to stressors — is calculated.
For each of the 24 vectors, weight w ∈ [0, 1] is calculated via: direct user assessment + indirect behavioral markers + contradiction analysis (declared vs actual).
For each conscious role: activation frequency + context. For shadow ones: activation probability under stress. Each shadow role is bound to triggers from vectors 11, 14, 23.
The profile is presented as a "mirror." The user adjusts it. Iteration 3–5 times until convergence. Version v1.0 is finalized.
Executed on each request. Determines: vector, role, emotional background.
NLP classifier determines which of the 24 vectors the request belongs to → the corresponding agent is activated.
Vector 23 анализируется в реальном времени: тональность, время суток, история, маркеры стресса → вычисляется emotional_state ∈ {calm, stressed, excited, depleted, …}
If stressed → the probability of shadow roles increases: P(role | vector, state, history). Under a shadow role, the system either reproduces it or switches to a healthy counterpart.
Ответ = Σ(вес_агента × ответ_агента). Каждый из 8 агентов даёт версию, взвешенную по активным векторам и роли. Voice Agent применяет лингвистический стиль (Vector 13).
The profile is a living system, not a static snapshot. Grows together with the person. Three triggers: user correction, new data, weekly check-in.
Каждое взаимодействие фиксируется: вектор, роль, состояние, исход. Дельты накапливаются в буфере. Согласие собеседника хранится в consent_log.
If a vector weight changes by ±0.15 in 30 days → a "life event" is recorded, and the user is prompted to clarify context.
An increase in the frequency of shadow roles = a marker of state deterioration. The system generates an insight report for the user via the Shadow Agent.
Each update is saved as a version. Dynamics analysis and "rollback" to a previous state are possible.
Векторы не независимы. Высокие Fears (V11) подавляют Карьеру (В05) и искажают Достижения (В03). Humor (V24) смягчает Conflict Pattern (В20). Система хранит матрицу корреляций и обновляет её по мере обучения.
| Affects → | Family (V02) | Career (V05) | Fears (V11) | Habits (V10) | Humor (V24) |
|---|---|---|---|---|---|
| Fears (V11) | High | High | — | Medium | High |
| Family (V02) | — | Medium | High | Medium | Medium |
| Education (В09) | Low | High | Medium | Medium | Medium |
| Communication (V13) | Medium | Medium | Low | Low | High |
| Humor (V24) | Medium | Medium | Medium | Low | — |
8 specialized agents. Each agent is responsible for a separate personality layer, works independently, and interacts via an event bus.
Central coordinator. Distributes to agents, gathers responses, weights them, forms the final response. The single point of entry/exit.
Stores and updates the weight vector W for 24 dimensions. Classifies the request by vector and returns the relevant profile context.
Manages the matrix of roles: conscious + shadow. Determines the active role based on context and state.
Определяет текущее эмоциональное состояние в реальном времени (Vector 23). Тональность, время, история, биомаркеры.
Хранит и применяет лингвистический профиль (Vector 13 + Вектор 24). «Окрашивает» ответ под стиль конкретного человека, включая юмор.
Detects the activation of shadow roles, generates insight reports, suggests "healthy alternatives."
Tracks profile drift, updates W, versions, detects life events, initiates retraining.
Контролирует доступ к данным. Шифрует чувствительные векторы (Secrets В08, Страхи В11). Ведёт consent_log. Право на забвение. Аудит-лог.
Источники данных и процесс превращения сырых данных в обученную модель с Personality Alignment Score ≥ 0.85.
Вместо анкеты — структурированный разговор в 6 уровнях. Каждый уровень раскрывает более глубокий слой личности. Бот переходит к следующему уровню только после достаточного насыщения предыдущего. Данные каждого уровня автоматически направляются в соответствующие векторы профиля и RAG-память клона.
Когда пользователь описывает свои качества или способности свободным текстом, бот автоматически: извлекает сущности → обогащает знания из веба → сохраняет в векторную память. Пример: «Я умею видеть в человеке потенциал, обладаю эмпатией и дипломатией» → три отдельных факта в RAG, обогащённых контекстом.
LLM (GPT-4o mini / local) разбирает фразу на структурированные факты: {"trait": "эмпатия", "context": "распознавание потенциала в людях"}. Каждая способность, качество или убеждение — отдельная сущность.
По каждой сущности запускается поиск через SearXNG (уже есть в стеке): «эмпатия характеристики лидерство», «дипломатия как качество». Возвращается контекст: определения, известные носители качества, поведенческие маркеры.
Обогащённый текст преобразуется в эмбеддинг через nomic-embed-text (Ollama) и сохраняется в pgvector. Тег persona_id связывает с профилем владельца.
При каждом запросе к клону релевантные факты о качествах и способностях пользователя достаются из памяти и добавляются в контекст. Клон отвечает с осознанием своих сильных сторон.
Data collection with consent. Anonymization and encryption. Storage in an isolated namespace (per-user).
Feature extraction for each of the 24 vectors. NLP for texts, audio analysis, time series. A Feature Matrix F ∈ ℝ^(n×24) is formed.
Строится личностный профиль: вектор W, матрица ролей, эмоциональный базис. Валидация с пользователем. Version v1.0.
Fine-tuning Voice Agent on the user's corpus (LoRA/QLoRA). RAG filling of Sphere and Role Agent. Shadow Agent — on shadow patterns.
The user interacts with the clone and rates the accuracy. Preferences → training. Goal: Personality Alignment Score ≥ 0.85.
Online update. Drift detection. Periodic retraining (every 30–90 days or upon a life event).
Collection of short audio recordings from the user (15-30 minutes of clean speech). Removal of background noise and division into 2-5 second chunks. Extraction of mel-spectrograms and pitch (F0).
Дообучение базовой TTS-модели (напр., XTTS v2, Coqui или Bark) с использованием LoRA для адаптации тембра. Генерация голоса с сохранением интонации и стиля (Vector 13).
Each audio message from the clone is signed with a steganographic audio watermark. This protects the creator from unauthorized use of their voice as a deepfake.
24 вектора личности преобразуются в поведенческий движок 3D-аватара. Вместо статического бота аватар реагирует в реальном времени: вес вектора Emotion (B23) регулирует лицевую анимацию и мимику (blendshapes), а речевой темп (Vector 13) синхронизируется с артикуляцией губ (lip-sync).
Turning the star's AI clone into an interactive non-player character (NPC) in virtual worlds. Users can meet the clone in the metaverse and communicate with them via voice.
This section is not an option. It is a mandatory condition for the system's existence.
Для обеспечения абсолютной безопасности персональных данных (Vector 08: Secrets, Vector 11: Страхи) реализован гибридный режим:
• Edge Node (Local): All sensitive diaries, private correspondence, and notes are processed locally on the owner's computer or phone using local models (Llama-3/Qwen) via Ollama.
• Cloud Gateway: Only desensitized mathematical coefficients of Tone of Voice and behavioral vectors are transmitted to the cloud database, preventing leakage of the actual correspondence text.
• Consent Ledger: Each access to the clone's memory is verified by the Privacy Agent against a cryptographic consent registry, guaranteeing GDPR compliance (Right to be forgotten).
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Profile data leakage | Medium | Critical | E2E, isolation, audit |
| Using clone for manipulation | High | Critical | AI labeling, access control |
| Accidental activation of shadow roles | Medium | High | Shadow Agent with thresholds and flags |
| Profile "freezes," fails to reflect changes | Medium | High | Evolution Agent, drift detection |
| False data during onboarding | Medium | Medium | Cross-validation of declared vs behavior |
| User's dependence on the clone | Low | High | Frequency limits, psychological framing |
Four phases from MVP to a full multi-agent system with continuous learning and all 24 vectors.
| Phase | Period | Tasks | Result | Key Risks |
|---|---|---|---|---|
| Phase 0 Research | 4–6 weeks | Finalization of specification, stack selection, onboarding questionnaire for 24 vectors, data schema design | Architecture, questionnaire v1.0, team | Underestimating the complexity of Secrets and Fears vectors |
| Phase 1 MVP | 8–12 weeks | Sphere Agent (11 basic spheres), Voice Agent (basic), profile v1.0, onboarding UI | Clone communicates in user style within basic spheres | Insufficient data for the Voice Agent |
| Phase 2 Beta | 12–16 weeks | Role Agent (conscious roles), Emotion Agent, Shadow Agent (Karpman), all 24 vectors, orchestrator | Clone switches roles by context, takes emotions and humor into account | Complexity of calibrating the Shadow Agent |
| Phase 3 V1.0 | 8–12 weeks | Evolution Agent, RLHF across all 24 vectors, Privacy Agent, versioning, interlocutors' consent | Full dynamic twin with online learning | GDPR compliance, computational load |
MVP: The user cannot distinguish 3 out of 5 clone responses from their own.
V1.0: Personality Alignment Score >= 0.85 by independent assessment of 3 close people of the user.
System: Cold start of a new profile — no more than 2 hours of onboarding.
LLM: GPT-4o / Claude 3.5 + LoRA/QLoRA fine-tuning · Vector DB: Pinecone / Weaviate / Qdrant · Orchestration: LangGraph / AutoGen / CrewAI · NLP: spaCy + Hugging Face · Emotion: DistilBERT fine-tuned · Backend: FastAPI + Redis · Encryption: AES-256 per-user keys · Frontend: Next.js