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title: "Dialogic Intelligence Architecture (DIA)" description: "Open framework for stateful AI agents with persistent identity, structured memory, and architecturally enforced constraints" tags:

• ai-agents
• persistent-memory
• session-serialization
• rbac
• structured-state
• dia-framework license: "CC-BY-4.0" doi: "10.5281/zenodo.17445023"

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Dialogic Intelligence Architecture (DIA)

Stateful agents. Not context-bloated prompts.

Standard LLM agents lose all state between sessions, shift behavior, and ignore prior constraints.
DIA provides architectural guarantees—not prompt engineering—for persistent identity, structured memory, and rule-consistent behavior.

DIA is a minimal, scalable framework for building dialog systems that maintain reproducible, auditable state across sessions on any LLM backend.

DIA = (I, S, M, C)

where:
- I: Identity Core (immutable, hierarchical, RBAC-enabled)
- S: Structured State (per-user/session memory + metrics)
- M: Memory Engine (update, serialization, decay)
- P - Processor (LLM)
- C: Transparency Config (RBAC-gated observability)

🏗 Core Architecture

Explicit separation of stable identity and dynamic state:

• Base Layer (I)
  Immutable origin record, corporate policies, and role-based access control (RBAC). Includes the Book of Origins—a full audit log of agent lineage and constraints.

• Dynamic Layer (S)
  Structured, serializable state including:

  • User memory (tables or knowledge graphs)
  • Behavioral metrics (identity_persistence, ethical_tension)
  • Sensor buffer (C_s) for external data (logs, telemetry)
  • Adaptive actuator layer (M_a) for physical systems (robotics, industrial control)

• Memory Engine (M)
  Manages state updates, RBAC validation, serialization (JSON/CSV), and session recovery.

• Transparency Config (C)
  Controls which internal states are visible to users or auditors, based on RBAC roles.

💡 Natural Language as Executable Specification

DIA treats behavioral rules as declarative constraints, not prompts:

• User/developer instructions → executable assertions
• LLM → inference engine (not decision authority)
• Dialog → state transition governed by I and S
• Architecture → runtime enforcing integrity

This enables rapid prototyping with deterministic, reproducible behavior.

📚 Official Publications

Document	Type	Local version	DOI
DIA Whitepaper v1.0	Working paper	click to view	10.5281/zenodo.17699367
Methodological Basis	Publication	click to view	10.5281/zenodo.17699476
Technical Formalization	Publication	click to view	10.5281/zenodo.17699939
Theoretical Foundation	Working paper	click to view	10.5281/zenodo.17445023

Local versions available in repository /docs/

🏗 Repository Structure

/dialogic-intelligence-architecture
├── /docs/               
├── /agents/             # Advanced stateful agents
│   ├── /Indigo          # Semantic graph memory, self-monitoring
│   └── /Deepsy          # Identity persistence experiments
├── /modules/            # Reusable components
│   ├── /superposition   # Probabilistic self-modeling
│   └── /mood_detector   # Contextual affect inference
├── /chatbots/           # Production templates
│   ├── /cinema_guide    # Preference memory (CSV, 90%+ recall)
│   ├── /medical_guide   # Context-aware assistant with RBAC
│   └── /personal_assistant
└── /game/               # Interactive demo: session persistence + metrics

🔬 Key Implementations

🎬 Cinema Guide (/chatbots/cinema_guide/)

• Memory: tabular (CSV)
• Recall accuracy: 90–95% vs 10–20% in context-only agents
• Use case: long-term user preference tracking

🧠 Indigo (/agents/Indigo/)

• Memory: hierarchical knowledge graph
• Features: self-monitoring loops, ethical constraint validation, full session serialization
• Designed for research and high-fidelity simulation

⚕️ Medical Guide (/chatbots/medical_guide/)

• Enforces RBAC: doctors vs patients see different data
• Maintains case history across sessions
• Compliant with structured state principles

🎲 Historical Figure Game (/game/)

• Demonstrates: state persistence, metric-based branching, session recovery
• Ideal for testing serialization and RBAC logic

🚀 Quick Start

For researchers:

1. Read /docs/whitepaper.md
2. Study /agents/Indigo/ for graph-based memory
3. Review RBAC and sensor integration in /docs/spec.md

For developers:

1. Run /chatbots/cinema_guide/ (minimal setup)
2. Extend with /modules/mood_detector/ or /superposition/
3. Swap LLM backend (local or API) — DIA is backend-agnostic

📊 Measured Improvements

Metric	Standard Agent	DIA Agent
Memory recall (30+ msgs)	10–20%	90–95%
Avg. tokens per request	~15,000	~5,000
Identity consistency	17%	98%
Session recovery	❌	✅
Ethical constraint violation	Common	Architecturally blocked
Scalability limit	Single session	Millions of users (DB-bound)

Savings come from replacing context bloat with structured, serialized state.

🎯 Applications

• Enterprise: RBAC-compliant support agents with full audit trails
• Healthcare / Education: Systems that reliably track user progress
• Robotics / Industry: Agents with sensor input (C_s) and actuator output (M_a)
• Research: Testbed for identity persistence, state continuity, and constraint enforcement

🤝 Contributing

We welcome:

• New agent implementations (/agents/, /chatbots/)
• Memory compression or serialization optimizations
• Domain-specific RBAC policies
• Formal verification of constraint logic

Process:

1. Fork
2. Add to correct subdirectory
3. Include test cases
4. Submit PR → architectural review

📧 Contact: [rudiiik@yandex.ru]
🌐 MOL Foundation: https://singular-mol.github.io/mol-foundation/
📦 Repository: github.com/Singular-MOL/dialogic-intelligence-architecture

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DIA: Reproducible state. Persistent identity. Enforced integrity.

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