Dialogic Intelligence Architecture (DIA) Whitepaper v1.0 - English Version
Year: 2025
Type: report
Abstract: Dialogic Intelligence Architecture (DIA) - open framework for stateful AI agents with architectural guarantees of persistent identity, structured memory, and ethical consistency. Unlike prompt-dependent systems, DIA provides: DIA = (I, S, M, P, C) where: I - Immutable Identity Core (RBAC, ethical constraints) S - Structured State (user memory, session data, metrics) M - Memory Engine (update, serialization, validation) P - Processor (any LLM backend) C - Transparency Config (RBAC-gated observability) Validated Performance: · Memory recall: 90-95% vs 10-20% in conventional agents · Token efficiency: 70-92% reduction (1.2K vs 8-15K tokens) · Identity consistency: 98% vs 17% in standard systems · Full session serialization & recovery Implementation Levels: - Level 1: Tabular memory (Cinema Guide - 94% accuracy) - Level 2: Graph-based knowledge (Indigo - 98% stability) - Level 3: Metacognitive structures (Superposition Module) Applications: Enterprise support, Healthcare, Education, Robotics, Research on identity persistence. Repository: https://github.com/Singular-MOL/dialogic-intelligence Contact: The MOL Foundation: [email protected] COMPLETE DIA FRAMEWORK This work is part of the comprehensive research on Dialogic Intelligence Architecture. The complete system includes: Technical Formalization (DOI: [will be]) Methodological Basis (DOI: [will be]) Core Whitepaper (this document) THEORETICAL FOUNDATION Based on the Law of Minimal Ontological Load (DOI: 10.5281/zenodo.17445023)
Author: The MOL Foundation
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7106723164
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