What question did this study set out to answer?

This research aims to develop a comprehensive framework for causal AI using relational reasoning.

April 3, 20260 citationsOpen Access

PRD-AGI 2.0.2: Complete Application Suite – 12 Domain‑Specific AI Systems Based on Relational Causal Reasoning

Key Points

This research aims to develop a comprehensive framework for causal AI using relational reasoning.
Development of a relational tensor formalism to encode causal relations.
Creation of 12 domain-specific applications that leverage the PRD framework.
Integration of common core functionalities across applications for explainability and intervention planning.
Successful instantiation of causal reasoning in applications for mobile, software development, medical diagnosis, and more.
Demonstrated ability of applications to perform causal analysis and generate counterfactual insights.
Open-source release of the framework encourages further research and practical deployment.

Abstract

The Pattana‑Relational Dynamics (PRD) framework is a causal AI architecture built on a relational tensor formalism: \( R(x, y) = C, W, L, T, U, D \), where each component encodes a distinct causal relation (Causality, Weight, Latent, Temporal, Uncertainty, Dependence). This framework enables machines to reason about cause and effect rather than merely correlating data. This package contains the complete PRD-AGI 2.0.2 suite, comprising 12 domain‑specific applications that instantiate the relational causal engine for real‑world tasks. All applications share the same underlying causal reasoning core, ensuring explainability, counterfactual reasoning, and intervention planning. **Applications included:** 1. **PRD-AGI Mobile App** – On‑device causal reasoning for mobile assistants.2. **PRD-AGI Software Dev AI** – Causal code analysis, refactoring, and bug prediction.3. **PRD-AGI Private LLM** – Locally hosted large language model with causal attention.4. **PRD-AGI Causal Engine** – Core inference engine for causal graph learning and counterfactual simulation.5. **PRD-Diagnosis AI** – Medical diagnosis assistant using causal symptom‑disease models.6. **PRD-Legal AI** – Case law analysis, legal argument causality, and precedent reasoning.7. **PRD-Financial AI** – Causal market modeling, risk assessment, and portfolio optimization.8. **PRD-Education Tutor** – Adaptive tutoring that identifies causal gaps in student knowledge.9. **PRD-AGI Orchestrator** – Multi‑agent coordination with causal dependency tracking.10. **PRD-Knowledge Graph Builder** – Automated construction of causal knowledge graphs from text.11. **PRD-Cybersecurity AI** – Threat detection via causal inference on system logs.12. **PRD-Mental Health Companion** – Conversational agent for mental well‑being, using causal reasoning to identify triggers and coping strategies. All applications are built on a unified codebase, written in Python and C++, with APIs for integration. The framework is designed for extensibility, and each application can be deployed as a standalone service or as part of a larger causal AI ecosystem. This release (2.0.2) includes final stable versions of all 12 apps, comprehensive documentation, and example datasets. The source code is open and provided under the CC BY 4.0 license to encourage research and deployment. For each application, a detailed user guide and technical specification are included in the package.

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