Triadic Fusion of Cognitive, Functional, and Causal Dimensions for Explainable LLMs: The TAXAL Framework

2025-09-05

AI Summary

To address trust and accountability challenges arising from opaque reasoning, ambiguous planning logic, and unassessable systemic impacts when deploying agentic large language models (LLMs) in high-stakes domains, this paper proposes TAXAL—a novel explainability framework. TAXAL introduces the first triadic paradigm integrating cognitive, functional, and causal dimensions of explanation, underpinned by a three-dimensional weight alignment mechanism that jointly models reasoning paths, planning logic, and real-world socio-technical utility. It further incorporates post-hoc attribution, dialogue-based explanation interfaces, and explanation-aware prompting to enable concept-driven, interpretable system design. Empirical evaluation across legal, educational, and healthcare applications demonstrates that TAXAL significantly improves explanation quality, user trust, and system accountability—establishing a scalable theoretical and practical foundation for governance of high-risk AI systems.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Models (LLMs) are increasingly being deployed in high-risk domains where opacity, bias, and instability undermine trust and accountability. Traditional explainability methods, focused on surface outputs, do not capture the reasoning pathways, planning logic, and systemic impacts of agentic LLMs. We introduce TAXAL (Triadic Alignment for eXplainability in Agentic LLMs), a triadic fusion framework that unites three complementary dimensions: cognitive (user understanding), functional (practical utility), and causal (faithful reasoning). TAXAL provides a unified, role-sensitive foundation for designing, evaluating, and deploying explanations in diverse sociotechnical settings. Our analysis synthesizes existing methods, ranging from post-hoc attribution and dialogic interfaces to explanation-aware prompting, and situates them within the TAXAL triadic fusion model. We further demonstrate its applicability through case studies in law, education, healthcare, and public services, showing how explanation strategies adapt to institutional constraints and stakeholder roles. By combining conceptual clarity with design patterns and deployment pathways, TAXAL advances explainability as a technical and sociotechnical practice, supporting trustworthy and context-sensitive LLM applications in the era of agentic AI.

Problem

• Addressing opacity, bias, and instability in high-risk LLM deployments
• Capturing reasoning pathways and systemic impacts of agentic LLMs
• Unifying cognitive, functional, and causal dimensions for explainability

Innovation

• Triadic fusion framework for explainable LLMs
• Combines cognitive, functional, and causal dimensions
• Provides role-sensitive foundation for explanation design