arXiv:2602.18905v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their decision-making processes remain difficult to interpret. Existing explanation methods often lack trustworthy structural insight and are limited to single-instance analysis, failing to reveal reasoning stability and systematic failure mechanisms. To address these limitations, we propose the Trustworthy Unified Explanation Framework (TRUE), which integrates executable reasoning verification, feasible-region directed acyclic graph (DAG) model
The increasing deployment of large language models in critical applications necessitates robust explainability frameworks to build trust and address regulatory concerns.
A strategic reader should care about advancements in LLM explainability as it directly impacts AI adoption, regulatory compliance, and the development of truly trustworthy autonomous systems.
The proposed TRUE framework offers a unified approach to LLM explanation that aims to provide structural insights and address limitations of existing single-instance methods, potentially improving debugging and auditing of AI systems.
- · AI developers
- · Auditors and regulators
- · Enterprises deploying LLMs
- · Researchers in AI safety
- · Opaque AI systems
- · Companies with poor AI explainability practices
Increased trust and accelerated adoption of large language models across sensitive domains.
New standards and regulatory requirements for LLM explainability emerge, driving further innovation in the field.
Explainable AI becomes a core component of general-purpose AI agent design, leading to more robust and reliable autonomous systems.
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Read at arXiv cs.LG