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A Unified Evaluation Framework for Trustworthy Large Language Models, Agentic AI, and Multimodal Systems

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Benchmark scores alone provide an incomplete basis for assessing the trustworthiness of modern artificial intelligence systems. Large language models (LLMs), agentic systems, and multimodal models (MLLMs) require different forms of assessment, yet their evaluation evidence must remain interpretable for development and oversight. We propose a unified framework that connects output-level, trajectory-level, and cross-modal assessment through eight trustworthiness dimensions: capability, robustness, safety, fairness, transparency, governance, oversight, and efficiency. The framework preserves syst

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