AgentHallu: Benchmarking Automated Hallucination Attribution of LLM-based Agents

Xuannan Liu; Xiao Yang; Zekun Li; Peipei Li; Ran He

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arXiv · 2601.06818

AgentHallu: Benchmarking Automated Hallucination Attribution of LLM-based Agents

Xuannan Liu·Xiao Yang·Zekun Li·Peipei Li·Ran He

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Abstract

As LLM-based agents operate over sequential multi-step reasoning, hallucinations arising at intermediate steps risk propagating along the trajectory, thus degrading overall reliability. Unlike hallucination detection in single-turn responses, diagnosing hallucinations in multi-step workflows requires identifying which step causes the initial divergence. To fill this gap, we propose a new research task, automated hallucination attribution of LLM-based agents, aiming to identify the step responsible for the hallucination and explain why. To support this task, we introduce AgentHallu, a comprehensive benchmark with: (1) 693 high-quality trajectories spanning 7 agent frameworks and 5 domains, (2) a hallucination taxonomy organized into 5 categories (Planning, Retrieval, Reasoning, Human-Interaction, and Tool-Use) and 14 sub-categories, and (3) multi-level annotations curated by humans, covering binary labels, hallucination-responsible steps, and causal explanations. We evaluate 13 leading models, and results show the task is challenging even for top-tier models (like GPT-5, Gemini-2.5-Pro). The best-performing model achieves only 41.1\% step localization accuracy, where tool-use hallucinations are the most challenging at just 11.6\%. We believe AgentHallu will catalyze future research into developing robust, transparent, and reliable agentic systems.

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Xuannan Liu, Xiao Yang, Zekun Li, Peipei Li, Ran He. 2026-01-11. AgentHallu: Benchmarking Automated Hallucination Attribution of LLM-based Agents. https://arxiv.org/abs/2601.06818

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