ClinHallu: A New Benchmark for Diagnosing Hallucination Sources in Medical AI Reasoning
By
[Submitted on 12 Jun 2026]
Right out the toaster. Reliable, with some real depth.
Summary
This paper introduces ClinHallu, a benchmark designed to diagnose stage-wise hallucinations in medical multimodal large language models (MLLMs). Unlike existing benchmarks that focus on data collection, ClinHallu identifies where hallucinations originate in the reasoning process—whether from visual misrecognition, incorrect medical knowledge recall, or flawed reasoning integration. The benchmark contains 7,031 validated instances, each with a structured reasoning trace decomposed into Visual Recognition, Knowledge Recall, and Reasoning Integration. It uses stage-replacement interventions to measure how correcting specific stages affects final answers, and shows that trace-supervised fine-tuning can reduce stage-wise hallucinations. The benchmark is publicly available on GitHub.
Key quotes
· 4 pulledBuilding trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support.
Existing medical hallucination benchmarks mainly focus on data collection, but often ignore where hallucinations originate within the reasoning process.
Hallucination sources vary across samples: errors may arise from visual misrecognition, incorrect medical knowledge recall, or flawed reasoning integration.
ClinHallu provides a fine-grained hallucination testbed for diagnosing and mitigating reasoning failures in medical MLLMs.
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