MedBench v5: A Dynamic, Process-Oriented, and Hallucination-Aware Benchmark for Clinical Multimodal Models
Quick Answer
This paper shows that MedBench v5 introduces a dynamic, process-oriented benchmark for clinical multimodal models, enhancing evaluation with 63 tasks across 14 cognitive dimensions and 4 agent environments, while addressing hallucination detection and process stability issues.
Quick Take
MedBench v5 introduces a dynamic, process-oriented benchmark for clinical multimodal models, enhancing evaluation with 63 tasks across 14 cognitive dimensions and 4 agent environments, while addressing hallucination detection and process stability issues. Experiments reveal that high task performance does not ensure reliability under stressors like contradiction detection and evidence delay.
Key Points
- Features a dual-dimensional framework with 63 tasks for comprehensive skill evaluation.
- Incorporates three stressors for analyzing model performance degradation.
- Utilizes a dynamic audit protocol to identify model-specific failure fingerprints.
- Monitors hallucination propagation across various stages of reasoning.
- Demonstrates that high overall performance does not guarantee process stability.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 24155v1 Announce Type: new Abstract: Existing medical AI benchmarks lack process visibility, atomic skill evaluation, and integrated hallucination detection. We introduce MedBench v5, a redesigned benchmark for clinical multimodal models (language, vision-language, and agent systems) that moves from static QA to dynamic, process-oriented evaluation.
MedBench v5 features: (1) a dual-dimensional framework combining Clinical Cognitive Responsiveness (14 sub-dimensions) and Medical Atomic Skills (4 agent environments), covering 63 tasks; (2) three switchable information-flow stressors (omission, contradiction, evidence delay) for factorized degradation analysis; (3) a dynamic process audit protocol with five reasoning nodes that produces model-specific failure fingerprints; (4) hallucination propagation monitoring across initiation, propagation, anchoring, and contradiction interaction-capturing silent hallucination.
Experiments on frontier models show that strong overall task performance does not guarantee process stability: stressors mainly disrupt contradiction detection, diagnosis updating, hallucination propagation, and contradiction-based self-correction, while final evidence grounding can remain superficially stable. MedBench v5 provides a unified infrastructure for capability profiling, controllable stress testing, process auditing, and hallucination trajectory analysis in clinical AI evaluation.
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