arXiv AI September 1, 2026 By Joonki Min, Chaeyun Kim, Hyungwook Choi, Yejin Kim, Kihyun Kim, Yohan Jo, Joonseok Lee

Fine-Grained Multi Image Object Hallucination Benchmark

The paper introduces MIOH, a fine‑grained benchmark for evaluating object hallucination in multimodal large language models (MLLMs) across multiple images. It assesses hallucination through four tasks—existence, counting, attribute, position—using three reasoning patterns (comprehensive, comparative, selective) and three adversarial pressures (visual context scale, perceptual difficulty, contextual bias). Evaluation of 29 models, including GPT‑5 and Gemini‑2.5‑Pro, shows distinct failure patterns, indicating that hallucination arises from integration‑stage limitations rather than just perceptual errors.

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