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HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models

arXiv cs.CV Multimodal & Generative Weilin Jin, Mingyu Wang, Wenbo Li, Haoyang Huang, Yifan Wu, Ying Li, Gang Huang, Zhonghai Wu 2026-07-23

TL;DR - HalluScope introduces a unified framework for detecting, classifying, and explaining hallucinations in multimodal large language models. Its fine-grained diagnoses can also help other models correct hallucinated outputs.

HalluScope-30K covers eight hallucination sources and five task categories.
HalluScope-4B and HalluScope-8B use a multi-granular joint reward to optimize detection and classification together.
The models achieve state-of-the-art results on MHALO and a fine-grained hallucination classification benchmark.
Diagnosis-driven feedback improves hallucination correction in Qwen3-VL-8B-Instruct and LLaVA-1.5-7B.

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