DOUBT: Decoupled Object-level Understanding and Bridging via vMF-based Trustworthiness for Hallucination Detection in MLLMs

Kaiqi Chen, Yang Qin, Changhao He, Xi Peng, Peng Hu
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:16418-16441, 2026.

Abstract

Multimodal Large Language Models (MLLMs) frequently produce hallucinations (i.e., assertions that contradict the image or facts), undermining reliability in high-risk applications. Existing detection approaches typically feed images and texts jointly and estimate hallucination scores by measuring the consistency of model outputs. However, because the visual module often lags behind the language module in understanding and reasoning, MLLMs can repeatedly produce similar yet incorrect answers, yielding overestimated trustworthiness and missed detections. To address this, we propose a simple yet effective model-agnostic method, dubbed Decoupled Object-level Understanding and Bridging via vMF-based Trustworthiness (DOUBT). DOUBT first employs Object-level Understanding and Bridging (OUB), a two-step prompting scheme that decouples object recognition from relational reasoning by prompting the model to identify objects and then reason based on them. It further introduces a von Mises-Fisher (vMF)-based trustworthiness metric, which is more stable than semantic entropy metrics in small-sample settings. Extensive experiments and ablation studies on multiple benchmarks show that DOUBT consistently outperforms state-of-the-art baselines, demonstrating its robustness and generalizability for hallucination detection in MLLMs. The code is available at https://github.com/XLearning-SCU/2026-ICML-DOUBT.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-chen26dl, title = {{DOUBT}: Decoupled Object-level Understanding and Bridging via v{MF}-based Trustworthiness for Hallucination Detection in {MLLM}s}, author = {Chen, Kaiqi and Qin, Yang and He, Changhao and Peng, Xi and Hu, Peng}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {16418--16441}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/chen26dl/chen26dl.pdf}, url = {https://proceedings.mlr.press/v306/chen26dl.html}, abstract = {Multimodal Large Language Models (MLLMs) frequently produce hallucinations (i.e., assertions that contradict the image or facts), undermining reliability in high-risk applications. Existing detection approaches typically feed images and texts jointly and estimate hallucination scores by measuring the consistency of model outputs. However, because the visual module often lags behind the language module in understanding and reasoning, MLLMs can repeatedly produce similar yet incorrect answers, yielding overestimated trustworthiness and missed detections. To address this, we propose a simple yet effective model-agnostic method, dubbed Decoupled Object-level Understanding and Bridging via vMF-based Trustworthiness (DOUBT). DOUBT first employs Object-level Understanding and Bridging (OUB), a two-step prompting scheme that decouples object recognition from relational reasoning by prompting the model to identify objects and then reason based on them. It further introduces a von Mises-Fisher (vMF)-based trustworthiness metric, which is more stable than semantic entropy metrics in small-sample settings. Extensive experiments and ablation studies on multiple benchmarks show that DOUBT consistently outperforms state-of-the-art baselines, demonstrating its robustness and generalizability for hallucination detection in MLLMs. The code is available at https://github.com/XLearning-SCU/2026-ICML-DOUBT.} }
Endnote
%0 Conference Paper %T DOUBT: Decoupled Object-level Understanding and Bridging via vMF-based Trustworthiness for Hallucination Detection in MLLMs %A Kaiqi Chen %A Yang Qin %A Changhao He %A Xi Peng %A Peng Hu %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-chen26dl %I PMLR %P 16418--16441 %U https://proceedings.mlr.press/v306/chen26dl.html %V 306 %X Multimodal Large Language Models (MLLMs) frequently produce hallucinations (i.e., assertions that contradict the image or facts), undermining reliability in high-risk applications. Existing detection approaches typically feed images and texts jointly and estimate hallucination scores by measuring the consistency of model outputs. However, because the visual module often lags behind the language module in understanding and reasoning, MLLMs can repeatedly produce similar yet incorrect answers, yielding overestimated trustworthiness and missed detections. To address this, we propose a simple yet effective model-agnostic method, dubbed Decoupled Object-level Understanding and Bridging via vMF-based Trustworthiness (DOUBT). DOUBT first employs Object-level Understanding and Bridging (OUB), a two-step prompting scheme that decouples object recognition from relational reasoning by prompting the model to identify objects and then reason based on them. It further introduces a von Mises-Fisher (vMF)-based trustworthiness metric, which is more stable than semantic entropy metrics in small-sample settings. Extensive experiments and ablation studies on multiple benchmarks show that DOUBT consistently outperforms state-of-the-art baselines, demonstrating its robustness and generalizability for hallucination detection in MLLMs. The code is available at https://github.com/XLearning-SCU/2026-ICML-DOUBT.
APA
Chen, K., Qin, Y., He, C., Peng, X. & Hu, P.. (2026). DOUBT: Decoupled Object-level Understanding and Bridging via vMF-based Trustworthiness for Hallucination Detection in MLLMs. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:16418-16441 Available from https://proceedings.mlr.press/v306/chen26dl.html.

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