@inproceedings{xiang-etal-2026-regrap,
title = "{R}e{G}ra{P}-{LL}a{VA}: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant",
author = "Xiang, Yifan and
Zhang, Zhenxi and
Li, Bin and
Weng, Yixuan and
Gao, Bo and
Zhou, Shoujun and
He, Yangfan and
Yuan, Yilin and
Li, Keqin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.359/",
pages = "8210--8248",
ISBN = "979-8-89176-459-0",
abstract = "Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. Recent advances in personalized MLLMs enable effective capture of user-specific concepts, supporting both recognition of personalized concepts and contextual captioning. However, humans typically explore and reason over relations among objects and individuals, transcending surface-level information to achieve more personalized and contextual understanding. To this end, existing methods may face three main limitations: (1) Their training data lacks multi-object sets in which relations among objects are learnable, (2) Existing models often neglect the connections between different personalized concepts, thereby failing to perform reasoning over them, (3) Their experiments mainly focus on a single personalized concept, where evaluations are limited to recognition and captioning tasks. To address the limitations, (i) We present a new dataset named ReGraP, consisting of 120 sets of personalized knowledge. Each set includes images, Knowledge Graphs (KGs), and Chain-of-Thought Question-Answering (CoT QA) pairs derived from the KGs, enabling more structured and sophisticated reasoning pathways. (ii) We propose Reasoning enabled Graph-based Personalized Large Language and Vision Assistant (ReGraP-LLaVA), an MLLM trained with the corresponding KGs and CoT QA pairs, where soft and/or hard graph prompting methods are designed to align KGs within the model{'}s semantic space. (iii) We establish the ReGraP Benchmark, which contains diverse task types: Multiple-Choice, Fill-in-the-blank, True/False, and Descriptive questions in both open- and closed-ended settings. The proposed benchmark is designed to evaluate the relational reasoning and knowledge-connection capability of personalized MLLMs. We conduct experiments on the proposed ReGraP-LLaVA and other competitive MLLMs. Results show that the proposed model not only learns personalized knowledge but also performs relational reasoning in responses, achieving the SoTA performance compared with the competitive methods. All the codes and datasets are released at: https://anonymous.4open.science/r/ReGraP."
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<abstract>Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. Recent advances in personalized MLLMs enable effective capture of user-specific concepts, supporting both recognition of personalized concepts and contextual captioning. However, humans typically explore and reason over relations among objects and individuals, transcending surface-level information to achieve more personalized and contextual understanding. To this end, existing methods may face three main limitations: (1) Their training data lacks multi-object sets in which relations among objects are learnable, (2) Existing models often neglect the connections between different personalized concepts, thereby failing to perform reasoning over them, (3) Their experiments mainly focus on a single personalized concept, where evaluations are limited to recognition and captioning tasks. To address the limitations, (i) We present a new dataset named ReGraP, consisting of 120 sets of personalized knowledge. Each set includes images, Knowledge Graphs (KGs), and Chain-of-Thought Question-Answering (CoT QA) pairs derived from the KGs, enabling more structured and sophisticated reasoning pathways. (ii) We propose Reasoning enabled Graph-based Personalized Large Language and Vision Assistant (ReGraP-LLaVA), an MLLM trained with the corresponding KGs and CoT QA pairs, where soft and/or hard graph prompting methods are designed to align KGs within the model’s semantic space. (iii) We establish the ReGraP Benchmark, which contains diverse task types: Multiple-Choice, Fill-in-the-blank, True/False, and Descriptive questions in both open- and closed-ended settings. The proposed benchmark is designed to evaluate the relational reasoning and knowledge-connection capability of personalized MLLMs. We conduct experiments on the proposed ReGraP-LLaVA and other competitive MLLMs. Results show that the proposed model not only learns personalized knowledge but also performs relational reasoning in responses, achieving the SoTA performance compared with the competitive methods. All the codes and datasets are released at: https://anonymous.4open.science/r/ReGraP.</abstract>
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%0 Conference Proceedings
%T ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant
%A Xiang, Yifan
%A Zhang, Zhenxi
%A Li, Bin
%A Weng, Yixuan
%A Gao, Bo
%A Zhou, Shoujun
%A He, Yangfan
%A Yuan, Yilin
%A Li, Keqin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xiang-etal-2026-regrap
%X Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. Recent advances in personalized MLLMs enable effective capture of user-specific concepts, supporting both recognition of personalized concepts and contextual captioning. However, humans typically explore and reason over relations among objects and individuals, transcending surface-level information to achieve more personalized and contextual understanding. To this end, existing methods may face three main limitations: (1) Their training data lacks multi-object sets in which relations among objects are learnable, (2) Existing models often neglect the connections between different personalized concepts, thereby failing to perform reasoning over them, (3) Their experiments mainly focus on a single personalized concept, where evaluations are limited to recognition and captioning tasks. To address the limitations, (i) We present a new dataset named ReGraP, consisting of 120 sets of personalized knowledge. Each set includes images, Knowledge Graphs (KGs), and Chain-of-Thought Question-Answering (CoT QA) pairs derived from the KGs, enabling more structured and sophisticated reasoning pathways. (ii) We propose Reasoning enabled Graph-based Personalized Large Language and Vision Assistant (ReGraP-LLaVA), an MLLM trained with the corresponding KGs and CoT QA pairs, where soft and/or hard graph prompting methods are designed to align KGs within the model’s semantic space. (iii) We establish the ReGraP Benchmark, which contains diverse task types: Multiple-Choice, Fill-in-the-blank, True/False, and Descriptive questions in both open- and closed-ended settings. The proposed benchmark is designed to evaluate the relational reasoning and knowledge-connection capability of personalized MLLMs. We conduct experiments on the proposed ReGraP-LLaVA and other competitive MLLMs. Results show that the proposed model not only learns personalized knowledge but also performs relational reasoning in responses, achieving the SoTA performance compared with the competitive methods. All the codes and datasets are released at: https://anonymous.4open.science/r/ReGraP.
%U https://aclanthology.org/2026.emnlp-main.359/
%P 8210-8248
Markdown (Informal)
[ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant](https://aclanthology.org/2026.emnlp-main.359/) (Xiang et al., EMNLP 2026)
ACL
- Yifan Xiang, Zhenxi Zhang, Bin Li, Yixuan Weng, Bo Gao, Shoujun Zhou, Yangfan He, Yilin Yuan, and Keqin Li. 2026. ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8210–8248, Budapest, Hungary. Association for Computational Linguistics.