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    <abstract>Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite advancements in Multimodal Large Language Models (MLLMs), current systems often fail to precisely ground the spatial semantics of pointing. Instead, they rely on spurious correlations with visual proximity or object saliency—a phenomenon we term “Referential Hallucination.” To address this gap, we introduce EgoPoint-Bench, a comprehensive question-answering benchmark designed to evaluate and enhance multimodal pointing reasoning in egocentric views. Comprising over 11k high-fidelity simulated and real-world samples, the benchmark spans five evaluation dimensions and three levels of referential complexity. Extensive experiments demonstrate that while state-of-the-art proprietary and open-source models struggle with egocentric pointing, models fine-tuned on our synthetic data achieve significant performance gains and robust Sim-to-Real generalization. This work highlights the importance of spatially-aware supervision and offers a scalable path toward precise egocentric AI assistants. The project website is available at https://guyyyug.github.io/EgoPoint-Bench/.</abstract>
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%0 Conference Proceedings
%T Do MLLMs Understand Pointing? Benchmarking and Enhancing Referential Reasoning in Egocentric Vision
%A Li, Chentao
%A Gao, Zirui
%A Gao, Mingze
%A Ren, Yinglian
%A Feng, Jianjiang
%A Zhou, Jie
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-mllms
%X Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite advancements in Multimodal Large Language Models (MLLMs), current systems often fail to precisely ground the spatial semantics of pointing. Instead, they rely on spurious correlations with visual proximity or object saliency—a phenomenon we term “Referential Hallucination.” To address this gap, we introduce EgoPoint-Bench, a comprehensive question-answering benchmark designed to evaluate and enhance multimodal pointing reasoning in egocentric views. Comprising over 11k high-fidelity simulated and real-world samples, the benchmark spans five evaluation dimensions and three levels of referential complexity. Extensive experiments demonstrate that while state-of-the-art proprietary and open-source models struggle with egocentric pointing, models fine-tuned on our synthetic data achieve significant performance gains and robust Sim-to-Real generalization. This work highlights the importance of spatially-aware supervision and offers a scalable path toward precise egocentric AI assistants. The project website is available at https://guyyyug.github.io/EgoPoint-Bench/.
%U https://aclanthology.org/2026.findings-acl.838/
%P 17000-17019

Markdown (Informal)

[Do MLLMs Understand Pointing? Benchmarking and Enhancing Referential Reasoning in Egocentric Vision](https://aclanthology.org/2026.findings-acl.838/) (Li et al., Findings 2026)

• Do MLLMs Understand Pointing? Benchmarking and Enhancing Referential Reasoning in Egocentric Vision (Li et al., Findings 2026)

ACL

• Chentao Li, Zirui Gao, Mingze Gao, Yinglian Ren, Jianjiang Feng, and Jie Zhou. 2026. Do MLLMs Understand Pointing? Benchmarking and Enhancing Referential Reasoning in Egocentric Vision. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17000–17019, San Diego, California, United States. Association for Computational Linguistics.