PointArena: Probing Multimodal Grounding Through Language-Guided
  Pointing
Pointing serves as a fundamental and intuitive mechanism for grounding
language within visual contexts, with applications spanning robotics, assistive
technologies, and interactive AI systems. While recent multimodal models have
started to support pointing capabilities, existing benchmarks typically focus
only on referential object localization tasks. We introduce PointArena, a
comprehensive platform for evaluating multimodal pointing across diverse
reasoning scenarios. PointArena comprises three components: (1) Point-Bench, a
curated dataset containing approximately 1,000 pointing tasks across five
reasoning categories; (2) Point-Battle, an interactive, web-based arena
facilitating blind, pairwise model comparisons, which has already gathered over
4,500 anonymized votes; and (3) Point-Act, a real-world robotic manipulation
system allowing users to directly evaluate multimodal model pointing
capabilities in practical settings. We conducted extensive evaluations of both
state-of-the-art open-source and proprietary multimodal models. Results
indicate that Molmo-72B consistently outperforms other models, though
proprietary models increasingly demonstrate comparable performance.
Additionally, we find that supervised training specifically targeting pointing
tasks significantly enhances model performance. Across our multi-stage
evaluation pipeline, we also observe strong correlations, underscoring the
critical role of precise pointing capabilities in enabling multimodal models to
effectively bridge abstract reasoning with concrete, real-world actions.
Project page: https://pointarena.github.io/
6826dce4682e62074d0ed686
Long Cheng
6826dce4682e62074d0ed687
Jiafei Duan
6826dce4682e62074d0ed688
Yi Ru Wang
admin_assigned
2025-05-16T14:09:18.022Z
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yiru wang
user
yiruwang0324
6826dce4682e62074d0ed689
Haoquan Fang
admin_assigned
2025-05-16T14:09:12.281Z
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Haoquan Fang
user
hqfang
6826dce4682e62074d0ed68a
Boyang Li
admin_assigned
2025-05-16T14:09:06.599Z
635d3e57abb916fca6af907d
/avatars/dc4db02e37eddd6b14a058349b218b44.svg
Albert Li
user
boyangli
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Yushan Huang
admin_assigned
2025-05-16T14:08:59.603Z
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Yu Shan Huang
user
hys990996
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Elvis Wang
6826dce4682e62074d0ed68d
Ainaz Eftekhar
admin_assigned
2025-05-16T14:08:48.493Z
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Ainaz Eftekhar
user
Ainaz99
6826dce4682e62074d0ed68e
Jason Lee
6826dce4682e62074d0ed68f
Wentao Yuan
6826dce4682e62074d0ed690
Rose Hendrix
admin_assigned
2025-05-16T14:08:36.316Z
627598f47128d87e9f92f487
/avatars/1f9ae7236b25f573f62e298436c7422a.svg
Rose Hendrix
user
roseh
6826dce4682e62074d0ed691
Noah A. Smith
6826dce4682e62074d0ed692
Fei Xia
6826dce4682e62074d0ed693
Dieter Fox
6826dce4682e62074d0ed694
Ranjay Krishna
admin_assigned
2025-05-16T14:08:19.914Z
66429868ab89e3a3a85668b0
/avatars/170e0daa454838deee2bf946f7118651.svg
Ranjay Krishna
user
ranjaykrishna
PointArena: Probing Multimodal Grounding Through Language-Guided
  Pointing
Pointing serves as a fundamental and intuitive mechanism for grounding
language within visual contexts, with applications spanning robotics, assistive
technologies, and interactive AI systems. While recent multimodal models have
started to support pointing capabilities, existing benchmarks typically focus
only on referential object localization tasks. We introduce PointArena, a
comprehensive platform for evaluating multimodal pointing across diverse
reasoning scenarios. PointArena comprises three components: (1) Point-Bench, a
curated dataset containing approximately 1,000 pointing tasks across five
reasoning categories; (2) Point-Battle, an interactive, web-based arena
facilitating blind, pairwise model comparisons, which has already gathered over
4,500 anonymized votes; and (3) Point-Act, a real-world robotic manipulation
system allowing users to directly evaluate multimodal model pointing
capabilities in practical settings. We conducted extensive evaluations of both
state-of-the-art open-source and proprietary multimodal models. Results
indicate that Molmo-72B consistently outperforms other models, though
proprietary models increasingly demonstrate comparable performance.
Additionally, we find that supervised training specifically targeting pointing
tasks significantly enhances model performance. Across our multi-stage
evaluation pipeline, we also observe strong correlations, underscoring the
critical role of precise pointing capabilities in enabling multimodal models to
effectively bridge abstract reasoning with concrete, real-world actions.
Project page: https://pointarena.github.io/
PointArena evaluates multimodal pointing models across various scenarios, demonstrating the importance of precise pointing in enabling abstract reasoning in real-world applications.
multimodal models pointing tasks reasoning categories supervised training Molmo-72B