title: PointArena: Probing Multimodal Grounding Through Language-Guided
    Pointing
summary: 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/
paper_authors._id: 6826dce4682e62074d0ed686
paper_authors.name: Long Cheng
paper_authors._id: 6826dce4682e62074d0ed687
paper_authors.name: Jiafei Duan
paper_authors._id: 6826dce4682e62074d0ed688
paper_authors.name: Yi Ru Wang
paper_authors.status: admin_assigned
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paper_authors.user: yiruwang0324
paper_authors._id: 6826dce4682e62074d0ed689
paper_authors.name: Haoquan Fang
paper_authors.status: admin_assigned
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paper_authors.fullname: Haoquan Fang
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paper_authors.name: Boyang Li
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paper_authors.avatarUrl: /avatars/dc4db02e37eddd6b14a058349b218b44.svg
paper_authors.fullname: Albert Li
paper_authors.type: user
paper_authors.user: boyangli
paper_authors._id: 6826dce4682e62074d0ed68b
paper_authors.name: Yushan Huang
paper_authors.status: admin_assigned
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paper_authors._id: 66236ecff1b64cfb49c236dc
paper_authors.avatarUrl: /avatars/8328233527d6785ef7ee8f726e696e72.svg
paper_authors.fullname: Yu Shan Huang
paper_authors.type: user
paper_authors.user: hys990996
paper_authors._id: 6826dce4682e62074d0ed68c
paper_authors.name: Elvis Wang
paper_authors._id: 6826dce4682e62074d0ed68d
paper_authors.name: Ainaz Eftekhar
paper_authors.status: admin_assigned
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paper_authors._id: 64272345d039ee7fa4e0b015
paper_authors.avatarUrl: /avatars/d8933fb0f171e1f752f64a59c1745da5.svg
paper_authors.fullname: Ainaz Eftekhar
paper_authors.type: user
paper_authors.user: Ainaz99
paper_authors._id: 6826dce4682e62074d0ed68e
paper_authors.name: Jason Lee
paper_authors._id: 6826dce4682e62074d0ed68f
paper_authors.name: Wentao Yuan
paper_authors._id: 6826dce4682e62074d0ed690
paper_authors.name: Rose Hendrix
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T14:08:36.316Z
paper_authors._id: 627598f47128d87e9f92f487
paper_authors.avatarUrl: /avatars/1f9ae7236b25f573f62e298436c7422a.svg
paper_authors.fullname: Rose Hendrix
paper_authors.type: user
paper_authors.user: roseh
paper_authors._id: 6826dce4682e62074d0ed691
paper_authors.name: Noah A. Smith
paper_authors._id: 6826dce4682e62074d0ed692
paper_authors.name: Fei Xia
paper_authors._id: 6826dce4682e62074d0ed693
paper_authors.name: Dieter Fox
paper_authors._id: 6826dce4682e62074d0ed694
paper_authors.name: Ranjay Krishna
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T14:08:19.914Z
paper_authors._id: 66429868ab89e3a3a85668b0
paper_authors.avatarUrl: /avatars/170e0daa454838deee2bf946f7118651.svg
paper_authors.fullname: Ranjay Krishna
paper_authors.type: user
paper_authors.user: ranjaykrishna
paper_title: PointArena: Probing Multimodal Grounding Through Language-Guided
    Pointing
paper_summary: 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/
paper_ai_summary: PointArena evaluates multimodal pointing models across various scenarios, demonstrating the importance of precise pointing in enabling abstract reasoning in real-world applications.
paper_ai_keywords: multimodal models pointing tasks reasoning categories supervised training Molmo-72B