arxiv: 2604.26752 · v3 · submitted 2026-04-29 · 💻 cs.CV

Recognition: no theorem link

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

GLM-V Team: Wenyi Hong , Xiaotao Gu , Ziyang Pan , Zhen Yang , Yuting Wang , Yue Wang , Yuanchang Yue , Yu Wang

show 74 more authors

Yanling Wang Yan Wang Xijun Liu Wenmeng Yu Weihan Wang Wei Li Shuaiqi Duan Sheng Yang Ruiliang Lv Mingdao Liu Lihang Pan Ke Ning Junhui Ji Jinjiang Wang Jing Chen Jiazheng Xu Jiale Zhu Jiale Cheng Ji Qi Guobing Gan Guo Wang Cong Yao Zijun Dou Zihao Zhou Zihan Wang Zhiqi Ge Zhijie Li Zhenyu Hou Zhao Xue Zehui Wang Zehai He Yusen Liu Yukuo Cen Yuchen Li Yuan Wang Yijian Lu Yanzi Wang Yadong Xue Xinyu Zhang Xinyu Liu Wenkai Li Tianyu Tong Tianshu Zhang Shengdong Yan Qinkai Zheng Mingde Xu Licheng Bao Jiaxing Xu Jiaxin Fan Jiawen Qian Jiali Chen Jiahui Lin Haozhi Zheng Haoran Wang Haochen Li Fan Yang Dan Zhang Chuangxin Zhao Chengcheng Wu Boyan Shi Bowei Jia Baoxu Wang Peng Zhang Debing Liu Bin Xu Juanzi Li Minlie Huang Yuxiao Dong Jie Tang Han Xu Da Yin Bowen Lv Bo Li Bin Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-13 07:25 UTC · model grok-4.3

classification 💻 cs.CV

keywords multimodal agentsfoundation modelsvisual perceptionagentic taskstool usereinforcement learningmultimodal coding

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The pith

GLM-5V-Turbo integrates multimodal perception directly into reasoning, planning, tool use, and execution for agents.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents GLM-5V-Turbo as a step toward native foundation models where perception of images, videos, webpages, and GUIs forms a core part of how the model reasons and acts rather than an add-on to language processing. This design choice aims to support real agent deployment in environments that mix text with visual and interface data. Improvements in model architecture, multimodal training, reinforcement learning, and toolchain integration are described as enabling stronger results on multimodal coding, visual tool use, and framework-based tasks while keeping text-only coding competitive. A sympathetic reader would see this as addressing the gap between current language-centric agents and the demands of heterogeneous real-world contexts.

Core claim

GLM-5V-Turbo is built around the objective that multimodal perception is integrated as a core component of reasoning, planning, tool use, and execution rather than as an auxiliary interface to a language model. The report summarizes advances across model design, multimodal training, reinforcement learning, toolchain expansion, and agent framework integration that produce strong performance on multimodal coding, visual tool use, and framework-based agentic tasks while preserving competitive text-only coding capability.

What carries the argument

Native integration of multimodal perception into the agent's reasoning and execution loop, achieved through combined model design, multimodal training, and reinforcement learning.

If this is right

Multimodal coding tasks improve because perception and reasoning operate within the same model.
Visual tool use becomes more reliable when perception is not routed through a separate interface.
Framework-based agentic tasks benefit from end-to-end optimization across perception and action.
Text-only coding capability remains competitive without sacrificing multimodal strengths.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Native multimodal agents could simplify deployment by removing the need for separate vision-language adapters in production systems.
Hierarchical optimization and verification methods highlighted in the work may become standard requirements for reliable real-world agent scaling.
The approach suggests that future agent benchmarks should test perception-reasoning integration under consistent compute and data conditions.

Load-bearing premise

That the reported gains in agent performance arise reliably from the described design, training, and reinforcement learning changes rather than from selective baselines or unstated evaluation choices.

What would settle it

A head-to-head comparison on the same agent benchmarks showing GLM-5V-Turbo matches but does not exceed a standard multimodal language model paired with an external perception module would falsify the claimed advantage of native integration.

Figures

Figures reproduced from arXiv: 2604.26752 by Baoxu Wang, Bin Chen, Bin Xu, Bo Li, Bowei Jia, Bowen Lv, Boyan Shi, Chengcheng Wu, Chuangxin Zhao, Cong Yao, Dan Zhang, Da Yin, Debing Liu, Fan Yang, GLM-V Team: Wenyi Hong, Guobing Gan, Guo Wang, Han Xu, Haochen Li, Haoran Wang, Haozhi Zheng, Jiahui Lin, Jiale Cheng, Jiale Zhu, Jiali Chen, Jiawen Qian, Jiaxin Fan, Jiaxing Xu, Jiazheng Xu, Jie Tang, Jing Chen, Jinjiang Wang, Ji Qi, Juanzi Li, Junhui Ji, Ke Ning, Licheng Bao, Lihang Pan, Mingdao Liu, Mingde Xu, Minlie Huang, Peng Zhang, Qinkai Zheng, Ruiliang Lv, Shengdong Yan, Sheng Yang, Shuaiqi Duan, Tianshu Zhang, Tianyu Tong, Weihan Wang, Wei Li, Wenkai Li, Wenmeng Yu, Xiaotao Gu, Xijun Liu, Xinyu Liu, Xinyu Zhang, Yadong Xue, Yanling Wang, Yan Wang, Yanzi Wang, Yijian Lu, Yuanchang Yue, Yuan Wang, Yuchen Li, Yue Wang, Yukuo Cen, Yusen Liu, Yuting Wang, Yu Wang, Yuxiao Dong, Zehai He, Zehui Wang, Zhao Xue, Zhen Yang, Zhenyu Hou, Zhijie Li, Zhiqi Ge, Zihan Wang, Zihao Zhou, Zijun Dou, Ziyang Pan.

**Figure 1.** Figure 1: Performance comparison of CogViT with other state-of-the-art vision encoders across view at source ↗

**Figure 2.** Figure 2: Illustration of our multimodal multi-token prediction (MMTP) design. view at source ↗

**Figure 3.** Figure 3: Examples of multimodal deep research and content creation. (a) A multimodal deep view at source ↗

**Figure 4.** Figure 4: Evaluation of GLM-5V-Turbo on multimodal coding, tool-use, and GUI agent benchmarks. view at source ↗

**Figure 5.** Figure 5: Evaluation of GLM-5V-Turbo on text coding and claw agent benchmarks. view at source ↗

**Figure 6.** Figure 6: A case showing the application of GLM-5V-Turbo to stock analysis, with OpenClaw and view at source ↗

**Figure 7.** Figure 7: A case showing the application of GLM-5V-Turbo to URL-based GUI exploration, asset col view at source ↗

**Figure 8.** Figure 8: A case showing the application of GLM-5V-Turbo to PRD-driven website generation, with view at source ↗

**Figure 9.** Figure 9: A case showing the application of GLM-5V-Turbo to full-stack e-commerce website design view at source ↗

**Figure 10.** Figure 10: A case showing the application of GLM-5V-Turbo to UI recreation and mock interface view at source ↗

**Figure 11.** Figure 11: A case showing the application of GLM-5V-Turbo to agentic UI recreation, using our view at source ↗

**Figure 12.** Figure 12: A case showing the application of GLM-5V-Turbo to automatic website generation for view at source ↗

**Figure 13.** Figure 13: A case showing the application of GLM-5V-Turbo to automatic PowerPoint generation view at source ↗

**Figure 14.** Figure 14: A case showing the application of GLM-5V-Turbo to image materials collection, using view at source ↗

**Figure 15.** Figure 15: A case showing the ability of document-based writing. (a) A travel guide of Beijing view at source ↗

**Figure 16.** Figure 16: A case showing the ability of multilingual OCR. (a) Original image. (b) Recognized view at source ↗

**Figure 17.** Figure 17: A case showing the ability of accurate document transcription. (a) Original page from a view at source ↗

**Figure 18.** Figure 18: A case showing the ability of utilizing the information from the image and multimodal view at source ↗

**Figure 19.** Figure 19: A case showing the ability of locating the input image and search local hotel prices on view at source ↗

**Figure 20.** Figure 20: A case showing the ability of video objects tracking. view at source ↗

**Figure 21.** Figure 21: A case showing the ability of video objects tracking. view at source ↗

**Figure 22.** Figure 22: A case demonstrating recognition capability based on grounding and search tools. (a) view at source ↗

**Figure 23.** Figure 23: A case demonstrating the ability to grounding educational scene elements. (a) Grounding view at source ↗

**Figure 24.** Figure 24: A case demonstrating 3D grounding capability, where our model outputs a 3D bounding view at source ↗

**Figure 25.** Figure 25: A case showing the ability of spatial reasoning and object counting. view at source ↗

read the original abstract

We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environments, agentic capability depends not only on language reasoning, but also on the ability to perceive, interpret, and act over heterogeneous contexts such as images, videos, webpages, documents, GUIs. GLM-5V-Turbo is built around this objective: multimodal perception is integrated as a core component of reasoning, planning, tool use, and execution, rather than as an auxiliary interface to a language model. This report summarizes the main improvements behind GLM-5V-Turbo across model design, multimodal training, reinforcement learning, toolchain expansion, and integration with agent frameworks. These developments lead to strong performance in multimodal coding, visual tool use, and framework-based agentic tasks, while preserving competitive text-only coding capability. More importantly, our development process offers practical insights for building multimodal agents, highlighting the central role of multimodal perception, hierarchical optimization, and reliable end-to-end verification.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

GLM-5V-Turbo is a model report pushing native multimodal integration for agents, but the lack of any numbers or controls makes the performance claims impossible to evaluate.

read the letter

The core pitch is that GLM-5V-Turbo folds perception directly into reasoning, planning, tool use, and execution instead of bolting vision on top of a language model. The abstract outlines updates to model design, multimodal training, reinforcement learning, and agent framework hooks that supposedly produce strong results on multimodal coding and visual tool tasks while holding text-only performance steady. They also flag practical lessons around hierarchical optimization and end-to-end verification, which could be handy for teams already building agent loops in visual environments like GUIs or documents. That part reads like the kind of engineering notes that come out of a group with real deployment experience. The obvious gap is the total lack of data. No tables, no baselines against standard multimodal-LLM-plus-wrapper setups, no ablations on whether the native changes actually drive the gains, and no error bars. Without those, you cannot separate the claimed architecture benefit from scaling, data selection, or post-hoc task choice. The stress-test note is right on this point. This is aimed at practitioners who want a concrete recipe for mixing vision and action in agent frameworks. Readers who need reproducible evidence that native integration beats the usual approach will come away empty. I would bring it to a reading group to discuss the design choices and toolchain details, but not for the results. It deserves a serious referee once the experiments section is filled in, because the underlying question about core versus auxiliary multimodal reasoning is worth testing properly.

Referee Report

2 major / 1 minor

Summary. The manuscript presents GLM-5V-Turbo as a step toward native foundation models for multimodal agents. It claims that multimodal perception (images, videos, webpages, documents, GUIs) is integrated as a core component of reasoning, planning, tool use, and execution rather than an auxiliary interface. The report summarizes improvements across model design, multimodal training, reinforcement learning, toolchain expansion, and agent framework integration, asserting that these yield strong performance in multimodal coding, visual tool use, and framework-based agentic tasks while preserving competitive text-only coding capability, along with practical insights on multimodal perception, hierarchical optimization, and end-to-end verification.

Significance. If the performance claims are substantiated with rigorous evidence, the work would be significant for the development of agentic foundation models by showing that native multimodal integration can improve reliability in real-world tasks involving heterogeneous contexts. This could influence future designs away from modular LLM-plus-wrapper architectures toward more unified systems, with potential benefits for deployment in coding, tool-use, and GUI environments.

major comments (2)

[Abstract] Abstract: The manuscript asserts 'strong performance' in multimodal coding, visual tool use, and agentic tasks resulting from the described model-design, training, and RL changes, yet provides no quantitative results, ablation studies, baseline comparisons, error bars, or statistical tables. This absence directly undermines evaluation of the central claim that native multimodal integration (rather than auxiliary interfaces) produces measurable gains.
[Abstract] Abstract: The summary of improvements in model design, multimodal training, reinforcement learning, and toolchain expansion is entirely high-level with no specific architectural details, equations, loss formulations, training objectives, or hyperparameter settings. Without these, the contributions cannot be assessed for novelty or correctness relative to prior multimodal agent work.

minor comments (1)

The manuscript lacks numbered sections, tables, or figures, which makes it difficult to reference specific claims or results for detailed review.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. The comments correctly identify opportunities to make the high-level claims more concrete and traceable to the experimental evidence in the full manuscript. We will revise the abstract to incorporate key quantitative highlights and explicit references to methodological details while preserving its concise format.

read point-by-point responses

Referee: [Abstract] Abstract: The manuscript asserts 'strong performance' in multimodal coding, visual tool use, and agentic tasks resulting from the described model-design, training, and RL changes, yet provides no quantitative results, ablation studies, baseline comparisons, error bars, or statistical tables. This absence directly undermines evaluation of the central claim that native multimodal integration (rather than auxiliary interfaces) produces measurable gains.

Authors: We agree that the abstract should provide immediate quantitative anchors for the performance claims. In the revised version we will add concise references to specific metrics (e.g., relative gains on visual tool-use and multimodal coding benchmarks) together with pointers to the corresponding tables, ablation studies, and statistical comparisons that appear in Sections 5 and 6. This change directly addresses the concern while keeping the abstract length appropriate. revision: yes
Referee: [Abstract] Abstract: The summary of improvements in model design, multimodal training, reinforcement learning, and toolchain expansion is entirely high-level with no specific architectural details, equations, loss formulations, training objectives, or hyperparameter settings. Without these, the contributions cannot be assessed for novelty or correctness relative to prior multimodal agent work.

Authors: Abstracts conventionally remain high-level, yet we accept that additional signposting would aid assessment. We will therefore insert brief, concrete references to the core architectural choices (hierarchical multimodal perception integration, end-to-end verification) and explicitly direct readers to the detailed equations, loss formulations, training objectives, and hyperparameter tables provided in Sections 3 and 4. Full reproducibility information remains in the main text and appendices. revision: partial

Circularity Check

0 steps flagged

No circularity: high-level empirical report without derivations or self-referential claims

full rationale

The manuscript is a descriptive summary of GLM-5V-Turbo's model design, multimodal training, reinforcement learning, and agent integration. No equations, fitted parameters presented as predictions, uniqueness theorems, or derivation chains appear in the abstract or full text. Performance statements are high-level assertions about 'strong performance' without reducing to self-citation load-bearing steps or ansatzes smuggled via prior work. The content is therefore self-contained as an empirical model report rather than a mathematical derivation that could be circular by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; all claims rest on unspecified training procedures and internal evaluations.

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About Brand

Zhipu AI Team. Autoclaw. https://autoglm.zhipuai.cn/autoclaw/, 2026. AI Assistant Tool Supporting Windows & macOS, Model Hot-Swapping, 50+ Skills, AutoGLM Browser Automation, accessed 2026-04-15. 17 A Demo Cases We demonstrate the capabilities and advantages of GLM-5V-Turbo through typical qualitative examples from various scenarios. A.1 In Combination wi...

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