GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents
GLM-V Team, :, Wenyi Hong, Xiaotao Gu, Ziyang Pan + 74 more
TLDR
GLM-5V-Turbo is a new foundation model integrating multimodal perception natively for enhanced agent reasoning, planning, and tool use across diverse contexts.
Key contributions
- Integrates multimodal perception as a core component for agent reasoning, planning, and tool use.
- Achieves strong performance in multimodal coding, visual tool use, and framework-based agentic tasks.
- Maintains competitive text-only coding capabilities while advancing multimodal functions.
Why it matters
This paper addresses the critical need for foundation models to natively handle diverse multimodal contexts for real-world agent deployment. By integrating perception directly into reasoning, GLM-5V-Turbo significantly advances agentic capabilities beyond language-only models, offering practical insights for future development.
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.
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