📋 摘要
⭐ 多模态agent基础模型，与SE for AI/测试/形式化方法等方向擦边，仅coding能力略有交集。
GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents
中文
本文提出 GLM-5V-Turbo，一个面向多模态 agent 的原生 foundation model。研究问题在于：随着 foundation models 在真实环境中的部署，agentic 能力不仅依赖语言推理，还要求模型能够感知、理解并作用于图像、视频、网页、文档、GUI 等异构上下文。作者认为，应将多模态感知作为推理、规划、工具使用与执行的核心组成部分，而非语言模型的辅助接口。围绕这一目标，作者在模型设计、多模态训练、reinforcement learning、工具链扩展以及与 agent 框架的集成等方面进行了系统性改进。实验结果表明，GLM-5V-Turbo 在多模态 coding、visual tool use 以及基于框架的 agentic 任务上取得了较强表现，同时保持了具有竞争力的纯文本 coding 能力。与既有以语言模型为中心、将视觉作为附加接口的工作不同，本文将多模态感知置于核心地位，并强调 hierarchical optimization 与可靠的 end-to-end verification 在构建多模态 agent 中的关键作用，为该方向提供了具有实践价值的开发经验。
English 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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