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

arXiv: https://arxiv.org/abs/2604.26752 · HTML: https://mlsanity.com/paper/2604.26752/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 26 of 26)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [GLM-5: from Vibe Coding to Agentic Engineering](https://mlsanity.com/paper/2602.15763/) (2026)
- [MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://mlsanity.com/paper/2409.02813/) (2024)
- [V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs](https://mlsanity.com/paper/2312.14135/) (2023)
- [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://mlsanity.com/paper/2305.07895/) (2023)
- [AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents](https://mlsanity.com/paper/2405.14573/) (2024)
- [WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models](https://mlsanity.com/paper/2401.13919/) (2024)
- [Better & Faster Large Language Models via Multi-token Prediction](https://mlsanity.com/paper/2404.19737/) (2024)
- [Query-Key Normalization for Transformers](https://mlsanity.com/paper/2010.04245/) (2020)
- [LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts](https://mlsanity.com/paper/2407.04973/) (2024)
- [Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering](https://mlsanity.com/paper/2403.03163/) (2024)
- [MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines](https://mlsanity.com/paper/2409.12959/) (2024)
- [SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models](https://mlsanity.com/paper/2502.13059/) (2025)
- [Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents](https://mlsanity.com/paper/2604.06132/) (2026)
- [MMSearch-Plus: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents](https://mlsanity.com/paper/2508.21475/) (2025)
- [PointArena: Probing Multimodal Grounding Through Language-Guided Pointing](https://mlsanity.com/paper/2505.09990/) (2025)
- [Advancing vision-language models in front-end development via data synthesis](https://mlsanity.com/paper/2503.01619/) (2025)
- [Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification](https://mlsanity.com/paper/2603.26648/) (2026)
- [UI2Code^N: UI-to-Code Generation as Interactive Visual Optimization](https://mlsanity.com/paper/2511.08195/) (2025)

## Built on by (most cited first, 30 of 34)

- [MMSkills: Towards Multimodal Skills for General Visual Agents](https://mlsanity.com/paper/2605.13527/) (2026)
- [MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft](https://mlsanity.com/paper/2605.30931/) (2026)
- [VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation](https://mlsanity.com/paper/2605.16079/) (2026)
- [Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction](https://mlsanity.com/paper/2606.05769/) (2026)
- [Thinking With Tools, Not With Pixels: Tool Calls as Text Scaffolds for Visual Reasoning](https://mlsanity.com/paper/2608.09682/) (2026)
- [EvoGUI: An Evolution-Aware Benchmark for GUI State-Transition Understanding](https://mlsanity.com/paper/2607.17050/) (2026)
- [UI-Venus-2 Technical Report](https://mlsanity.com/paper/2609.00028/) (2026)
- [SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenes](https://mlsanity.com/paper/2605.31148/) (2026)
- [HLL: Can Agents Cross Humanity's Last Line of Verification?](https://mlsanity.com/paper/2606.02449/) (2026)
- [Each Judge Its Own Yardstick: Discovering Per-VLM Taxonomies for Physical Video Evaluation](https://mlsanity.com/paper/2606.22918/) (2026)
- [PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception](https://mlsanity.com/paper/2606.28322/) (2026)
- [Invisible Ink Threats: Adversarial Goals Behind Legitimate Tasks in Computer-Use Agents](https://mlsanity.com/paper/2608.02018/) (2026)
- [UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City](https://mlsanity.com/paper/2608.27456/) (2026)
- [FIKA-Bench: From Fine-grained Recognition to Fine-Grained Knowledge Acquisition](https://mlsanity.com/paper/2605.13193/) (2026)
- [Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans](https://mlsanity.com/paper/2606.10953/) (2026)
- [Efficient Spatio-Temporal Grounding with Multimodal Large Models via Second-Level Tracking and RL Verification](https://mlsanity.com/paper/2606.29023/) (2026)
- [MT-Web2Code: Benchmarking Coding Agents on Multi-Turn Regional Reconstruction and Localized Modification](https://mlsanity.com/paper/2608.03474/) (2026)
- [VideoGAIA: A Benchmark for General AI Assistants on Agentic Video Understanding](https://mlsanity.com/paper/2608.14718/) (2026)
- [HarmTrace: Anchor-Calibrated Decoupled Optimization for Fine-Grained Target Identification in Harmful Memes](https://mlsanity.com/paper/2608.16622/) (2026)
- [Ancient-Bench: A Comprehensive Multi-millennial, Multi-medium, and Multi-script Benchmark for Ancient Chinese Artifact Text Recognition](https://mlsanity.com/paper/2608.27169/) (2026)
- [Framework and Benchmark for Code-Driven Agentic Testing in Web Development](https://mlsanity.com/paper/2609.00081/) (2026)
- [Sci-MMR: Benchmarking Multi-Step Evidence-Grounded Scientific Reasoning in Multimodal Agents](https://mlsanity.com/paper/2609.11243/) (2026)
- [Inside VLM Chart Reading: Tracing Value Reading from Vertical Bar Charts Across Space and Depth](https://mlsanity.com/paper/2609.13745/) (2026)
- [Block-Sparse Attention with Semantic-Geometric Decoupled Routing](https://mlsanity.com/paper/2609.22884/) (2026)
- [CCRV-Bench: Constraint-Based Evaluation of Causal Reasoning in Vision-Language Models](https://mlsanity.com/paper/2609.30979/) (2026)
- [Convergence of Practical Muon](https://mlsanity.com/paper/2609.33152/) (2026)
- [SciGen-Verifier: A Multimodal Reasoner for Explainable Verification in Scientific Image Generation](https://mlsanity.com/paper/2609.33399/) (2026)
- [IVT-Guard: All-in-One Reasoning Model for AI-Generated Content Detection](https://mlsanity.com/paper/2609.33585/) (2026)
- [PhysFieldBench: Can Multimodal Models Understand Physical Fields?](https://mlsanity.com/paper/2609.34072/) (2026)
- [Just MLPs: Efficient Visual State Reconstruction for Multimodal Language Models](https://mlsanity.com/paper/2609.34972/) (2026)

## Abstract (arXiv)

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.