Seed1.8 Model Card: Towards Generalized Real-World Agency

2025-12-10 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

Seed1.8 is a new foundation model designed for generalized real-world agency, integrating strong large language model (LLM) and vision-language model (VLM) capabilities with multi-turn interaction and task execution. It supports search, code generation/execution, and graphical user interface (GUI) interaction within a unified agentic interface. The model features configurable "thinking modes" to balance inference depth and latency, and optimized visual encoding for multimodal inputs. Seed1.8 demonstrates competitive performance across standard LLM and VLM benchmarks, including reasoning, complex instruction following, and knowledge. It also excels in agentic tasks like search, coding, writing, and GUI operation, achieving state-of-the-art results on several benchmarks such as GAIA (93.2), ZeroBench (11.0), and OSWorld. The model shows significant improvements in inference and multimodal token efficiency compared to its predecessor, Seed1.6, particularly in long-video understanding with a 32K token budget.

Key takeaway

For AI Engineers developing real-world agentic applications, Seed1.8 offers a robust foundation for multi-step task execution and multimodal interaction. You should explore its configurable "thinking modes" to optimize performance and cost for interactive deployments, and consider its strong performance in GUI and video understanding for automating complex workflows in domains like customer support, finance, and scientific research.

Key insights

Seed1.8 unifies LLM/VLM capabilities with multi-step agentic interaction for generalized real-world task execution.

Principles

• Integrate perception, reasoning, and action within a single model.
• Balance inference depth and latency with configurable thinking modes.
• Prioritize evaluation aligned with practical, high-value application domains.

Method

Seed1.8 employs a unified agentic interface for iterative decision-making, leveraging search, code execution, and native visual perception for multi-step task execution and environment interaction.

In practice

• Utilize "thinking modes" to optimize inference for latency-sensitive applications.
• Apply Seed1.8 for complex workflows like financial analysis and travel planning.
• Leverage video tool-use (e.g., VideoCut) for enhanced long-video understanding.

Topics

• Seed1.8 Model
• Generalized Real-World Agency
• Multimodal AI
• Agentic Task Execution
• Inference Optimization

Code references

• youdotcom-oss/ydc-deep-research-evals

Best for: AI Engineer, NLP Engineer, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.