# Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity

arXiv: https://arxiv.org/abs/2607.00248 · HTML: https://mlsanity.com/paper/2607.00248/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2026.

## Built on by (most cited first, 30 of 58)

- [HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better](https://mlsanity.com/paper/2607.04884/) (2026)
- [Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning](https://mlsanity.com/paper/2608.27549/) (2026)
- [UI-MOPD: Multi-Platform On-Policy Distillation for Unified GUI Agents](https://mlsanity.com/paper/2607.04425/) (2026)
- [SceneActBench: Can Agents Act on the 3D Scenes They See?](https://mlsanity.com/paper/2607.22393/) (2026)
- [Self-Authored Verification Is Unreliable in Heuristic Self-Improving Agents](https://mlsanity.com/paper/2607.24300/) (2026)
- [Annotations as Rollouts: Efficient and Scalable Reinforcement Learning for Video MLLMs](https://mlsanity.com/paper/2608.20492/) (2026)
- [SearchArt: Training Long-Horizon Search Agent with Scalable Synthetic and Verified Task](https://mlsanity.com/paper/2607.24850/) (2026)
- [ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment](https://mlsanity.com/paper/2608.05102/) (2026)
- [StreamArena: Toward Continuous, Interactive, and Long-Horizon Agentic Streaming Video Understanding](https://mlsanity.com/paper/2608.05703/) (2026)
- [Douyin Multimodal Embedding Model Technical Report](https://mlsanity.com/paper/2608.02148/) (2026)
- [The Problem Is the Problem: Towards Scalable Mathematical Discovery](https://mlsanity.com/paper/2608.16977/) (2026)
- [FIRM-Video: Check Before You Score for Reliable Text-to-Video Reward Modeling](https://mlsanity.com/paper/2608.21839/) (2026)
- [UI-Venus-2 Technical Report](https://mlsanity.com/paper/2609.00028/) (2026)
- [Online Video Agent Harness for Long Video Understanding](https://mlsanity.com/paper/2609.12818/) (2026)
- [Qwen3.8-Omni: Towards Native Omni-Modal Agents](https://mlsanity.com/paper/2609.25611/) (2026)
- [Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports](https://mlsanity.com/paper/2608.04682/) (2026)
- [UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City](https://mlsanity.com/paper/2608.27456/) (2026)
- [LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation](https://mlsanity.com/paper/2608.30935/) (2026)
- [Thinking with Cameras: Active Visual Reasoning via Dynamic Viewpoint Control for Surveillance Video Understanding](https://mlsanity.com/paper/2609.06475/) (2026)
- [Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction](https://mlsanity.com/paper/2609.21392/) (2026)
- [Enhancing Fitness Intelligence through Domain-Specific LLM Post-Training](https://mlsanity.com/paper/2607.02118/) (2026)
- [UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma](https://mlsanity.com/paper/2607.06987/) (2026)
- [DecoupleMix: Decoupled Ratio Search and Convex Allocation for Scalable VLM Data Recipes](https://mlsanity.com/paper/2607.24516/) (2026)
- [Models as Tools: An Agentic Coordination Framework for Unified Multimodal Visual Tracking](https://mlsanity.com/paper/2608.00847/) (2026)
- [MT-Web2Code: Benchmarking Coding Agents on Multi-Turn Regional Reconstruction and Localized Modification](https://mlsanity.com/paper/2608.03474/) (2026)
- [FactorDrive: Adaptive Multi-Step Reasoning Driven by Planning-Critical Factors for End-to-End Autonomous Driving](https://mlsanity.com/paper/2608.09591/) (2026)
- [VideoGAIA: A Benchmark for General AI Assistants on Agentic Video Understanding](https://mlsanity.com/paper/2608.14718/) (2026)
- [OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs](https://mlsanity.com/paper/2608.21360/) (2026)
- [Repo2Skill-Evo: Repository Skills Go Stale in Silence](https://mlsanity.com/paper/2608.21964/) (2026)
- [Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing](https://mlsanity.com/paper/2608.24263/) (2026)

## Abstract (arXiv)

We present Seed2.0, a model series that takes a meaningful step toward solving complex, real-world tasks. Our approach begins with identifying users' genuine needs and constructing a reliable, forward-looking evaluation system by selecting and abstracting benchmarks grounded in these needs and in realistic, complex scenarios. Guided by this evaluation system, Seed2.0 targets two persistent challenges, long-tail knowledge and complex instruction following, substantially improving the model's reliability on intricate, long-horizon tasks. Beyond these, Seed2.0 delivers world-leading reasoning intelligence, visual understanding, and search capabilities that address the most common needs of a broad user base. Through extensive real-world use cases documented in this model card, we demonstrate that Seed2.0 begins to exhibit the ability to handle initial complex real-world tasks, delivering greater value to hundreds of millions of users.