A generalized multimodal agent for real-world complex tasks.
Visit Seed by ByteDanceSeed by ByteDance is a state-of-the-art generalized agentic model (Seed1.8) that efficiently and accurately accomplishes complex tasks in real-world scenarios. Supporting both text and image inputs, Seed1.8 is equipped for multimodal processing, excelling in information retrieval, coding, GUI interaction, and video understanding. Designed for researchers, developers, and organizations seeking advanced large language, vision-language, and agentic AI capabilities, Seed enables real-time interaction and tool-use across diverse application domains.
ByteDance Rules Out AI Distillation ByteDance founder Zhang Yiming has told staff to stop improving the company’s AI
In recent years, research using artificial intelligence to predict material properties has advanced rapidly. Neural network quantum Monte Carlo methods have attracted attention as highly accurate simulation techniques. However, their extremely high computational cost has limited their application to small molecular systems. This study introduces a new computational method that overcomes this limitation.
The video model stands alongside Anthropic’s Claude as a compelling case for AI monetization.
SeedAudio 1.0 isn't just another voice generator — it's a unified tool for sound design that blurs the line between creation and post-production.
ByteDance has set four AI priorities for 2026: investing in world models, keeping the video model `Seedance` at top-tier performance, strengthening its coding foundation and agent capabilities, and accelerating commercialization of `Doubao` with an office-productivity focus, according to a 36Kr exclusive. Per 36Kr, Doubao's daily active users reached 200 million shortly after the 2026 Lunar New Year. The report says Seed leadership, under Wu Yonghui, set a target to release at least one world model by the end of 2026 and benchmark it against Google's `Genie 3`. 36Kr also describes a 2025 reorganization that folded ByteDance's AI Lab (led by Li Hang) and robotics team into Seed and created a small group exploring vision-language-action (VLA) approaches, while noting that progress on world models has been slower than expected.
ByteDance Seed shows that a 7B model can answer questions on long, image-heavy documents more reliably than much larger models, even when documents are four times longer than anything it saw during training. Instead of transcribing pages, the model learns by answering questions and finding the right passages on its own.
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