Products powered by large language models have been deeply integrated into our lives. In the past year or so, the LLM model series developed by Seed has supported C-end products with hundreds of millions of users, such as Doubao. Meanwhile, we've also noticed that with the arrival of the Agent era, LLMs will play an even more significant role in complex real-world tasks. For example, it can participate in scientific research, support complex software development, and even autonomously learn from context to complete various tasks with economic value.

At this pivotal moment, we are honored to introduce the latest Seed2.0 series. It has been systematically optimized to meet the requirements of large-scale production deployment, aiming to help tackle complex real-world tasks.

By analyzing how the Seed general-purpose model is invoked in the MaaS service, we discovered that the highest proportion of requests is to process knowledge-intensive content from unstructured sources, like complex charts and documents. Companies usually expect the model to start with tasks that require "heavy reading and deep thinking" before engaging in complex and professional workflows. This places increasingly high demands on the model's ability to understand long content and execute multi-step tasks.