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Atria Dawn Preview is introduced, a foundation agentic language model designed for scientific research and engineering workflows, with the goal of expanding the frontier of agent productivity in the real world.
This paper conducts a systematic study of unsupervised visual pretraining paradigms that directly leverage visual documents without text extraction, showing that Visual Pretraining is a scalable learner for foundation model intelligence.
Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings.
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