At the 2026 Beijing Auto Show, former DeepSeek multimodal technology core researcher Ruan Chong made his first public appearance as Chief Scientist at DeepRoute.ai, accompanied by a fundamental shift in the company's technology route — a full bet on large-model autonomous driving.
DeepRoute.ai CEO Zhou Guang said multimodal large model capability achieved breakthrough progress in early 2026, and the starting point of the large-model autonomous-driving route is already far superior to the previous-generation technology. This means the industry has officially acknowledged a fact: the era of stacking small models is ending.
DeepRoute.ai clearly pointed out the core problem of the small-model route — the "seesaw effect": when small models switch between different scenarios, capability will rebound, solving one corner case may degrade in another. This isn't a problem algorithm tuning can completely solve, but a fundamental architectural defect. Full-scenario safety coverage requires stronger generalization capability.
Under the new architecture, DeepRoute.ai is migrating from multiple small models to a unified base large model, splitting into three vertical models: driving, analysis, and commentary. The real highlight is the iteration efficiency leap: single model iteration cycle compressed from 100+ hours to 10+ hours, nearly 10× faster. Shortened iteration cycle means road-test-discovered problems can be fixed quickly, corner case coverage speed greatly increases, directly determining the speed of technology convergence.
This isn't one company's route choice, but a wind vane for the entire autonomous-driving industry. When multimodal large model capability crosses the critical point, the "main large model, small model as guarantee" hybrid architecture will gradually become mainstream. Ruan Chong's joining means DeepSeek's accumulation in multimodal is spilling over into autonomous driving, and large-model competition is entering a new stage of cross-domain integration.