On June 10 Decart officially released Oasis 3 and opened API access to developers. This is the first programmatically callable real-time interactive world model, marking the move of "world models" from research demo to genuine production infrastructure.
Oasis 3's core breakthrough is turning video generation from "one output of fixed length" into "unlimited continuous response." The model accepts text prompts to define environmental conditions — geography, weather, lighting, traffic density — and continuously evolves over time. Three key capabilities make "continuous operation" possible: first, infinite scene generation — developers can switch scenes infinitely without triggering crashes; second, synchronized multi-view output — the front and side cameras share the same world state, and geometric consistency is preserved, which is critical for multi-camera training in autonomous driving; third, closed-loop control signal response — Oasis 3 directly accepts steering, throttle, and API commands, achieving true "trainable" simulation.
Technically, Oasis 3 runs on Decart's self-developed DOS (Decart Optimization Stack), with the inference stack optimized all the way from model architecture down to the hardware kernel, pushing end-to-end latency under 200ms. This is in stark contrast to previous world models — Google Genie 3, World Labs Marble, etc. generally last only tens of seconds to minutes before showing geometric drift or scene degradation, while Oasis 3 can run continuously for hours.
The first batch of landing scenarios for Oasis 3 is autonomous-driving simulation, but the architecture is naturally extensible to robots, drones, humanoid robots, and other Physical AI training. With a community of over a hundred thousand developers, Decart is trying to turn "real-time generated video" into the next LLM moment.
My judgment: the real watershed for world models has never been "how realistic the picture," but "can it run, run long, and run cheap." Oasis 3 is the first to push all three to production-usable levels simultaneously. When the marginal cost of simulation environments approaches zero, the iteration cadence of the robotics industry — historically dependent on real road testing — will be completely rewritten.