1.Research the model's ability to understand generated content, parsing semantics, objects, relationships, and spatial structures.
2.Implement state tracking and consistency modeling for generated videos.
3.Explore "unified generation-understanding" model architectures.
4.Research causal consistency control and physical constraints for the "action input → video output" pipeline.
5.Research hybrid architectures to achieve low-latency feedback for real-time interactive generation.
6.Collaborate with Agent/RL teams to drive an end-to-end closed loop of "generation → understanding → control → feedback."
7.Validate controllable interactive capabilities in game scenarios and lead the establishment of evaluation systems.
8.Continuously track industry-related work and formulate proprietary technical roadmaps.
1.Ph.D. in AI-related fields, covering areas such as video understanding, video prediction, reinforcement learning, or multimodal generation and understanding.
2.Deep understanding of the internal mechanisms of both video generation and VLM models; familiar with diffusion model principles.
3.Research experience in interactive video generation or controllable video generation.
4.Ability to independently design complex model training pipelines with strong engineering skills.
5.Proficient in Python/PyTorch, with publications in video understanding, controllable generation, or reinforcement learning.
6.Experience with Game AI, simulation environments, or closed-loop systems is preferred.
7.Relevant publications in top-tier conferences or journals are preferred.
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