Large Video Planner Enables Generalizable Robot Control

This is a research paper with 0 stars, 0 forks, and no apparent public code release (109 days old, recently published). The work presents an alternative paradigm to existing VLA approaches by using large video foundation models for planning rather than direct action prediction—a novel combination of techniques but not a breakthrough. The core idea (leveraging pre-trained models for robot control) extends established patterns in robotics and multimodal learning. As a paper-only artifact without released code or adoption, defensibility is low. Frontier risk is HIGH because: (1) OpenAI, Google DeepMind, and other labs are actively investing in robot foundation models and VLA systems; (2) this directly competes with their research directions (e.g., Google's RT-2/RT-X, OpenAI's robotics work); (3) frontier labs have the compute resources, robotics infrastructure, and pretraining datasets to execute similar ideas at scale; (4) the approach is not defensible through network effects or data gravity—it's an algorithmic contribution that can be rapidly reproduced or integrated into existing platforms. The work is technically sound but lacks production code, users, or ecosystem lock-in. Implementation is reference-level (paper + likely code appendix). This represents high frontier obsolescence risk if labs choose to integrate video-model-based planning into their robot platforms.