I'm also deeply curious about this legal question.

As I see it, model weights are the result of a mechanistic and lossy translation between training data and the final output weights. There is some human creativity involved, but that creativity is found exclusively in the model's code and training data, which are independently covered by copyright. Training is like a very expensive compilation process, and we have long-established that compiled artifacts are not distinct acts of creation.

In the case of a proprietary model like Kimi, copyright might survive based on 'special sauce' training like reinforcement learning – although that competes against the argument that pretraining on copyrighted data is 'fair use' transformation. However, I can't see a good argument that a model trained on a fully public domain dataset (with a genuinely open-source architecture) could support a copyright claim.