Moonshot AI’s Kimi K2.5 is framed less as a new transformer and more as a systems bet: train text and vision together early, then let an external swarm split branch-heavy tasks across parallel sub-agents.
Kimi’s claim is architectural composition, not a fresh sequence model. The diagram separates what happens inside one multimodal backbone from what happens outside in the swarm controller.
The paper argues that low-ratio early joint text-vision training beats adding vision later. This heatmap uses realistic mock scores to visualize the pattern the episode discusses.
Parallel delegation should shine on branchable search, not on tightly shared state. Switch workload shape and metric to see where the systems story looks plausible and where it starts to flatten.
The episode’s skepticism is methodological: several attractive claims may be recipe-level observations rather than settled causal laws. This matrix scores how cleanly each claim is isolated.