Note General model is not great at specializing tasks. Narrow-domain fine-tuned checkpoint becomes better at specific tasks, such local improvement can feedback onto the full training dataset, achieving self-augmentation based improvement. This is a interesting idea.

Note Use small language model to search the graph and route to the doman expert.

Note Automatic Flow Engineering done by 3B fine-tuned LLM, grounded on selective set of API-based functions. Planning model perform task decomposition, but do not do specific calls. Effectively doing flow (prompt) engineering here. Topology in plans are lacking and static plan-ahead approach is less robust (although good according to their curated 1k test dataset)