TransWeave is a framework for carrying useful structure forward when a system moves from one task, environment, or regime into another. The core idea is that an intelligent system should not have to either cling rigidly to an old solution or start over from scratch. It should preserve what is still true, adapt what has changed, and do so in a disciplined way.
In formal terms, TransWeave studies transfer maps that preserve the deep organization of a task while allowing local adaptation. In reinforcement learning, this is framed through Bellman–Darboux intertwining: a transfer is good when “transfer then learn” comes out nearly the same as “learn in the new setting,” which gives a principled way to judge when prior structure remains valid, when adaptation can stay local, and when more substantial revision is needed.
Within Hyperon, TransWeave is best understood as a cross-stack strategy for continuity of intelligence. It complements predictive coding, causal coding, and modular memory by helping explain how learned structure can persist across changing contexts, so that modules still suited to the new reality remain largely intact while learning effort concentrates where the shift actually occurred.