The Sequence Research #683: Orchestrating Intelligence: Sakana AI’s Multi-Model Tree Search Architecture
Inside one of the most creative AI papers of the last few months.
Over the past few years, we’ve witnessed astonishing leaps in the performance of large language models. But as we ascend the curve of scale, a familiar law of diminishing returns emerges. Training ever-larger models yields only incremental benefits, and the marginal cost of additional performance grows exponentially. What if the next breakthrough didn’t come from size—but from collaboration?
This is the provocative premise behind Sakana AI’s Multi-LLM AB-MCTS (Adaptive Branching Monte Carlo Tree Search): a framework that challenges the prevailing narrative of model monoliths. Instead of building ever-bigger brains, Sakana proposes a collective—an adaptive system where multiple models reason together, iteratively, guided by a search tree that mirrors human deliberation.