Recurrent depth helps compositional tool calling but barely moves isolated API calls

arXiv 2608.18171·low signalXBlueskyLinkedInCopy link

Testing looped language models on API-Bank, BFCL, and NESTful, the authors found accuracy on multi-step tool use generally rises with recurrent depth, while gains on single isolated calls were small and varied by model. Adaptive inference, spending extra recurrence only when needed, gave the better compute-performance trade-off. The takeaway for agent design is that the hard part is dependency tracking across calls, not the individual call, so extra compute should be aimed there.