Looped Language Models Improve Compositional Tool Calling

A new arXiv preprint (2608.18171v1) finds that looped language models improve compositional tool calling, with accuracy on multi-step tool use generally increasing with recurrent depth. The study, which evaluated native and retrofitted looped models on API-Bank, BFCL, and NESTful, shows that recurrent computation benefits compositional and dependency-aware tool use, while adaptive inference offers a better compute-performance trade-off by allocating computation only when needed.

arXiv:2608.18171v1 Announce Type: new Abstract: Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We study this question in compositional tool-calling settings, where models must coordinate multiple API calls, maintain intermediate state, and preserve dependencies across tool interactions. We evaluate native and retrofitted looped language models on API-Bank, BFCL, and NESTful, comparing looped and non-looped models trained under matched supervised fine-tuning recipes and varying recurrent depth at inference time. In controlled experiments, recurrent computation generally benefits compositional and dependency-aware tool use, while providing smaller and more model-dependent gains on isolated API invocation. Accuracy on multi-step tool use generally increases with recurrent depth; adaptive inference, however, achieves a more favorable compute-performance trade-off by allocating additional computation only when needed. Our results suggest that looped language models are a promising architecture for agentic systems that require reliable planning, coordination, and execution of compositional tool use workflows.