arXiv cs.AI论文
循环语言模型提升组合式工具调用能力
该研究探讨循环语言模型在组合式工具调用中的潜力，通过对比循环与非循环模型在API-Bank、BFCL和NESTful基准上的表现，发现循环计算有助于依赖感知的工具使用，且多步工具调用准确率随循环深度增加而提升，但自适应推理能更高效地分配计算资源。
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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.