Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs

Abstract:Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. Under these semantics, LLM decoding is blocked until each function call completes, resulting in increasing end-to-end latency. In this work, we introduce AsyncFC, a pure execution-layer framework that decouples LLM decoding from function execution, enabling overlap between model decoding and function execution as well as inter-function parallelism when dependencies permit. AsyncFC layers over existing models and unmodified function implementations, requiring no fine-tuning or changes to the standard synchronous function-calling protocol. On coding tasks, AsyncFC achieves a $1.4\times$ end-to-end speedup while preserving task accuracy. Furthermore, these results reveal that LLMs possess a native capability to reason over symbolic futures that represent unresolved execution results, enabling an asynchronous paradigm for model-tool interaction.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2605.15077 [cs.CL]

(or arXiv:2605.15077v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.15077 Focus to learn more arXiv-issued DOI via DataCite

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