Stateful Inference for Low-Latency Multi-Agent Tool Calling
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Computer Science > Machine Learning
Title:Stateful Inference for Low-Latency Multi-Agent Tool Calling
Abstract:Multi-agent tool calling is becoming the dominant interaction pattern for LLM-based systems, yet existing inference frameworks treat each tool call as an independent request, re-processing the entire conversation from scratch even though 85-95% of the prompt is unchanged from the previous turn.
We present a stateful inference architecture that converts the $O(n_t)$ per-turn cost of conventional serving into an $O(\Delta_t)$ delta-only cost: a persistent KV cache lives across turns and advances by ingesting only the new tokens, while a radix prefix cache extends this across interleaved multi-agent traffic and a prompt-lookup speculative decoder accelerates structured output. Against vLLM and SGLang on novel, fully-generated workloads, the reference implementation is $2.1\times$ faster per turn on a 6-turn agentic workflow and $4.2\times$ on the median turn of a 35-turn one, halving end-to-end wall time. The advantage comes from stateful reuse and speculation, not caching.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.26289 [cs.LG] |
| (or arXiv:2605.26289v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26289
arXiv-issued DOI via DataCite (pending registration)
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