arXiv:2605.26289v1 Announce Type: new 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 prefi
The rapid adoption of multi-agent LLM systems for complex tasks necessitates more efficient inference architectures to overcome existing computational bottlenecks.
This development addresses a fundamental inefficiency in LLM-based systems, enabling faster, cheaper, and more complex multi-agent interactions, thus accelerating the scalability and utility of AI agents.
LLM inference for multi-agent tool calling will transition from a computationally expensive, full-reprocessing model to an efficient, delta-based update, significantly reducing latency and cost.
- · AI Agent Developers
- · Cloud Compute Providers (efficient usage)
- · Enterprises deploying LLM-based solutions
- · Hardware Manufacturers (optimized for stateful inference)
- · Companies with inefficient inference architectures
- · Systems not optimized for persistent KV caches
Reduced operational costs and increased throughput for advanced LLM applications, particularly those involving sequential decision-making and tool use.
Acceleration in the development and deployment of more sophisticated and truly autonomous AI agents capable of handling long-running, stateful tasks.
Potential for new business models and applications leveraging highly efficient and persistent multi-agent AI systems, leading to further disruption of traditional white-collar workflows.
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