arXiv:2605.25424v1 Announce Type: new Abstract: Existing LLM routing frameworks treat queries as independent events, neglecting the sequential nature of real-world user sessions constrained by global computational budgets. This mismatch inevitably leads to budget bankruptcy: myopic routing policies exhaust resources on early interactions, forcing subsequent and often more complex queries onto inadequate models. We introduce SeqRoute, a framework that formulates multi-turn routing as a finite-horizon Markov Decision Process and solves it via offline reinforcement learning. By incorporating the
The increasing complexity and computational cost of LLMs, coupled with the growing demand for multi-turn conversational AI, necessitate more efficient resource management strategies.
Efficient routing and budget management for LLM interactions directly impact the scalability, cost-effectiveness, and user experience of advanced AI applications.
The approach to managing multi-turn interactions with diverse LLMs shifts from myopic, independent query handling to a globally optimized, budget-aware sequential process.
- · Cloud providers offering AI services
- · Developers of AI applications
- · Users of conversational AI systems
- · Inefficient LLM routing frameworks
- · Companies with high LLM operational costs
This framework could lead to more robust and cost-efficient deployment of complex AI agents and services.
Improved resource management might accelerate the development and adoption of AI-driven tools in various industries by reducing operational expenditures.
The widespread implementation of such intelligent routing could create new competitive dynamics among LLM providers, favouring those that can be optimally integrated into sequential decision-making frameworks.
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