arXiv:2606.07392v1 Announce Type: cross Abstract: Motivated by Large Language Model (LLM) cascading, we propose an online contextual Pandora's Box model for adaptively querying and selecting LLM APIs. In each period, a decision-maker observes a request context and faces a two-phase decision problem. In the query phase, the decision-maker sequentially queries APIs, where each query reveals a generated output and the decision-maker incurs an (output-dependent) cost. In the selection phase, the decision-maker selects one of the generated outputs to deploy and observes only the downstream reward o
The proliferation of advanced LLM APIs necessitates novel mechanisms for adaptive querying and selection to optimize their utility and manage costs.
This research introduces a structured approach to managing complexity and cost in an environment increasingly reliant on multiple LLM services, impacting efficiency and economic models of AI deployment.
The proposed 'Online Pandora's Box' model changes how decision-makers interact with and derive value from diverse generative AI capabilities by introducing a formalized approach to querying and selection.
- · Businesses building multi-LLM applications
- · AI orchestration platforms
- · Developers of LLM-powered agents
- · LLM providers with high, undifferentiated API costs
- · Systems lacking adaptive decision-making capabilities
Adaptive querying models will optimize the cost-benefit trade-off for LLM API usage.
This optimization could lead to the emergence of new market structures around LLM brokerage and intelligent API management.
Increased efficiency in LLM utilization may accelerate the development and deployment of sophisticated AI agents across various sectors.
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Read at arXiv cs.LG