Online Pandora's Box for Contextual LLM Cascading
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 of the deployed output. This output-mediated feedback structure differs from classical online contextual Pandora's Box models, in which opening a bo...
| Authors | Alexandre Belloni, Yan Chen, Yehua Wei |
|---|---|
| Primary category | cs.AI |
| Published | 5 June 2026 |
| Last updated at source | 26 August 2026 |
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