A Stackelberg Framework for Resource-Aware LLM Agents: Learning, Repair, and Conditional Guarantees
Large language model (LLM) agents increasingly operate as multi-turn systems that must allocate context, prompt verbosity, and tool access under finite computational budgets. Static thresholds are simple, but they are brittle under heterogeneous tasks and evolving session states. We formulate resource governance as a contextual Stackelberg game: a controller commits to a quality target and a cost incentive, while an executor responds with resource actions over context, prompting, and tool usage. We learn a conditional response model, optimize a leader policy against that model, and repair the resulting policy using real-API calibration and projection onto an empirically selected action se...
| Authors | Baoxun Wang |
|---|---|
| Primary category | cs.AI |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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