LLM Action Selection: A Regime Theory for Controller Classes
A recent study published on arXiv (2605.06339) questions the belief that enhanced controllers invariably boost the performance of large language models (LLMs). The researchers categorize controllers into a hierarchical structure consisting of four types: fixed actions, partition routers, instance-level controllers, and prior-gated controllers. They establish a regime theory demonstrating that the limitations of finite samples regarding instance-level uncertainty signals lead to varying preferences for controller classes across different benchmarks when subjected to rigorous cross-validation. This theory identifies three data-estimable constraints that influence the choice of class: the improvement over the optimal fixed action, the necessary sample size for dependable instance-level decisions, and other factors reliant on distribution.
Key facts
- arXiv paper 2605.06339
- Title: A Regime Theory of Controller Class Selection for LLM Action Decisions
- Four controller classes: fixed actions, partition routers, instance-level controllers, prior-gated controllers
- Common monotonicity intuition is not uniformly beneficial in finite samples
- Different benchmarks prefer different controller classes under identical strict cross-validation
- Instance-level uncertainty signals can be exhausted at a distribution-dependent scale
- Regime theory uses three data-estimable bottlenecks for class choice
- Published on arXiv
Entities
Institutions
- arXiv