Directly understanding and adjusting the internal logic of black-box algorithms

Determine effective techniques to directly understand and adjust the internal decision logic of black-box algorithms, without relying on distilled interpretable surrogates, so that logic-level modifications can be applied in practice.

Background

Crucible evaluates and improves the tuning potential of control algorithms by allowing logic-level modifications guided by an LLM agent. However, this approach currently relies on algorithms whose internal structure is accessible and amenable to modification.

The authors note that they cannot directly modify the internal logic of black-box algorithms and instead analyze decision trees distilled from such models. They explicitly state that effectively understanding and adjusting the internal logic of black-box algorithms remains an open challenge, indicating a key unresolved barrier to extending Crucible’s methodology to opaque models (e.g., complex learned policies).