ExecTune: Effective Steering of Black-Box LLMs with Guide Models

For large language models deployed through black-box APIs, recurring inference costs often exceed one-time training costs. This motivates composed agentic systems that amortize expensive reasoning into reusable intermediate representations. We study a broad class of such systems, termed Guide-Core Policies (GCoP), in which a guide model generates a structured strategy that is executed by a black-box core model. This abstraction subsumes base, supervised, and advisor-style approaches, which diffe

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ethics.ai (9 April 2026), “ExecTune: Effective Steering of Black-Box LLMs with Guide Models,” evidence record 6092, https://ethics.ai/record/6092 (originally published by arXiv).

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