Reliably constructing large-scale language models (LLMs) for complex, multi-step workflows is a critical challenge. Conventional approaches, such as optimizing individual prompts in a pipeline, struggle to achieve the formal compliance required for structured tasks. In this paper, we introduce the Type-Compliant Adaptation Cascades (TACs) framework, which reinterprets workflow adaptation as typed probabilistic program learning. TACs treats the entire workflow, consisting of a parameter-efficiently adapted LLM and deterministic logic, as a non-normalized joint distribution. This enables principled gradient-based learning even in potential intermediate structures. Furthermore, we demonstrate that optimization bias disappears as the model learns type compliance, providing a theoretical basis for an efficient optimization objective. Experimentally, TACs outperforms state-of-the-art prompt-optimization-based models. Specifically, on structured tasks, FinQA improved from 12.0% to 24.7% on the Qwen 3 8B model, MGSM-SymPy improved from 57.1% to 75.9% on the Gemma 2 27B model, MGSM improved from 1.6% to 27.3% on the Gemma 7B model, and MuSR improved from 36.5% to 62.6%. TACs provide a robust and theoretically supported paradigm for developing reliable and task-compliant LLM systems.
