Adaptive Dependency-aware Prompt Optimization Framework for Multi-Step LLM Pipeline

• URL: http://arxiv.org/abs/2512.24933v1
• Date: Wed, 31 Dec 2025 15:46:37 GMT
• Title: Adaptive Dependency-aware Prompt Optimization Framework for Multi-Step LLM Pipeline
• Authors: Minjun Zhao, Xinyu Zhang, Shuai Zhang, Deyang Li, Ruifeng Shi,
• Abstract summary: We propose ADOPT, an Adaptive Dependency-aware Prompt Optimization framework for multi-step LLM pipelines.<n> ADOPT explicitly models the dependency between each LLM step and the final task outcome, enabling precise text-gradient estimation.<n>Experiments on real-world datasets and diverse pipeline structures show that ADOPT is effective and robust.
• Score: 9.013236765328301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-step LLM pipelines invoke large language models multiple times in a structured sequence and can effectively solve complex tasks, but their performance heavily depends on the prompts used at each step. Jointly optimizing these prompts is difficult due to missing step-level supervision and inter-step dependencies. Existing end-to-end prompt optimization methods struggle under these conditions and often yield suboptimal or unstable updates. We propose ADOPT, an Adaptive Dependency-aware Prompt Optimization framework for multi-step LLM pipelines. ADOPT explicitly models the dependency between each LLM step and the final task outcome, enabling precise text-gradient estimation analogous to computing analytical derivatives. It decouples textual gradient estimation from gradient updates, reducing multi-prompt optimization to flexible single-prompt optimization steps, and employs a Shapley-based mechanism to adaptively allocate optimization resources. Experiments on real-world datasets and diverse pipeline structures show that ADOPT is effective and robust, consistently outperforming state-of-the-art prompt optimization baselines.

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