[2512.24933] ADOPT: Adaptive Dependency-Guided Joint Prompt Optimization for Multi-Step LLM Pipelines

Computer Science > Computation and Language arXiv:2512.24933 (cs) [Submitted on 31 Dec 2025 (v1), last revised 8 Apr 2026 (this version, v2)] Title:ADOPT: Adaptive Dependency-Guided Joint Prompt Optimization for Multi-Step LLM Pipelines Authors:Minjun Zhao, Xinyu Zhang, Shuai Zhang, Deyang Li, Ruifeng Shi View a PDF of the paper titled ADOPT: Adaptive Dependency-Guided Joint Prompt Optimization for Multi-Step LLM Pipelines, by Minjun Zhao and 4 other authors View PDF Abstract:Multi-step LLM pipelines can solve complex tasks, but jointly optimizing prompts across steps remains challenging due to missing step-level supervision and inter-step dependency. We propose ADOPT, an adaptive dependency-guided joint prompt optimization framework for multi-step LLM pipelines. ADOPT analyzes the dependency between each LLM step and the final output, constructs a global textual gradient from final-task errors, and decomposes it into step-level local textual gradients, providing more precise optimization signals for local prompt updates. It further decouples signal estimation from prompt updating, enabling flexible integration of single-prompt optimizers, and uses a Shapley-based strategy to adaptively allocate optimization resources to high-impact steps. Experiments on real-world datasets and structurally diverse pipelines demonstrate that ADOPT is effective and robust, consistently outperforming strong prompt optimization baselines. Subjects: Computation and Language (cs.CL); Machine Le...