Select-Then-Decompose: From Empirical Analysis to Adaptive Selection Strategy for Task Decomposition in Large Language Models

2025-10-20

AI Summary

Existing task decomposition methods for large language models predominantly focus on tool invocation and feedback mechanisms, overlooking the critical trade-off between performance and computational cost. Method: This paper proposes a “select-then-decompose” paradigm, establishing a closed-loop framework comprising three stages: selection, execution, and verification. First, we systematically categorize six decomposition patterns and identify key task features governing performance and cost. Second, we design a dynamic selection mechanism that adaptively identifies the optimal decomposition strategy per task. Third, we integrate a lightweight verification module to ensure result reliability. Contribution/Results: Our approach consistently achieves Pareto-optimal performance across multiple benchmarks—significantly improving inference efficiency while reducing computational overhead compared to fixed decomposition strategies. The implementation is publicly available.

Technology Category

• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large language models (LLMs) have demonstrated remarkable reasoning and planning capabilities, driving extensive research into task decomposition. Existing task decomposition methods focus primarily on memory, tool usage, and feedback mechanisms, achieving notable success in specific domains, but they often overlook the trade-off between performance and cost. In this study, we first conduct a comprehensive investigation on task decomposition, identifying six categorization schemes. Then, we perform an empirical analysis of three factors that influence the performance and cost of task decomposition: categories of approaches, characteristics of tasks, and configuration of decomposition and execution models, uncovering three critical insights and summarizing a set of practical principles. Building on this analysis, we propose the Select-Then-Decompose strategy, which establishes a closed-loop problem-solving process composed of three stages: selection, execution, and verification. This strategy dynamically selects the most suitable decomposition approach based on task characteristics and enhances the reliability of the results through a verification module. Comprehensive evaluations across multiple benchmarks show that the Select-Then-Decompose consistently lies on the Pareto frontier, demonstrating an optimal balance between performance and cost. Our code is publicly available at https://github.com/summervvind/Select-Then-Decompose.

Problem

• Optimizing task decomposition balance between performance and cost
• Dynamically selecting decomposition methods based on task characteristics
• Establishing closed-loop verification for reliable problem-solving results

Innovation

• Dynamic selection of decomposition approach based on task characteristics
• Closed-loop process with selection, execution, and verification stages
• Verification module enhances reliability of decomposition results