Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching
Summary: arXiv:2604.12126v1 Announce Type: new
Abstract
Large Language Models (LLMs) have significantly advanced tool-augmented agents, enabling autonomous reasoning via API interactions. However, executing multi-step tasks within massive tool libraries remains challenging due to two critical bottlenecks:
- The absence of rigorous, plan-level evaluation frameworks.
- The computational demand of exploring vast decision spaces stemming from large toolsets and long-horizon planning.
Introduction
As the field of artificial intelligence continues to evolve, the integration of large language models into practical applications has seen significant advancements. Particularly in the realm of tool-augmented agents, the ability to autonomously reason and interact with APIs has opened new frontiers. However, despite these advancements, executing complex multi-step tasks within extensive tool libraries presents notable challenges.
Challenges in Tool-Augmented Agents
Two primary bottlenecks hinder the efficiency of tool-augmented agents:
- Lack of Evaluation Frameworks: Current methodologies often lack rigorous frameworks for evaluating plans on a holistic level, making it difficult to assess the effectiveness of agents in real-world scenarios.
- Computational Demands: The expansive nature of toolsets and the intricacies of long-horizon planning create a computational burden that can overwhelm existing systems, leading to inefficiencies.
Introducing SLATE
To address these challenges, we introduce SLATE (Synthetic Large-scale API Toolkit for E-commerce), a benchmark designed to automate the assessment of tool-integrated agents. SLATE differs from traditional metrics by accommodating diverse yet functionally valid execution trajectories. Our findings suggest that current agents frequently struggle with self-correction and exhibit low search efficiency.
Entropy-Guided Branching (EGB)
In response to the limitations identified through SLATE, we propose a novel approach known as Entropy-Guided Branching (EGB). This uncertainty-aware search algorithm focuses on dynamically expanding decision branches in areas where predictive entropy is high. By optimizing the exploration-exploitation trade-off, EGB substantially improves both task success rates and computational efficiency.
Experimental Validation
We conducted extensive experiments utilizing the SLATE benchmark to evaluate the effectiveness of our dual contributions. The results demonstrate that EGB significantly enhances the performance of tool-augmented agents, offering a robust foundation for developing reliable and scalable LLM agents in tool-rich environments.
Conclusion
The advancements presented in this study highlight the potential for significant improvements in the execution of multi-step tasks within large tool spaces. By integrating SLATE as a comprehensive evaluation framework and employing EGB for optimized decision-making, we pave the way for the future development of efficient and effective tool-augmented agents.