Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching

• URL: http://arxiv.org/abs/2604.12126v1
• Date: Mon, 13 Apr 2026 23:14:32 GMT
• Title: Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching
• Abstract summary: Large Language Models (LLMs) have significantly advanced tool-augmented agents, enabling autonomous reasoning via API interactions.<n> executing multi-step tasks within massive tool libraries remains challenging due to two critical bottlenecks.<n>We first introduce SLATE, a large-scale context-aware benchmark designed for the automated assessment of tool-integrated agents.<n>We propose Entropy-Guided Branching (EGB), an uncertainty-aware search algorithm that dynamically expands decision branches where predictive entropy is high.
• Score: 21.203929965634853
• License: http://creativecommons.org/licenses/by/4.0/
• 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: (1) the absence of rigorous, plan-level evaluation frameworks and (2) the computational demand of exploring vast decision spaces stemming from large toolsets and long-horizon planning. To bridge these gaps, we first introduce SLATE (Synthetic Large-scale API Toolkit for E-commerce), a large-scale context-aware benchmark designed for the automated assessment of tool-integrated agents. Unlike static metrics, SLATE accommodates diverse yet functionally valid execution trajectories, revealing that current agents struggle with self-correction and search efficiency. Motivated by these findings, we next propose Entropy-Guided Branching (EGB), an uncertainty-aware search algorithm that dynamically expands decision branches where predictive entropy is high. EGB optimizes the exploration-exploitation trade-off, significantly enhancing both task success rates and computational efficiency. Extensive experiments on SLATE demonstrate that our dual contribution provides a robust foundation for developing reliable and scalable LLM agents in tool-rich environments.

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