AI RESEARCH
Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching
arXiv CS.AI
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ArXi:2604.12126v1 Announce Type: new 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