Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching
📰 ArXiv cs.AI
Learn to execute long-horizon plans in large tool spaces using entropy-guided branching, overcoming bottlenecks in plan evaluation and decision space exploration
Action Steps
- Apply entropy-guided branching to explore vast decision spaces in large tool libraries
- Evaluate plan-level performance using rigorous frameworks to identify bottlenecks
- Execute multi-step tasks using autonomous agents with API interactions
- Optimize computational demand by selecting the most informative branches
- Test the entropy-guided branching approach in various tool spaces to validate its effectiveness
Who Needs to Know This
Researchers and engineers working on autonomous agents and Large Language Models (LLMs) can benefit from this technique to improve plan execution in complex tool spaces
Key Insight
💡 Entropy-guided branching can efficiently explore vast decision spaces and improve plan execution in large tool spaces
Share This
🤖 Execute long-horizon plans in large tool spaces with entropy-guided branching! 🚀
Key Takeaways
Learn to execute long-horizon plans in large tool spaces using entropy-guided branching, overcoming bottlenecks in plan evaluation and decision space exploration
Full Article
Title: Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching
Abstract:
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: (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
Abstract:
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: (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
DeepCamp AI