Utility-Guided Agent Orchestration for Efficient LLM Tool Use
#LLM #agent orchestration #utility-guided #tool use #efficiency #computational cost #automation
📌 Key Takeaways
- Utility-guided agent orchestration optimizes LLM tool usage by prioritizing tasks based on utility.
- The approach enhances efficiency by dynamically selecting and sequencing tools for complex tasks.
- It reduces computational costs and improves response times in multi-tool LLM applications.
- The framework is designed to scale across various domains requiring automated tool integration.
📖 Full Retelling
arXiv:2603.19896v1 Announce Type: new
Abstract: Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable but inflexible, while free-form multi-step reasoning methods such as ReAct may improve task performance at the expense of excessive tool calls, longer trajectories, higher token consumption, and increased latency. In this paper, we study agent orchestration as an explicit decision problem rather than l
🏷️ Themes
AI Efficiency, Tool Orchestration
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Original Source
arXiv:2603.19896v1 Announce Type: new
Abstract: Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable but inflexible, while free-form multi-step reasoning methods such as ReAct may improve task performance at the expense of excessive tool calls, longer trajectories, higher token consumption, and increased latency. In this paper, we study agent orchestration as an explicit decision problem rather than l
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