🤖AI Summary
Researchers introduce AgentDiet, a trajectory reduction technique that cuts computational costs for LLM-based agents by 39.9%-59.7% in input tokens and 21.1%-35.9% in total costs while maintaining performance. The approach removes redundant and expired information from agent execution trajectories during inference time.
Key Takeaways
- →AgentDiet reduces LLM agent computational costs by up to 59.7% in input tokens without performance loss.
- →The technique identifies and removes useless, redundant, and expired information from agent trajectories during execution.
- →Testing on coding agents showed 21.1%-35.9% reduction in total computational costs across two LLMs and benchmarks.
- →Efficiency concerns in LLM agent systems have been largely overlooked despite high computational costs.
- →Inference-time trajectory reduction represents a promising direction for optimizing agent system performance.
#llm#ai-agents#computational-efficiency#cost-reduction#trajectory-optimization#inference#arxiv#research
Read Original →via arXiv – CS AI
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