AI RESEARCH
Reducing Cost of LLM Agents with Trajectory Reduction
arXiv CS.AI
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ArXi:2509.23586v2 Announce Type: replace-cross Multi-turn agent systems based on Large Language Models (LLMs) have become increasingly popular for software engineering tasks. While LLM agents nstrate promising effectiveness, the high computational cost of input tokens due to ever-growing trajectories remains a significant efficiency concern. Efficiency has been largely overlooked in existing studies and agent products, and this paper addresses this gap by