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arXiv AI By Uiji Hwang, Richeng Xuan, Taeuk Kim

Rethinking the Role of Tool Graph Memory in Multi-Turn Tool Use

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The paper investigates the effectiveness of tool graph memory—a component that records past tool transitions to guide future tool selection—in large language model agents performing multi‑turn tasks. Across nine models and two benchmarks, the authors find that tool graph memory rarely yields significant performance gains, often incurs more than twice the number of LLM calls, and its guidance is frequently incorrect. They observe that random or no guidance can achieve similar improvements, suggesting the benefits arise from additional computation rather than the memory itself, and that storing experiences as textual insights outperforms the graph approach with minimal extra calls.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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