The paper evaluates six retrieval structures for multi‑tool requests in LLM agents, measuring how many required tools appear in the top‑k results and the associated LLM calls, tokens, and latency. Across two testbeds—345 AppWorld queries and 103 MCP‑Bench tasks—none of the untrained, single‑LLM‑call structures completes more than 10% of AppWorld queries at k = 16. A trained set‑level retriever and an LLM‑navigated hierarchy achieve higher completeness (up to 58% on MCP‑Bench) but at different costs and failure modes, with complete retrieval sets leading to 100% task success in a downstream AppWorld agent test.
By Shuyang Zhang
arXiv:2608. 16502v1 Announce Type: new Abstract: Large-scale agents increasingly rely on retrieval to access external capabilities.
By Yiqi Liu, Joseph James, Yang Wang, Chenghao Xiao, Chenghua Lin
arXiv:2610.23707v1 Announce Type: new
Abstract: A multi-step agent task fails when a single tool it needs is missing, yet the tools an agent may need are too many to list: the roughly 8,200 tools on...
By Sam Partee, River Stevens, Thierry Damiba, Valerie Fanelle
arXiv:2606. 12451v1 Announce Type: new Abstract: Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck.
By Ashutosh Hathidara, Sai Shruthi Sistla, Sebastian Schreiber, Sahil Bansal
arXiv:2606. 28376v1 Announce Type: cross Abstract: Long-horizon large language model (LLM) agents accumulate interaction trajectories that quickly exceed any practical prompt budget, and existing memory methods either truncate aggressively and lose non-local evidence or retain boilerplate that degrades decision quality.
By Mellow Baixuan Chen, Xiangguo Sun
The paper proposes typed federated artifacts—schema‑validated objects with per‑field privacy and dispute resolution—to enable tool‑routing knowledge sharing among frozen, heterogeneous LLM agents. By replacing flat text prompts with typed fields, the authors achieve near‑centralized routing performance on StableToolBench while reducing data size to 20 MB JSON per client. The study also highlights that a simple TF‑IDF classifier can outperform LLM routing on labeled benchmarks, indicating limitations in current evaluation methods.
By Abhijit Chakraborty, Ni Trieu, Vivek Gupta