SPMR: Continual Tool Retrieval Learning via Multi-Path Structured Prompt Memory Recovery

Tool retrieval determines which capabilities a large language model (LLM) agent can actually exercise at any given moment. Generative tool retrieval internalizes tool knowledge into model parameters, turning retrieval from context enumeration into a learned mapping. As deployed tool inventories keep growing, however, a parameterized catalogue turns every tool registration into a model update, and extending a generative tool retriever becomes a continual learning problem. Existing continual learning methods largely attribute forgetting to knowledge loss and respond by freezing or otherwise protecting historical parameters. We argue that preserved parameters do not imply preserved retrieval performance, and decompose retrieval failure into three separable mechanisms: Storage, whether historical parameters survive; Access, whether the router can still reach them; and Competition, whether newly added labels displace historical targets even when their logits are unchanged. Motivated by this view, we propose a prompt-based generative tool retriever with a structured prompt memory of box-indexed soft prompts and a multi-path recovery memory mechanism that restores access to historical tools displaced by newly added tools.On ToolBench and ToolRet, our method surpasses most continual learning baselines. These results indicate that forgetting in continual retrieval should be measured along storage, access, and competition separately, rather than attributed to a single memory problem.