arXiv:2605.29271v1 Announce Type: cross Abstract: Tool retrieval over large API catalogs is a core bottleneck for LLM agents: user queries arrive in colloquial, often underspecified language, while the catalog uses technical API vocabulary that no fixed encoder can bridge on its own. The two dominant training approaches, contrastive encoder fine-tuning and HyDE-style query expansion with a frozen LLM, address this problem from opposite ends and fail in complementary directions: the fine-tuned encoder excels when the query's surface form already matches the catalog but collapses when it does no
The proliferation of LLMs and the increasing complexity of their applications, particularly in agentic systems, highlight the urgent need for more effective tool retrieval mechanisms to bridge natural language and technical APIs.
Improving tool retrieval directly enhances the utility and autonomy of AI agents by allowing them to interface more effectively with vast catalogs of specialized functions, reducing current bottlenecks.
The ability of LLM agents to accurately and efficiently identify and utilize external tools through more robust query expansion and encoding will significantly improve, reducing development friction and expanding their practical capabilities.
- · AI Agent developers
- · Enterprises with large API catalogs
- · Cloud service providers
- · Software developers
- · Companies relying on manual API integration
- · Less sophisticated AI search/retrieval methods
LLM agents become more capable and reliable in complex, multi-tool environments.
Increased adoption of LLM agents across various industries as their performance in specialized tasks improves.
Accelerated collapse of some white-collar workflows and SaaS layers as agents autonomously handle more sophisticated tasks previously requiring human intervention.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG