arXiv:2606.12451v1 Announce Type: new Abstract: Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck. As embedding-based retrieval approaches rely on compact encoders that may under-capture specialized tool semantics, parametric tool retrieval addresses this by encoding each tool as a virtual token appended to the LLM vocabulary, fine-tuned in two stages (memorization then retrieval SFT) to use the LLM as a retriever, achieving strong performance on standard ToolBench retrieval benchmarks. Yet these benchmarks use verbose, fully-specified
The proliferation of large language models deployed as agents over extensive tool catalogs necessitates novel diagnostic frameworks to understand and improve their performance in complex retrieval tasks.
This research addresses a critical bottleneck in the real-world deployment of AI agents by providing a method to audit and enhance their ability to effectively use tools, which is fundamental for their utility and reliability.
The proposed 'ToolSense' framework offers a structured way to evaluate and improve the 'parametric tool knowledge' of LLMs, potentially leading to more robust and accurate AI agents capable of navigating diverse tool environments.
- · AI Agent developers
- · Companies deploying LLMs in agentic workflows
- · Researchers in LLM interpretability
- · Companies relying on inefficient tool retrieval methods
- · Legacy AI agent architectures without audit mechanisms
Improved performance and reliability of AI agents leveraging large tool catalogs.
Accelerated adoption of AI agents in complex enterprise applications due to enhanced trust and efficacy.
New competitive landscape where the quality of an LLM's tool-use capabilities becomes a key differentiator.
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Read at arXiv cs.AI