arXiv:2605.29561v1 Announce Type: new Abstract: Tool calling extends large language models (LLMs) by enabling grounded interaction with external executable interfaces, thereby supporting environment-coupled problem solving. However, mainstream in-context learning (ICL) approaches typically incorporate detailed tool documentation and usage examples directly into the context. This results in substantial inference overhead and heightened risks of hallucination as the context length grows. Conversely, while tuning-based methods improve general tool-calling capabilities, they often fail to effectiv
The rapid advancement and adoption of large language models (LLMs) necessitate more efficient and robust methods for tool interaction to overcome current limitations like inference overhead and hallucination.
This research addresses fundamental inefficiencies in how LLMs interact with external tools, potentially leading to more scalable, reliable, and powerful AI systems for complex problem-solving.
The shift from in-context learning to parameter-based tool representation for LLMs could significantly reduce computational costs and improve stability, making AI agents more practical and effective.
- · AI agent developers
- · Cloud computing providers
- · Software-as-a-Service (SaaS) platforms
- · LLM developers
- · Inefficient AI tool integration methods
LLMs can integrate more complex and numerous tools with less computational overhead and reduced errors.
The development and deployment of highly autonomous AI agents accelerate, collapsing certain white-collar workflows.
The increased efficiency and capability of AI agents drive further AI adoption across industries, reshaping business models and job functions.
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Read at arXiv cs.AI