arXiv:2606.06566v1 Announce Type: cross Abstract: Agentic tool-calling language models depend on large registries of callable APIs, functions, and local actions. Placing full tool specifications directly in the prompt incurs a cost that scales linearly with the size of the tool registry, rapidly consuming the context budget. As the registry grows, this leads to higher latency and degrades selection accuracy, particularly due to interference from irrelevant tools. We overcome these limitations by introducing NTILC, a neural tool selection and invocation framework that replaces in-context regist
The development of more sophisticated AI agents and the expansion of callable API registries necessitate new methods for efficient tool invocation to overcome current context window limitations.
This development addresses a critical scaling bottleneck for agentic AI, potentially accelerating their adoption and capability by making tool use more efficient and less resource-intensive.
Current linear scaling costs for agentic tool invocation are replaced with a more efficient, learned compression method, allowing AI agents to utilize larger tool registries without performance degradation.
- · AI Agent developers
- · API providers
- · Cloud infrastructure providers
- · Software developers
- · Inefficient AI agent architectures
- · Companies reliant on simple, static tool integration methods
AI agents can access and utilize a much wider array of tools and functions, enhancing their versatility and problem-solving capabilities.
This improved efficiency could lead to a rapid expansion of AI agent applications across various industries, collapsing more white-collar workflows.
The increased sophistication of AI agents, driven by better tool invocation, might accelerate the deployment of autonomous systems, further impacting labor markets and operational structures.
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Read at arXiv cs.AI