arXiv:2605.28835v1 Announce Type: cross Abstract: Large Language Models (LLMs) extend their capabilities through function-calling (FC), which relies on training data with high quality, diversity, and broad coverage of scenario. However, obtaining and annotating real function-calling data is challenging, while synthetic data from existing pipelines often suffers from unreliable APIs, limited tool scalability, insufficient diversity, and weak quality control. To address these, we present GenesisFunc, an automated pipeline for generating FC training data. Starting from reliable tools in widely us
The rapid advancement of LLMs necessitates more sophisticated and reliable methods for function-calling data generation to overcome current limitations in data quality and diversity.
Improving function-calling data directly enhances the capabilities and reliability of AI agents, accelerating their deployment and usefulness across various applications.
The ability to automatically generate high-quality, diverse, and reliable function-calling datasets will significantly de-risk and speed up the development of advanced AI agent systems.
- · AI Agent Developers
- · LLM Providers
- · Automation Software Companies
- · Data Infrastructure Providers
- · Manual Data Annotators (for function-calling)
- · Companies reliant on low-quality synthetic data
- · Competitors with less robust data generation pipelines
More capable and generalizable AI agents become deployable in real-world scenarios due to improved function-calling accuracy.
Increased adoption of AI agents could lead to significant collapse in certain white-collar workflows and a shift in demand for human-computer interaction paradigms.
The enhanced autonomy and reliability of AI agents could accelerate broader societal integration, prompting new regulatory and ethical considerations around their deployment.
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