arXiv:2508.18636v2 Announce Type: replace-cross Abstract: Representing a new paradigm in software distribution, LLM app stores are rapidly emerging, offering users diverse choices for content generation, coding assistance, education, and more. However, current ranking and recommendation mechanisms in LLM app stores predominantly rely on static metrics, such as user interactions and favorites, making it challenging for users to efficiently identify high-quality apps. At the same time, current academic research focuses on specific vertical fields and lacks a general, automated evaluation framewo
The rapid emergence of LLM app stores necessitates automated evaluation frameworks to ensure quality and reliability as the market matures.
The lack of robust quality evaluation hinders the adoption and trustworthiness of LLM applications, making it difficult for users to navigate a growing ecosystem.
The introduction of automated quality evaluation for LLM apps could standardize performance metrics and accelerate the development of more reliable AI agents.
- · LLM app users
- · LLM application developers prioritizing quality
- · AI quality assurance platforms
- · Low-quality LLM app developers
- · LLM app stores without robust evaluation
- · Manual app review processes
Automated evaluation tools will become critical infrastructure for LLM app ecosystems.
Higher quality LLM apps will accelerate enterprise adoption and integration of AI agents into workflows.
The increased reliability of AI agents could reshape white-collar productivity and software-as-a-service offerings.
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Read at arXiv cs.AI