LaQual: An Automated Framework for LLM App Quality Evaluation

Researchers introduce LaQual, an automated framework that evaluates the quality of LLM applications using dynamic scenario-based metrics rather than static user engagement indicators. The system demonstrates high alignment with human judgment and can filter out 67-81% of low-quality apps, addressing a critical gap in LLM app store curation.

The emergence of LLM app stores has created a discovery problem: while these platforms offer diverse tools for content generation, coding, and education, their ranking systems rely on crude metrics like user counts and favorites, leaving quality assessment to chance. LaQual addresses this by automating quality evaluation across three stages—initial app classification, static indicator filtering, and dynamic scenario-adapted testing—creating a scalable alternative to manual review.

The framework reflects broader infrastructure challenges in AI marketplaces. As LLM applications proliferate, platforms like OpenAI's GPT Store and similar ecosystems struggle to surface genuinely useful apps amid noise. Traditional app store models worked for mobile because functionality was bounded and testable; LLM apps operate in ambiguous domains where quality depends on context and user expectations. LaQual's approach of generating scenario-specific metrics represents a pragmatic solution: letting AI itself define evaluation criteria based on app type and use case.

The market implications are substantial. For developers, improved curation mechanisms could reduce the discoverability challenge that plagues emerging platforms. For users, automated quality filtering reduces decision fatigue and time spent evaluating apps. The validation metrics—achieving 66.7-81.3% filtering rates while maintaining human-judgment consistency—suggest the framework could meaningfully reshape how apps are ranked and recommended.

Future adoption hinges on standardization and integration into major platforms. If LaQual or similar frameworks become embedded in app store infrastructure, they could establish quality baselines that influence user trust and developer incentives, potentially accelerating the shift from engagement-based to quality-based discovery in AI marketplaces.

• →LaQual automates LLM app quality evaluation using dynamic metrics tailored to specific use cases rather than static user engagement data.
• →The framework filters 67-81% of candidate apps while maintaining high consistency with human quality judgments.
• →User studies show LaQual significantly outperforms baseline systems in comparison efficiency and explanatory value.
• →Current LLM app stores lack scalable quality evaluation mechanisms, creating friction for both users and developers.
• →Automated quality frameworks could reshape discovery and recommendation in emerging AI marketplaces.