GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics

Researchers introduced GateLens, an LLM-based system that uses Relational Algebra as an intermediate layer to analyze complex tabular data more reliably than traditional approaches. The system demonstrated over 80% reduction in analysis time in automotive software analytics while maintaining high accuracy, outperforming existing Chain-of-Thought methods.

• →GateLens uses Relational Algebra as a formal bridge between natural language queries and executable code for tabular data analysis.
• →The system achieved over 80% reduction in analysis time compared to manual methods in automotive software release analytics.
• →GateLens outperformed existing Chain-of-Thought + Self-Consistency systems on real-world datasets, especially for complex queries.
• →The architecture operates effectively in zero-shot settings without requiring few-shot examples or complex agent orchestration.
• →Industrial deployment validated the system's reliability for safety-critical domains where analytical accuracy is paramount.