用数学语言桥接自然语言与代码，让AI更准更快分析汽车软件发布数据。
GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics
- 用关系代数做中间桥梁，把人话转成可执行代码
- 实测分析速度提升80%以上，复杂查询准确率更高
- 零样本即用，无需训练或调参，适合工业落地
在安全、合规或运营结果直接影响的领域，可靠的数据驱动决策至关重要。此类决策依赖大型表格数据，而人工分析效率低、成本高且易出错。尽管大语言模型（LLM）具备自动化潜力，但在分析推理、结构化数据处理和歧义消解方面仍面临挑战。本文提出GateLens，一种基于LLM的复杂表格数据分析架构。其核心创新在于采用关系代数（RA）作为自然语言推理与可执行代码之间的形式化中间表示，解决直接生成带来的推理到代码鸿沟。在汽车软件发布分析的应用中，GateLens将自然语言查询转化为RA表达式并生成优化的Python代码。相较于传统多智能体或规划系统，GateLens强调速度、透明性和可靠性。实验结果表明，在真实数据集上，它优于现有的链式思维（CoT）+ 自洽性（SC）系统，尤其在处理复杂和模糊查询时表现更优。消融实验验证了RA层的关键作用。工业部署显示，分析时间减少超80%，同时保持高准确性。系统可在零样本设置下运行，无需少样本示例或智能体编排。本工作推进了可部署的LLM系统设计，识别出中间形式表示、执行效率和低配置开销等关键架构特征，对领域特定分析应用具有重要意义。
原文摘要 · Abstract (English)
Ensuring reliable data-driven decisions is crucial in domains where analytical accuracy directly impacts safety, compliance, or operational outcomes. Decision support in such domains relies on large tabular datasets, where manual analysis is slow, costly, and error-prone. While Large Language Models (LLMs) offer promising automation potential, they face challenges in analytical reasoning, structured data handling, and ambiguity resolution. This paper introduces GateLens, an LLM-based architecture for reliable analysis of complex tabular data. Its key innovation is the use of Relational Algebra (RA) as a formal intermediate representation between natural-language reasoning and executable code, addressing the reasoning-to-code gap that can arise in direct generation approaches. In our automotive instantiation, GateLens translates natural language queries into RA expressions and generates optimized Python code. Unlike traditional multi-agent or planning-based systems that can be slow, opaque, and costly to maintain, GateLens emphasizes speed, transparency, and reliability. We validate the architecture in automotive software release analytics, where experimental results show that GateLens outperforms the existing Chain-of-Thought (CoT) + Self-Consistency (SC) based system on real-world datasets, particularly in handling complex and ambiguous queries. Ablation studies confirm the essential role of the RA layer. Industrial deployment demonstrates over 80% reduction in analysis time while maintaining high accuracy across domain-specific tasks. GateLens operates effectively in zero-shot settings without requiring few-shot examples or agent orchestration. This work advances deployable LLM system design by identifying key architectural features--intermediate formal representations, execution efficiency, and low configuration overhead--crucial for domain-specific analytical applications.
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