The emergence of AI-augmented Data Processing Systems (DPSs) has introduced powerful semantic operators that extend traditional data management capabilities with LLM-based processing. However, these systems face fundamental reliability (a.k.a. trust) challenges, as LLMs can generate erroneous outputs, limiting their adoption in critical domains. Existing approaches to LLM constraints--ranging from user-defined functions to constrained decoding--are fragmented, imperative, and lack semantics-aware integration into query execution. To address this gap, we introduce Semantic Integrity Constraints (SICs), a novel declarative abstraction that extends traditional database integrity constraints to govern and optimize semantic operators within DPSs. SICs integrate seamlessly into the relational model, allowing users to specify common classes of constraints (e.g., grounding and soundness) while enabling query-aware enforcement and optimization strategies. In this paper, we present the core design of SICs, describe their formal integration into query execution, and detail our conception of grounding constraints, a key SIC class that ensures factual consistency of generated outputs. In addition, we explore novel enforcement mechanisms, combining proactive (constrained decoding) and reactive (validation and recovery) techniques to optimize efficiency and reliability. Our work establishes SICs as a foundational framework for trustworthy, high-performance AI-augmented data processing, paving the way for future research in constraint-driven optimizations, adaptive enforcement, and enterprise-scale deployments.

翻译：AI增强数据处理系统的兴起引入了强大的语义算子，通过基于大语言模型的处理扩展了传统数据管理能力。然而，这些系统面临根本性的可靠性（即可信度）挑战，因为大语言模型可能生成错误输出，限制了其在关键领域的应用。现有的大语言模型约束方法——从用户定义函数到约束解码——呈现碎片化、命令式特征，且缺乏语义感知的查询执行集成。为弥补这一缺陷，我们提出了语义完整性约束，这是一种新颖的声明式抽象机制，通过扩展传统数据库完整性约束来管理和优化数据处理系统中的语义算子。该约束机制可无缝集成到关系模型中，使用户能够指定常见约束类别（例如事实基础性与合理性），同时支持查询感知的执行与优化策略。本文阐述了语义完整性约束的核心设计，描述了其与查询执行的正式集成方式，并详细说明了基础性约束这一关键类别的设计理念，该类约束可确保生成输出的事实一致性。此外，我们探索了结合主动式（约束解码）与反应式（验证与恢复）技术的新型执行机制，以优化系统效率与可靠性。本研究确立了语义完整性约束作为可信赖、高性能AI增强数据处理的奠基性框架，为约束驱动优化、自适应执行机制及企业级部署等未来研究方向开辟了道路。