With the power of LLMs, we now have the ability to query data that was previously impossible to query, including text, images, and video. However, despite this enormous potential, most present-day data systems that leverage LLMs are reactive, reflecting our community's desire to map LLMs to known abstractions. Most data systems treat LLMs as an opaque black box that operates on user inputs and data as is, optimizing them much like any other approximate, expensive UDFs, in conjunction with other relational operators. Such data systems do as they are told, but fail to understand and leverage what the LLM is being asked to do (i.e. the underlying operations, which may be error-prone), the data the LLM is operating on (e.g., long, complex documents), or what the user really needs. They don't take advantage of the characteristics of the operations and/or the data at hand, or ensure correctness of results when there are imprecisions and ambiguities. We argue that data systems instead need to be proactive: they need to be given more agency – armed with the power of LLMs – to understand and rework the user inputs and the data and to make decisions on how the operations and the data should be represented and processed. By allowing the data system to parse, rewrite, and decompose user inputs and data, or to interact with the user in ways that go beyond the standard single-shot query-result paradigm, the data system is able to address user needs more efficiently and effectively. These new capabilities lead to a rich design space where the data system takes more initiative: they are empowered to perform optimization based on the transformation operations, data characteristics, and user intent. We discuss various successful examples of how this framework has been and can be applied in real-world tasks, and present future directions for this ambitious research agenda.
Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by integrating Graph Neural Networks (GNNs) for predictive analysis and reasoning over incomplete or noisy data. However, NGDBs rely on predefined queries and lack autonomy and adaptability. This paper introduces Agentic Neural Graph Databases (Agentic NGDBs), which extend NGDBs with three core functionalities: autonomous query construction, neural query execution, and continuous learning. We identify ten key challenges in realizing Agentic NGDBs: semantic unit representation, abductive reasoning, scalable query execution, and integration with foundation models like large language models (LLMs). By addressing these challenges, Agentic NGDBs can enable intelligent, self-improving systems for modern data-driven applications, paving the way for adaptable and autonomous data management solutions.
Recent years have witnessed major technical breakthroughs in AI- facilitated by tremendous data and high-performance computers, large language models (LLMs) have brought disruptive progress to information technology from accessing data to performing analysis. While demonstrating unprecedented capabilities, LLMs have been found unreliable in tasks requiring factual knowledge and rigorous reasoning. Despite recent works discussing the hallucination problem of LLMs, systematic studies on empowering LLMs with the ability to plan, reason, and ground with explicit knowledge are still lacking. On the other hand, real-world data are enormous and complex, coming from different sources and bearing various modalities. Data professionals have spent tremendous efforts collecting and curating countless datasets with different schemas and standards. Transforming the separate datasets into unified knowledge graphs (KGs) can facilitate their integrative analysis and utilization, but these processes would often require strong domain expertise and significant human labor. In this paper, we discuss recent progress and promise in the co-learning of KGs and LLMs, through LLM-aided KG construction, KG-guided LLM enhancement, and knowledge-aware multi-agent federation, particularly emphasizing a structure-oriented retrieval augmented generation (SRAG) paradigm, towards fully utilizing the value of complex data, unleashing the power of generative models, and expediting next-generation trustworthy AI.
