基于非结构化数据自适应结构化的令牌高效数据推理智能体

摘要

宝贵的数据仍嵌入于非结构化来源中：网页、报告、合同、申报文件、财报电话会议记录和PDF文档。企业AI领域的重大布局在于部署LLM智能体，使其能够对这些数据进行推理，为每位知识工作者解答复杂问题。如今智能体已能胜任此项任务，但成本高得令人却步。每个问题都会反复打开大型文档以检索分散的证据，消耗多达百万级token。然而，如果数据已经结构化，同样的问题只需进行一次廉价的数据库查询即可解决。例如，在FanOutQA基准测试中，基于理想预结构化存储的推理成本仅为原有的1/28，且随着问题在更多文档中扇出，这一差距可扩大至数量级。然而，预先将所有内容结构化并不可行：文档所蕴含的可能结构远超任何工作负载实际所需，而且有用的结构和文档在查询到来之前无从知晓。我们提出智能体数据破解（agentic data cracking）方法，该方法将非结构化数据作为推理过程本身的副产品，进行自适应性和推测性的结构化。其自适应性体现在观测到的查询决定了何时进行结构化以及何种结构最为重要；其推测性体现在它不仅服务于当前问题。每当智能体打开文档进行作答时，一个破解子智能体从已加载的上下文中以边际成本分叉而出，提取可能服务于未来相关查询的基于事实的结构。随着时间的推移，越来越多的查询完全由结构化数据覆盖，无需打开文档即可作答，从而使智能体的准确性保持在接近RAG成本的水平。在FanOutQA上，若每个测试问题仅附带一个相关问题进行扩展，破解方法可在保持准确性的同时将成本降低53%。智能体数据破解是迈向面向非结构化数据智能体推理的下一代数据基础设施的第一步：一个位于模型之下的共享基座，推理过程中已付出代价挖掘出的知识在此不断积累。

English

Valuable data remains embedded in unstructured sources: web pages, reports, contracts, filings, earnings calls, and PDFs. The big bet in enterprise AI is deploying LLM agents that reason over this data to answer complex questions for every knowledge worker. Agents can do this today, but at prohibitive cost. Each question repeatedly opens large documents to recover scattered evidence, consuming up to a million tokens. However, if the data were already structured, the same question would reduce to a cheap database lookup. For example, on FanOutQA benchmark, reasoning over an ideal pre-structured store is 28X cheaper, and the gap grows to orders of magnitude as questions fan out over more documents. Yet structuring everything in advance is not viable: documents hold vastly more possible structure than any workload will use, and the useful structure and documents are unknown until queries arrive. We propose agentic data cracking, a method that structures unstructured data adaptively and speculatively as a byproduct of reasoning itself. Structuring is adaptive because observed queries decide when it happens and what matters, and speculative because it goes beyond the current question. Whenever the agent opens a document to answer, a cracking sub-agent forks from the already-loaded context at marginal cost and extracts grounded structure likely to serve related future queries. Over time, an increasing share of queries is fully covered by structured data and answered without opening a document, keeping agentic accuracy at close to RAG cost. On FanOutQA, extended with merely one related question per test question, cracking cuts cost by 53% while preserving accuracy. Agentic data cracking is a first step toward next-generation data infrastructure for agentic reasoning over unstructured data: a shared substrate beneath the model where knowledge that reasoning already paid to uncover accumulates.