AI summaryⓘ
The authors explain that a lot of useful information is stuck in unorganized documents like reports and PDFs, making it expensive for AI agents to answer complicated questions because they have to search through large texts repeatedly. They propose a new method called agentic data cracking, which helps AI agents organize important information from documents while they are answering questions, so future questions can be answered faster and cheaper. This approach works by adapting to the kinds of questions asked and even guessing what related information might be needed later. Their tests show this method can significantly reduce costs while keeping answers accurate. Overall, the authors suggest this method as a new way to help AI handle unstructured data more efficiently over time.
unstructured datalarge language modelsagentic reasoningdata structuringretrieval-augmented generationFanOutQA benchmarkspeculative extractioncost efficiencydocument understandingadaptive processing
Abstract
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.