表格空单元格会让 agent 解析出错：LlamaParse 用美联储 dot plot 演示对齐
LlamaIndex 拿美联储 dot plot 当例子，演示表格空单元格怎么让 agent 读错数，LlamaParse 解析后 9 个数字全对上。做文档解析的可以试试。
LlamaIndex 指出许多文档 OCR 工具遇到表格中的空单元格时，会让数值错位到错误的行列，导致下游数字解读出错。他们以美联储 2026 年 9 月的经济预测摘要（dot plot）为例：该表含 2029 列，而 6 月对比行在该列留空，是常见的静默失败点。用 LlamaParse 解析第 2 页后，GDP 中位数摘录的 9 个数字与原 PDF 全部一致，数据在返回的 HTML 中保持对齐。
blank cells in tables can mess up agent decision making many document OCR tools struggle with reading multiple blank cells in a document and cause values to be shifted from the original column and rows. this causes the wrong numerical interpretation downstream. This is a nice illustration of what happens! If you have complex documents where table accuracy is extremely important, come check out LlamaParse: cloud.llamaindex.ai/signup Your browser does not support the video tag. 🔗 View on Twitter LlamaIndex 🦙 @llama_index A blank cell can change the meaning of a forecast. Four times a year, the Fed's 18 top policymakers each put their forecasts on paper: where growth, jobs, inflation, and interest rates are headed. This is September 2026's edition, home of the "dot plot" that markets treat as the Fed tipping its hand. This release is the closest thing to the Fed saying what it plans to do. Most analysts will want to throw this documents to an AI agent, but this messy doc is dense: full of complex tables and charts that hold valuable context. All things that frequently trip up raw LLM APIs. The Fed’s September 2026 projections table includes a 2029 column, but its June comparison row leaves that cell empty. This is a common but silent failure point for document parsers. We parsed page 2 with LlamaParse and checked the displayed GDP median excerpt against the original PDF. All nine numbers matched and the data stays aligned in the returned HTML. Try LlamaParse on a table where headers and missing cells matter. cloud.llamaindex.ai/signup Source: federalreserve.gov/monetarypolicy… 🔗 View Quoted Tweet 💬 7 🔄 1 ❤️ 23 👀 2916 📊 10 ⚡
