Jerry Liu(@jerryjliu0):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:

2026.09.25 20:39

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:

LlamaIndex 🦙@llama_index

2026.09.25 15:12

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. Source: