It is currently February 24, 2026, and while AI has made massive leaps, the “PDF problem” remains one of the most persistent bottlenecks in the industry.
Even with the release of multimodal models like GPT-5.2 and Gemma 3, AI still fundamentally “hallucinates” layouts because of how PDFs were designed in the 1990s—not as data files, but as digital paper.
The 3 Core Reasons Why AI Still Struggles
1. The “Tokenization” vs. “Vision” Gap
Most AI models still try to “read” a PDF by converting it into a linear stream of text (tokenization).
- The Issue: When a PDF is serialized into text, the spatial relationships are destroyed.
- The Result: The AI might read the first word of “Column A” followed by the first word of “Column B” rather than reading all of Column A first. Even in 2026, leading models only hit an average F1 score of ~55% on complex medical or legal extractions when using standard text-parsing.
2. The “Hidden” Data Layer
PDFs often contain multiple layers: the visual image, the OCR text layer, and metadata.
- The Issue: Often, the underlying text layer is “junk”—misaligned characters or hidden text from previous edits.
- The Result: AI models frequently trust this invisible, messy text layer over what is actually visible in the image, leading to “ghost” words or numbers that don’t exist on the page.
3. Table Hierarchy & Merged Cells
Tables are the “final boss” of PDF parsing.
- The Issue: AI struggles to understand that a header might span three columns, or that a blank cell actually inherits the value from the cell above it.
- The Result: In recent February 2026 benchmarks, even advanced parsers like Dolphin (by ByteDance) occasionally shuffled heading orders or mis-parsed currency symbols (e.g., parsing
$as$/$), which breaks automated financial workflows.
State of the Art in Feb 2026: What’s Changing?
The industry is moving away from “Reading” PDFs and toward “Seeing” them.
| Technology | Why it matters |
| Vision-Language Models (VLMs) | Models like ColPali and Qwen2.5-VL treat each page as an image first. They “look” at the layout like a human, preserving table structures. |
| Agentic PDF Extraction | Tools from LandingAI (backed by Andrew Ng) use “Agents” that can self-correct. If the AI is confused by a table, it “re-reads” specific coordinates to verify the data. |
| Markdown Pre-processing | The current gold standard is converting PDFs to Markdown before the AI sees them. Tools like LlamaParse and Docling are 41% more accurate than legacy OCR. |
The “New Delhi Declaration” Impact
Following the AI Impact Summit in New Delhi last week (Feb 19, 2026), 89 countries endorsed a plan to build the “Trusted AI Commons.” One of its primary goals is to standardize “Machine-Readable PDF Tags” globally, which would finally allow AI to understand document structures without having to “guess” the layout.
