• As OCR corruption increases, performance gaps between different models narrow — a larger model's advantage shrinks because OCR quality, not model capability, becomes the dominant bottleneck.
• The paper identifies three recurring failure modes under noisy OCR input specifically: key-value misalignment, hallucination, and numeric corruption, rather than a uniform across-the-board accuracy drop.
• Larger models' gains over smaller ones hold on clean text but diminish substantially once OCR noise is introduced, since extraction quality then depends on preserved textual fidelity rather than semantic reasoning capacity alone.

• benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• hallucination — When a model states something false with full confidence — inventing facts, citations, or APIs that don't exist.

Shows LLM document-extraction quality holds up on clean text but degrades sharply once real OCR noise enters the pipeline, worth testing before shipping document AI features.

• articleDo large language models scrutinise what they review? A multimodal audit of scoring calibration, error detection, and author-identity effectsEmad Alharbi
• articleCompliance, Capability, and Conflict: Benchmarking Multimodal LLMs under System MessagesJuan Yeo, Geewook Kim
• postGLM-OCR puts document parsing into a 0.9B open modelZ.ai

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