An Empirical Study of VLM Pipelines for Long-Document QA
Vision-Language Models (VLMs) are increasingly used for long-document processing, where the inputs combine text with charts, tables, figures, and complex layouts. Deploying them means choosing how to feed the document to the model, which retriever to use when only a subset of pages is sent, and whether to run the model agentically or as a static pipeline. We study these choices on two long-document QA benchmarks with both frontier API and open-weight VLMs. First, on MMLongBench-Doc our six-tool agent with page, table, figure, and search calls pays off only once the answering VLM is large enough: with Qwen3.5-4B and 9B it trails static page input, with Qwen3.5-27B it draws level, and with ...
| Authors | Kenan E. Ak, Jay Mohta, Gwang Gook Lee et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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