DocSLM: A Small Vision-Language Model for Long Multimodal Document Understanding

• URL: http://arxiv.org/abs/2511.11313v3
• Date: Fri, 21 Nov 2025 14:18:23 GMT
• Title: DocSLM: A Small Vision-Language Model for Long Multimodal Document Understanding
• Authors: Tanveer Hannan, Dimitrios Mallios, Parth Pathak, Faegheh Sardari, Thomas Seidl, Gedas Bertasius, Mohsen Fayyaz, Sunando Sengupta,
• Abstract summary: Large Vision-Language Models (LVLMs) have demonstrated strong multimodal reasoning capabilities on long and complex documents.<n>We present DocSLM, an efficient Small Vision-Language Model designed for long-document understanding under constrained resources.
• Score: 26.823523249232533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Vision-Language Models (LVLMs) have demonstrated strong multimodal reasoning capabilities on long and complex documents. However, their high memory footprint makes them impractical for deployment on resource-constrained edge devices. We present DocSLM, an efficient Small Vision-Language Model designed for long-document understanding under constrained memory resources. DocSLM incorporates a Hierarchical Multimodal Compressor that jointly encodes visual, textual, and layout information from each page into a fixed-length sequence, greatly reducing memory consumption while preserving both local and global semantics. To enable scalable processing over arbitrarily long inputs, we introduce a Streaming Abstention mechanism that operates on document segments sequentially and filters low-confidence responses using an entropy-based uncertainty calibrator. Across multiple long multimodal document benchmarks, DocSLM matches or surpasses state-of-the-art methods while using 82\% fewer visual tokens, 75\% fewer parameters, and 71\% lower latency, delivering reliable multimodal document understanding on lightweight edge devices. Code and Model are available in https://github.com/Tanveer81/DocSLM.git.

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