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

MCML Authors

Tanveer Hannan

→ Group Thomas Seidl
 Database Systems, Data Mining and AI

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

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 is available in the supplementary material.

misc HMP+25

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Preprint

Nov. 2025

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Authors

T. Hannan • D. Mallios • P. Pathak • F. Sardari • T. Seidl • G. Bertasius • M. Fayyaz • S. Sengupta

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Links

arXiv

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Research Area

 A3 | Computational Models