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

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 further introduce a Streaming Abstention mechanism that operates on document segments sequentially and filters low-confidence responses through 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 URL.