Vision Language Models (VLMs) have shown strong performance on multimodal reasoning tasks, yet most evaluations focus on short videos and assume unconstrained computational resources. In industrial settings such as pharmaceutical content understanding, practitioners must process long-form videos under strict GPU, latency, and cost constraints, where many existing approaches fail to scale. In this work, we present an industrial GenAI framework that processes over 200,000 PDFs, 25,326 videos across eight formats (e.g., MP4, M4V, etc.), and 888 multilingual audio files in more than 20 languages. Our study makes three contributions: (i) an industrial large-scale architecture for multimodal reasoning in pharmaceutical domains; (ii) empirical analysis of over 40 VLMs on two leading benchmarks (Video-MME and MMBench) and proprietary dataset of 25,326 videos across 14 disease areas; and (iii) four findings relevant to long-form video reasoning: the role of multimodality, attention mechanism trade-offs, temporal reasoning limits, and challenges of video splitting under GPU constraints. Results show 3-8 times efficiency gains with SDPA attention on commodity GPUs, multimodality improving up to 8/12 task domains (especially length-dependent tasks), and clear bottlenecks in temporal alignment and keyframe detection across open- and closed-source VLMs. Rather than proposing a new "A+B" model, this paper characterizes practical limits, trade-offs, and failure patterns of current VLMs under realistic deployment constraints, and provide actionable guidance for both researchers and practitioners designing scalable multimodal systems for long-form video understanding in industrial domains.

翻译：视觉语言模型（VLMs）在多模态推理任务中展现出强大性能，但现有评估大多聚焦于短视频且假设计算资源不受限。在药物内容理解等工业场景中，从业者必须在严格的GPU、延迟和成本约束下处理长视频，而许多现有方法难以实现规模化。本研究提出一个工业级生成式人工智能框架，处理了超过20万份PDF文件、涵盖八种格式（如MP4、M4V等）的25,326个视频，以及超过20种语言的888个多语言音频文件。我们的研究作出三项贡献：（i）面向药物领域多模态推理的工业级大规模架构；（ii）在两大主流基准（Video-MME与MMBench）及包含14个疾病领域25,326个视频的专有数据集上对超过40个VLM的实证分析；（iii）关于长视频推理的四项发现：多模态的作用、注意力机制权衡、时序推理局限，以及GPU约束下视频分割的挑战。实验结果表明：在商用GPU上采用SDPA注意力机制可获得3-8倍的效率提升；多模态技术可改善8/12任务领域（尤其长度依赖型任务）；开源与闭源VLM在时序对齐和关键帧检测方面均存在明显瓶颈。本文并未提出新的"A+B"模型，而是刻画了当前VLM在现实部署约束下的实际局限、权衡与失效模式，为研究者和从业者设计面向工业领域长视频理解的可扩展多模态系统提供了可操作的指导。