Whole Slide Image (WSI) MLLMs are difficult to build and deploy because gigapixel slides induce thousands of visual tokens, while only a small fraction of regions is diagnostically relevant. Existing slide-level pathology MLLMs typically combine heavy slide-level encoders with long visual prefixes, making end-to-end slide-level development and deployment expensive under limited computational resources. We revisit this regime and show that WSI tile features are highly redundant at both global and local scales, while task-relevant evidence is sparse and query-dependent. We therefore introduce LoC-Path, a resource-efficient slide-level MLLM that compresses before fusion. LoC-Path uses a Sparse Token Merger (STM) and an MAE-pretrained resampler to replace expensive slide-level encoding with a compact latent interface, then uses a Token Importance Scorer (TIS) to select the most relevant latents and a Cross-Attention Routing Adapter (CARA) to fuse them into a few LLM decoder layers. This design lowers both multimodal tuning cost and inference-time latency/memory by avoiding heavy slide-level encoding and long visual prefixes. Extensive experiments show that LoC-Path remains competitive with prior slide-level MLLMs while making end-to-end development and deployment more practical under limited computational resources.

翻译：全切片图像（WSI）多模态大语言模型的构建与部署面临巨大挑战，因为千兆像素级的切片会产生数千个视觉标记，而其中仅有一小部分区域具有诊断相关性。现有的切片级病理学多模态大语言模型通常将笨重的切片级编码器与冗长的视觉前缀相结合，导致在有限计算资源下进行端到端切片级开发与部署的成本极高。我们重新审视了这一范式，并发现WSI图块特征在全局和局部尺度上均存在高度冗余，而任务相关的证据则稀疏且依赖于查询。为此，我们提出了LoC-Path——一种在融合前进行压缩的资源高效型切片级多模态大语言模型。LoC-Path采用稀疏标记合并器与基于MAE预训练的重采样器，以紧凑的潜在接口替代昂贵的切片级编码；随后通过标记重要性评分器筛选最相关的潜在特征，并利用交叉注意力路由适配器将其融合至少数大语言模型解码层中。该设计通过避免笨重的切片级编码与冗长的视觉前缀，显著降低了多模态调优成本及推理时的延迟与内存占用。大量实验表明，LoC-Path在性能上与先前的切片级多模态大语言模型保持相当，同时在有限计算资源下使端到端开发与部署更具可行性。