把1000个向量压成4个，检索精度反而更高
视觉文档搜索的顶尖方案有个死穴：每页要存约1000个向量，存储成本高得吓人。以前压缩就是砍掉一部分或取平均值，存储一紧张精度就崩。这篇发现了一个反直觉的事实：这些向量其实都挤在一个只有五六个维度的“曲面”上，根本不需要全存。于是他们只存4个“代表向量”，查询时先用这4个快速筛出候选，再现场把完整向量“生成”出来做精确重排。4个向量就能保住原来近80%的精度，而老办法只能保住70%。更妙的是，这套压缩不用重新训练模型，只用一个41万参数的小网络、花3分钟GPU时间就搞定。它不是你明天能直接用的工具，但“按需重建证据”这个思路，给存储受限的检索系统开了一条新路。
📄 原文摘要(英文)
Late-interaction retrieval is the state-of-the-art for visual document search, but it pays for its accuracy in storage. Existing compression methods retain a subset or local average of the N~1,000 vectors per page. Under aggressive storage budgets, however, these methods degrade sharply, and alternatives require retraining the encoder. Investigating this degradation across three encoders, we found two consistent properties: the vectors lie exactly on the unit sphere and concentrate near a manifold of intrinsic dimension five to six. This geometry yields two insights. First, standard k-means centroids fall inside the sphere, causing systematic underestimation of MaxSim scores. Normalizing them to the surface is a free correction worth up to +0.093 nDCG@5 over raw centroids. Second, because the page manifold has few degrees of freedom, the full set of vectors can be regenerated from only a few. To this end, we introduce Generative Late-Interaction Embeddings (GLIE): k << N vectors per page learned from the normalized centroids to serve as both a lightweight index and a basis for regenerating the page's full embedding set. At query time, search runs exclusively on these k vectors, and a decoder expands only the top candidates back to all N vectors for exact rescoring. At four vectors per page on ViDoRe v1, GLIE retains nearly 80% of the uncompressed system's nDCG@5, against 70% for the best prior post-hoc method. These results use a 415K-parameter network fitted in under three GPU-minutes on just a thousand training pages. At a matched training budget, fine-tuning the encoder does not reach even the training-free stage of GLIE, and the full system beats it at every budget. These patterns hold across a second encoder and ViDoRe v2. By reconstructing evidence on demand rather than sampling it, GLIE opens a new axis for storage-efficient retrieval, with the decoder as its main design surface.