AI 中文总结
研究针对企业文档分类难题，提出SIFT动态分类器服务，通过低成本管道、特定机制及判断反馈实现自我改进，解决标注和安全问题，介绍其架构、机制及部署示例，探讨边际标注成本趋零的经济性。
AI 中文摘要
文档分类在实验室中已解决，但在企业中仍未解决。阻碍因素并非模型架构，而是模型训练前的标注项目以及企业对模型自我再训练的担忧。我们提出了SIFT（自我改进冻结门训练），一种动态分类器服务，它解决了这两个问题。SIFT通过一个低成本的CPU管道进行分类，使用SPLADE稀疏编码器和LightGBM头，仅将低置信度的少数页面提交给LLM判断。判断结果被写回标注语料库，使昂贵的模型不断教导便宜的模型。引入新文档家族只需声明性捆绑、标签空间、锚定短语和判断词汇表，而非标注项目。更难的问题是安全性，SIFT通过两部分促进门解决，即关键标签F1回归检查和模型从未训练过的冻结黄金回归集，两者均可否决升级。这使得“无需人工每月再训练”从鲁莽变为常规。我们描述了架构、自我馈送语料库循环、冻结门促进机制和一个多域部署示例，并讨论了边际标注成本趋于零的分类器的经济性。
英文摘要
Document classification is a solved problem in the laboratory and an unsolved one in the enterprise. The blocker is rarely model architecture; it is the labeling project that must precede a model and the institutional fear of letting a model retrain itself once one exists. We present SIFT (Self-Improving, Frozen-gate Training), a dynamic classifier service, which attacks both. SIFT serves classification from a deliberately cheap, CPU-bound pipeline, a SPLADE sparse encoder feeding a LightGBM head, and escalates only the low-confidence minority of pages to an LLM judge. The judge's verdicts are written back into a labeled corpus, so the expensive model continuously teaches the cheap one: the escalation rate falls, the corpus grows from production traffic rather than from an up-front annotation effort, and accuracy compounds with use. Onboarding a new document family requires only a declarative bundle, label space, anchor phrases, and a judge glossary, not a labeling project. The harder problem is safety: an autonomously retraining classifier can silently regress. SIFT resolves this with a two-part promote gate, a critical-label F1 regression check plus a frozen golden regression set the model is never trained on, either of which vetoes promotion. This turns "retrain monthly without a human" from reckless into routine. We describe the architecture, the self-feeding corpus loop, the frozen-gate promotion mechanism, and an illustrative multi-domain deployment, and we discuss the economics of a classifier whose marginal labeling cost trends toward zero.
Comments12 pages, 2 figures
