用LLM嵌入打通程序分析与优化，让代码理解更智能，优化更精准。
arXiv:2608.07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models…
不再丢弃大模型输出语义，用自监督生成式嵌入让LLM直接产出向量，重新定义嵌入学习。
arXiv:2603.10913v3 Announce Type: replace Abstract: Fine-tuning LLM-based text embedders via contrastive learning maps inputs and outputs into a new r…
不用黑盒，距离解释器让嵌入空间每个维度都有了人话级可解释性
arXiv:2505.15516v3 Announce Type: replace-cross Abstract: While eXplainable AI (XAI) has advanced significantly, few methods address interpretability …
大规模推荐系统如何更懂用户？TokenMinds用预训练用户令牌和嵌入，解锁更精准的用户理解新思路。
arXiv:2606.25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer fr…
自回归建模加持，让密集检索嵌入更智能，刷新稠密向量生成思路
arXiv:2606.24667v1 Announce Type: new Abstract: Dense retrieval embedding models are a fundamental component of modern retrieval-based AI systems. Mos…