AI 记忆会进化：检索不再只看关键词
现在的 AI 检索像翻字典：每个词条固定不变，不管上下文。这篇让 AI 的「记忆」像人一样，读一段更新一次。它给模型加了一个持续更新的「潜记忆」，每处理一段文本就刷新一次，所以同一个问题，在不同语境下能召回不同的答案。比如你问「他后来怎么样了」，模型会根据之前读到的故事进展，自动理解「他」是谁、该找哪段。在长文本检索上，它甚至超过了 8B、12B 的大模型。更意外的是，一个简单的 RAG 系统配上它，效果就超过了专门的智能体记忆系统。它不是你明天能直接用的工具，但指向了一个方向：AI 的「记忆」不该是静态数据库，而该是不断演化的状态。
📄 原文摘要(英文)
Existing embedding models are inherently static: they encode text segments in isolation, ignoring their surrounding context and temporal order. This paper introduces EvoEmbedding, a novel embedding model that generates evolvable representations for retrieval. It is tailored for long-context scenarios, where information is dynamic, sequential, and requires continuous state tracking. Our design is simple: EvoEmbedding maintains a continuously updated latent memory as it sequentially processes inputs, and uses it alongside the raw content to jointly generate evolvable embeddings. Consequently, for the same query, our model adapts its representation to retrieve distinct targets based on the evolving context, going beyond static semantic search. To equip the model with this capability, we construct EvoTrain-180K, a diverse dataset for the joint optimization of latent memory and retrieval. Furthermore, we introduce a memory queue to prevent representation collapse during recurrent encoding, alongside segment-batching techniques that tackle significant length variance and accelerate training by 3.8times. Extensive experiments show that our model not only outperforms larger-scale specialists (e.g., Qwen3-Embedding-8B and KaLM-Embedding-Gemma3-12B) across a range of long-context retrieval benchmarks, but also generalizes well to downstream tasks (e.g., personalization) with contexts 10times longer than its training window. Notably, EvoEmbedding seamlessly integrates into agentic workflows to boost performance. For instance, a naive RAG pipeline equipped with our model surpasses dedicated agentic memory systems. Project Page: https://clare-nie.github.io/EvoEmbedding.