AI 记忆会进化：同一问题，不同答案
现在的 AI 搜索是「死」的：同一段话，不管上下文怎么变，它都给出同一个向量。这篇让 AI 的「记忆」像人一样会更新：它一边读长文本一边维护一个隐状态，每次生成向量时都结合当前记忆和内容。结果同一个问题，在故事开头和结尾能搜到不同的答案——比如你问「主角的动机」，它会根据剧情进展给出不同结果。这靠一个 18 万条的训练集和防止记忆崩塌的队列实现。它不是你明天能用上的，但如果你在做需要持续跟踪状态的 AI 助手（比如聊天机器人、个人知识库），这个方向值得关注。
📄 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.