EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory
🔗 GitHub Repository | 📚 Training Dataset | 📑 Paper (https://arxiv.org/abs/2606.21649)
EvoEmbedding is a novel embedding model designed for long-context and dynamic retrieval scenarios. Unlike static embedding models that chunk text in isolation, EvoEmbedding maintains a continuously updated Latent Memory Queue. This allows it to capture temporal dynamics and generate context-aware, evolvable embeddings for precise retrieval in agentic workflows and long-conversations.
📦 Model Family
We provide EvoEmbedding in three sizes based on the Qwen architecture:
| Model | Parameters | Base Model | Hugging Face Link |
|---|---|---|---|
| EvoEmbedding-0.8B | 0.8B | Qwen3.5-0.8B | MiG-NJU/EvoEmbedding-0.8B |
| EvoEmbedding-2B | 2B | Qwen3.5-2B | MiG-NJU/EvoEmbedding-2B |
| EvoEmbedding-4B | 4B | Qwen3-4B | MiG-NJU/EvoEmbedding-4B |
📚 Citation
If you find this model or our methodology useful, please cite our paper:
@article{nie2026evoembedding,
title={EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory},
author={Nie, Chang and Fu, Chaoyou and Feng, Junlan and Shan, Caifeng},
journal={arXiv preprint arXiv:2606.21649},
year={2026}
}