ClareNie/EvoEmbedding-2B

EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory

🔗 [GitHub Repository](https://github.com/MiG-NJU/EvoEmbedding) | 📚 [Training Dataset](https://huggingface.co/datasets/MiG-NJU/EvoTrain-180K) | 📑 [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:

📚 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}
}