---
language:
- en
- zh
pipeline_tag: feature-extraction
tags:
- embedding
- retrieval
- long-context
- rag
- qwen
license: apache-2.0
---

# 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:

| Model | Parameters | Base Model | Hugging Face Link |
| :--- | :---: | :---: | :--- |
| **EvoEmbedding-0.8B** | 0.8B | Qwen3.5-0.8B | [MiG-NJU/EvoEmbedding-0.8B](https://huggingface.co/MiG-NJU/EvoEmbedding-0.8B) |
| **EvoEmbedding-2B** | 2B | Qwen3.5-2B | [MiG-NJU/EvoEmbedding-2B](https://huggingface.co/MiG-NJU/EvoEmbedding-2B) |
| **EvoEmbedding-4B** | 4B | Qwen3-4B | [MiG-NJU/EvoEmbedding-4B](https://huggingface.co/MiG-NJU/EvoEmbedding-4B) |

## 📚 Citation

If you find this model or our methodology useful, please cite our paper:

```bibtex
@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}
}
```