MiG-NJU/EvoEmbedding-0.8B

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

🔗 GitHub Repository | 🌐 Project Page | 📚 Training Dataset | 📄 Paper

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.

🚀 Quick Start

Installation

First, clone the repository and install the dependencies:

git clone https://github.com/MiG-NJU/EvoEmbedding.git
cd EvoEmbedding
conda create -n evoemb python=3.10 -y
conda activate evoemb
pip install -r requirements-evoembedding-lite.txt

Usage

As an Embedding Model

from model.client import EvoEmbeddingClient

client = EvoEmbeddingClient()

messages = [
    {"role": "user", "content": "I visited Paris in April."},
    {"role": "assistant", "content": "Noted."},
    {"role": "user", "content": "I bought a new laptop yesterday."},
    {"role": "assistant", "content": "Got it."},
    {"role": "user", "content": "Where did I travel in spring?"},
]

embeddings = client.encode_messages(messages)

The messages input preserves the original dialogue order. encode_messages returns normalized embeddings for the history turns and the final query.

As a Reranker

candidates = [
    "I visited Paris in April.",
    "I bought a new laptop yesterday.",
    "The meeting was moved to Friday.",
]
query = "Where did I travel in spring?"

ranked_candidates, ranked_indices = client.rerank(
    query,
    candidates,
    top_k=1,
    return_indices=True,
)

The reranker takes a direct list of candidate strings and returns them in relevance order.

📦 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}
}