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

To use EvoEmbedding, please clone the GitHub Repository and install the environment.

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:

📚 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}
}