KURE-v2

KURE-v2 is a Korean-English bilingual late-interaction (multi-vector) retrieval model built on skt/A.X-Encoder-base. It encodes every token into a 128-dimensional vector and scores query–document pairs with MaxSim.

At 154M parameters it averages nDCG@10 0.8160 across the nine MTEB(kor, v2) retrieval tasks — the strongest model on that suite, above every single-vector model measured, including one 175× its size.

It is trained in two stages: unsupervised contrastive pretraining on 20.7M unlabeled pairs (KURE-v2-unsupervised), then supervised fine-tuning with contrastive loss and KL distillation from a reranker.

Key Characteristics

• Late interaction: one 128-d vector per token, scored with MaxSim.
• Long documents: up to 8,192 tokens.
• Compact: 154M parameters.
• No instruction prefixes: queries and documents need no task instruction. Query expansion to 64 tokens is handled by the model.

Usage

PyLate Usage

pip install -U pylate

Indexing documents

from pylate import indexes, models, retrieve

model = models.ColBERT(model_name_or_path="nlpai-lab/KURE-v2")

index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
    override=True,
)

documents_ids = ["1", "2", "3"]
documents = [
    "세종대왕은 1443년에 훈민정음을 창제하고 1446년에 이를 반포하였다.",
    "김치는 배추나 무를 소금에 절인 뒤 고춧가루와 젓갈을 넣어 발효시킨 음식이다.",
    "한라산은 해발 1,947m로 남한에서 가장 높은 산이며 제주도 중앙에 자리한다.",
]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,
    show_progress_bar=True,
)

index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

To reuse an existing index, instantiate it without override:

index = indexes.PLAID(index_folder="pylate-index", index_name="index")

Retrieving top-k documents

retriever = retrieve.ColBERT(index=index)

queries_embeddings = model.encode(
    ["훈민정음은 언제 만들어졌나요?"],
    batch_size=32,
    is_query=True,
    show_progress_bar=True,
)

scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,
)

Reranking

To rerank a first-stage candidate list without building an index:

from pylate import models, rank

model = models.ColBERT(model_name_or_path="nlpai-lab/KURE-v2")

queries = [
    "전기차 폐배터리는 어떻게 재활용하나요?",
    "겨울에 한라산을 오를 때 필요한 장비는?",
]
documents = [
    [
        "폐배터리에서 리튬과 코발트를 회수하는 습식 제련 공정이 상용화되고 있다.",
        "급속 충전기는 30분 내외로 배터리를 80%까지 충전할 수 있다.",
    ],
    [
        "겨울 한라산 산행에는 아이젠과 방한 장갑이 필수이며 입산 시간이 제한된다.",
        "제주 올레길은 해안을 따라 이어지는 27개 코스로 구성되어 있다.",
        "적설기에는 등산화에 스패츠를 착용해 눈이 들어가는 것을 막는 것이 좋다.",
    ],
]
documents_ids = [[1, 2], [1, 3, 2]]

queries_embeddings = model.encode(queries, is_query=True)
documents_embeddings = model.encode(documents, is_query=False)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Sentence-Transformers Usage

pip install -U sentence-transformers

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("nlpai-lab/KURE-v2")

query = "훈민정음은 언제 만들어졌나요?"
documents = [
    "세종대왕은 1443년에 훈민정음을 창제하고 1446년에 이를 반포하였다.",
    "김치는 배추나 무를 소금에 절인 뒤 고춧가루와 젓갈을 넣어 발효시킨 음식이다.",
    "한라산은 해발 1,947m로 남한에서 가장 높은 산이며 제주도 중앙에 자리한다.",
]

query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings[0].shape)
# (64, 128) (29, 128)

# MaxSim late-interaction scoring (higher is more relevant)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)

Evaluation

KURE-v2 is evaluated on the nine MTEB(kor, v2) Retrieval tasks. We report nDCG@10. The results are also shown in the official MTEB Leaderboard

Model	Params	Avg	AutoRAG	PubHealthQA	Ko-StrategyQA	LawIRKo	SQuADKorV1	Belebele (ko-ko)	MrTidy	MLDR	MIRACL
Our Models
nlpai-lab/KURE-v2	154M	0.8160	0.9718	0.8229	0.8070	0.7550	0.9846	0.9660	0.5974	0.7159	0.7237
nlpai-lab/KURE-v2-unsupervised	154M	0.7283	0.8822	0.8240	0.7818	0.7680	0.9427	0.9525	0.3496	0.6130	0.4411
Late-Interaction (Multi-Vector) Models
yjoonjang/colbert-ko-en-v2	149M	0.8063	0.9686	0.8222	0.7940	0.7181	0.9846	0.9687	0.5783	0.6992	0.7230
lightonai/mLateOn	307M	0.7906	0.9392	0.8061	0.7905	0.6431	0.9803	0.9608	0.5817	0.7005	0.7135
perplexity-ai/pplx-embed-v1-late-0.6b	596M	0.7381	0.8557	0.8089	0.7973	0.7285	0.9696	0.9548	0.5400	0.2816	0.7064
dragonkue/colbert-ko-0.1b	149M	0.6776	0.9700	0.7482	0.7364	0.4475	0.9794	0.9644	0.3966	0.2872	0.5685
yjoonjang/colbert-ko-v1	149M	0.6282	0.9557	0.6783	0.6560	0.4823	0.9594	0.9154	0.3279	0.2214	0.4575
Dense (Single-Vector) Models
sionic-ai/comsat-embed-ko-8b-preview	7.6B	0.7927	0.8518	0.8871	0.8394	0.8164	0.9168	0.9853	0.6253	0.5157	0.6964
Qwen/Qwen3-Embedding-8B	7.6B	0.7826	0.8276	0.8721	0.8363	0.8171	0.9063	0.9824	0.6187	0.5046	0.6783
Qwen/Qwen3-Embedding-4B	4.0B	0.7737	0.8431	0.8693	0.8270	0.7769	0.9044	0.9522	0.6076	0.5022	0.6803
microsoft/harrier-oss-v1-27b	27.0B	0.7667	0.8176	0.8971	0.8361	0.8737	0.9204	0.9546	0.5306	0.4046	0.6653
dragonkue/snowflake-arctic-embed-l-v2.0-ko	568M	0.7653	0.9093	0.8337	0.8050	0.7735	0.9447	0.9518	0.5712	0.4304	0.6685
codefuse-ai/F2LLM-v2-8B	7.6B	0.7638	0.7678	0.9380	0.8371	0.8405	0.8874	0.9513	0.6162	0.4047	0.6313
telepix/PIXIE-Rune-v1.5	568M	0.7618	0.8927	0.8426	0.8064	0.7705	0.9457	0.9617	0.5492	0.4482	0.6393
nlpai-lab/KURE-v1	568M	0.7616	0.8708	0.8193	0.7999	0.7426	0.9357	0.9502	0.5909	0.4637	0.6816
dragonkue/BGE-m3-ko	568M	0.7547	0.8738	0.8155	0.7959	0.7322	0.9414	0.9503	0.6099	0.3899	0.6833
BAAI/bge-m3	568M	0.7509	0.8301	0.8041	0.7941	0.7174	0.9038	0.9316	0.6471	0.4287	0.7015
nlpai-lab/KoE5	560M	0.7337	0.8434	0.8351	0.8001	0.7756	0.8980	0.9425	0.5841	0.3015	0.6235

Late-interaction rows were measured with mteb 2.18.16 and PLAID retrieval. Single-vector rows are taken from the official MTEB results repository, except for Belebele, where only the Korean-query / Korean-corpus subset is used. The original version also includes cross-lingual subsets (Korean query – English corpus, English query – Korean corpus).

Serving

KURE-v2 is a late-interaction model: each document is stored as a set of token vectors, so the practical questions for deployment are index size and search cost. We benchmarked KURE-v2 across ANN backends and compression schemes on the 9 Korean MTEB retrieval tasks, against five single-vector baselines served with faiss HNSW. All numbers are end-to-end: batch-1 query encoding + index search, measured serially on one A100 80GB.

Two things the figures show:

• Hierarchical token pooling (x2) halves the index for a 0.04 nDCG drop. Asymmetric binary quantization (1-bit document tokens, bf16 queries) shrinks it 9.4x for 1.05. Stacking the two (pooling x3 + binary), the entire 9-corpus index fits in 1.7 GB, smaller than every single-vector HNSW index (13.1-50.0 GB), while still outscoring the best single-vector model (79.57 vs 79.07).
• A live query arrives as text: 4B-8B single-vector models spend 38-40 ms encoding it, capping them at ~25 QPS no matter how fast HNSW is. KURE-v2 encodes in 13.8 ms (154M params), so every configuration except MUVERA serves 43-55 QPS, roughly 2x the 8B single-vector models, at higher quality.

Large corpora: tail latency

On the largest corpus (MIRACL, ~1.5M documents) an exhaustive 1-bit scan costs O(corpus): p95 climbs to 156 ms, and pooling the tokens 3x only brings it to 74 ms. Generating candidates with faiss BinaryIVF (Hamming search over the same 1-bit index) and re-scoring them with exact asymmetric MaxSim cuts p95 to 38 ms on the same 2.2 GB index, lower tail latency than the 4B-8B single-vector baselines (43 ms) at higher nDCG. For large collections, use a candidate-generating index (PLAID or BinaryIVF), not an exhaustive scan.

Measurement details

• Hardware: 1x NVIDIA A100 80GB, 2x AMD EPYC 7513 (64 cores), 1.2 TB RAM.
• Software: faiss-cpu 1.15.0, fast-plaid 1.6.0, sentence-transformers 6.0.0, PyTorch 2.8.0.
• Protocol: batch-1, serial. Index-search latency: 10 warmup queries, then every query of the task measured once (QPS = 1/mean). Query-encoding latency: 5 warmup, 50 measured. End-to-end = encoding + search.
• Precision: encoding in bf16; each index stores its own format (HNSW fp32, PLAID 4-bit residuals, binary 1-bit).
• Index size: the full serialized index on disk (vectors, graph, codebooks; external doc-id mapping excluded).
• Tasks: the 9 Korean MTEB retrieval tasks; MLDR is the mean of its dev/test splits; nDCG@10 x100.
• HNSW: IndexHNSWFlat (inner product on L2-normalized embeddings), M=32, efConstruction=200, efSearch=64.
• PLAID: nbits=4, all other settings fast-plaid defaults (kmeans_niters=4, n_ivf_probe=8, n_full_scores=4096). nbits=2/1 give 27.0/17.0 GB at 81.25/81.09 nDCG.
• MUVERA: num_repetitions=10, num_simhash_projections=6, final_projection_dimension=8192, exact-MaxSim rerank of the top 1,000.
• BinaryIVF: nlist=floor(sqrt(total tokens)) capped at 65,536, nprobe=32, top-128 Hamming tokens per query token, exact asymmetric-MaxSim rerank of the top 1,000 documents.
• Token pooling: hierarchical (Ward linkage), pool_factor 2-3, documents only.

Citation

@misc{kure-v2,
  title  = {KURE-v2: a Korean-English bilingual late-interaction retriever},
  author = {Jang, Youngjoon and Son, Junyoung and Lee, Taemin and Hong, Seongtae and Lim, Heuiseok},
  year   = {2026},
  url    = {https://huggingface.co/nlpai-lab/KURE-v2},
}

@inproceedings{jang2025kure,
  title={KURE: Embedding Model for Korean-Specific Retrieval},
  author={Jang, Youngjoon and Son, Junyoung and Lee, Taemin and Hong, Seongtae and Park, JeongBae and Lim, Heuiseok},
  booktitle={Annual Conference on Human and Language Technology},
  pages={129--134},
  year={2025},
  organization={Human and Language Technology}
}

@inproceedings{santhanam-etal-2022-colbertv2,
  title     = {ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction},
  author    = {Santhanam, Keshav and Khattab, Omar and Saad-Falcon, Jon and Potts, Christopher and Zaharia, Matei},
  booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year      = {2022},
  pages     = {3715--3734},
}

@misc{PyLate,
  title  = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
  author = {Chaffin, Antoine and Sourty, Raphaël},
  year   = {2024},
  url    = {https://github.com/lightonai/pylate},
}