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README.md

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 ---
 license: cc-by-4.0
+pipeline_tag: feature-extraction
+library_name: transformers
+tags:
+- embeddings
+- multimodal
+- retrieval
+- compositional-reasoning
+- vision
+- reranker-distillation
 ---
+
+<h1 align="center">Core-Embed: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation</h1>
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2609.04083"><img src="https://img.shields.io/badge/arXiv-CORE-b31b1b.svg" alt="arXiv"></a>
+  <a href="https://github.com/"><img src="https://img.shields.io/badge/GitHub-CODE-black.svg" alt="GitHub"></a>
+  <img src="https://img.shields.io/badge/License-CC--BY--4.0-blue.svg" alt="License: CC-BY-4.0">
+</p>
+
+Core-Embed is an MLLM-based multimodal embedding model that resolves fine-grained attribute-object bindings by distilling a reranker's compositional judgments into the embedding space.
+
+## Model Family
+
+| Model | Backbone | Parameters | Outputs | Modalities |
+|---|---|---:|---|---|
+| core-emb-2b | VL-Emb (Qwen3-VL) | 2B | Dense embedding | Text, image |
+| core-emb-8b | VL-Emb (Qwen3-VL) | 8B | Dense embedding | Text, image |
+| core-reranker-2b | Qwen3-VL-Reranker | 2B | Relevance score | Text, image |
+| core-reranker-8b | Qwen3-VL-Reranker | 8B | Relevance score | Text, image |
+
+## Highlights
+
+- **Compositional retrieval**: distinguishes scenes with the same concepts but different attribute-object bindings (e.g., "a white plate and a black chair" vs. "a black plate and a white chair").
+- **Rank-KL distillation**: trained to reproduce the reranker teacher's fine-grained ranking over a five-level compositional matching spectrum (full match, partial presence, attribute error, object error, full mismatch), instead of collapsing all negatives into one class as InfoNCE does.
+- **General retrieval preserved**: continual training from a strong MLLM embedding backbone, so COCO and Flickr30k retrieval quality is retained while compositional accuracy improves.
+
+## Training
+
+Candidate lists are synthesized from LAION-400M seed images: Qwen3-VL-32B extracts structured scene representations and generates queries plus five captions spanning the matching levels, Z-Image-Turbo generates the candidate images, and MLLM-based verification filters low-quality tuples. The student (VL-Emb) is then trained with the Rank-KL objective — a KL divergence between temperature-softened teacher reranker scores and student cosine similarities over each candidate list. Core-Reranker models are fine-tuned from Qwen3-VL-Reranker on the same synthesized data.
+
+## Results
+
+On compositional reasoning benchmarks (COLA, SugarCrepe++, NegBench):
+
+- **Core-Reranker-8B**: 82.7% total average, +10.7 points over Jina-Reranker, while recovering negation sensitivity that standard reranker fine-tuning erodes.
+- **Core-Embed-8B**: best total average (0.666) among all evaluated embedding models, +5.7 points over its VL-Emb-8B backbone.
+- Gains transfer to the MCMR multi-condition retrieval benchmark (R@1 0.375 → 0.412) with COCO/Flickr30k performance fully preserved.
+
+See the paper for the full evaluation across 12 embedding baselines and 5 reranker baselines.
+
+## Usage
+
+Requires a recent `transformers` build with Qwen3-VL support:
+
+```bash
+pip install "transformers>=4.57.0" torch qwen-vl-utils pillow
+```
+
+The model is loaded through the wrapper classes provided in the [GitHub repository](https://github.com/):
+
+```python
+import torch
+from models.qwen3_vl.qwen3_vl_wrapper import Qwen3VLForEmbedding, Qwen3VLForEmbeddingProcessor
+
+model_path = "core-emb-8b"
+model = Qwen3VLForEmbedding.from_pretrained(
+    model_path, device_map="cuda", torch_dtype=torch.bfloat16
+)
+processor = Qwen3VLForEmbeddingProcessor.from_pretrained(
+    model_path,
+    instruction_standalone=True,
+    max_length=3024,
+    min_pixels=32*32*4,
+    max_pixels=32*32*1280,
+    total_pixels=32*32*4500,
+    num_frames=48,
+)
+
+query_inst = "Find me an everyday image that matches the given caption: "
+psg_inst = "Represent the given image."
+
+queries = ["a white plate and a black chair"]
+inputs = processor(
+    texts=queries, images=[None], videos=[None],
+    instruction=[query_inst], is_query=True,
+)
+with torch.inference_mode():
+    query_emb = model(**inputs.to(model.device)).last_hidden_state[:, -1]
+
+images = ["path/to/image.jpg"]
+inputs = processor(
+    texts=[None], images=images, videos=[None],
+    instruction=[psg_inst], is_query=False,
+)
+with torch.inference_mode():
+    img_emb = model(**inputs.to(model.device)).last_hidden_state[:, -1]
+
+print(query_emb @ img_emb.T)
+```
+
+For FlashAttention acceleration, pass `attn_implementation="flash_attention_2"` when loading the model.
+
+For evaluation on COLA, SugarCrepe++, NegBench, COCO, Flickr30k, and MCMR, use the evaluation code in the [GitHub repository](https://github.com/).
+
+## Citation
+
+If you use Core-Embed, please cite the paper:
+
+```bibtex
+@misc{song2026core,
+      title={CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation}, 
+      author={Tingyu Song and Mingxin Li and Yanzhao Zhang and Dingkun Long and Chu Liu and Pengjun Xie and Yilun Zhao and Shu Wu},
+      year={2026},
+      eprint={2609.04083},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2609.04083}, 
+}
+```
+
+## Acknowledgements
+We thank the authors of [COLA](https://arxiv.org/abs/2305.03689), [SugarCrepe++](https://arxiv.org/abs/2406.11171), and [NegBench](https://github.com/m1k2zoo/negbench) for their benchmarks, and the [Qwen3-VL-Embedding](https://github.com/QwenLM/Qwen3-VL-Embedding) project for the evaluation framework.