🤗 Model  •  💻 GitHub  •  📜 Technical Report  •  ⚖️ License: Apache-2.0 + SAM License

神珍 · Monkey King Bang (MKB)

神珍 is a unified scientific multimodal foundation model that supports scientific understanding and generation across Earth science, proteins, RNA, DNA, and small molecules. Native scientific encoders/decoders wrap a shared Qwen3-VL-8B-Instruct backbone (about 11B parameters in total), so heterogeneous scientific data (sequences, molecular graphs, gridded physical fields, medical images) are reasoned about and generated in one representation space — natural language in and out, no per-task fine-tuning.

📜 Read the technical report for architecture, training, and full benchmarks.

Key features

• Unified understanding and generation across 7 modalities through one natural-language interface.
• Seven modalities, one 8B backbone (protein / RNA / DNA / molecule / weather / medical-image / text) via a modality router.
• Native scientific encoders/decoders (ESM-2, RNA/DNA ConvFormers, molecular graph encoder, Swin-ViT weather tower, SAM-based image path) preserve domain structure a generic tokenizer would destroy.

Capabilities

Understanding = classification / regression / scientific QA. Generation: RNA sequence design · Molecule text → SMILES · Weather 10-day global ERA5 0.25° forecast · Medical-image text-prompted segmentation (SAM 3-based; Meta SAM License).

Benchmarks

神珍 (8B backbone, ~11B total) vs Biology-Instructions (Llama-3.1-8B, text-token, no scientific encoders) and Intern-S1-Pro (~1T MoE scientific model). Bold = best; underline = second-best.

Biological sequence understanding

Aggregate over 20 biological-understanding benchmarks: 神珍 matches or beats the ~1T Intern-S1-Pro on 10/20 and the same-scale 8B text-token baseline on 16/20.

Molecule understanding (SMolInstruct)

Earth-science forecasting — vs ECMWF HRES (day-10, global ERA5 0.25°)

神珍 tracks or beats the operational physics-based HRES system, with the advantage growing at longer lead times.

Medical-image segmentation

Mean Dice (%) on the BiomedParse test splits, 102,855 image–prompt pairs across nine imaging modalities, versus six modality-native segmentation specialists.

Best overall Dice (All), and best on CT, MRI, pathology, dermoscopy, and endoscopy; on X-ray, Fundus, and Ultrasound the gap to BiomedParse is ≤ 0.5 Dice, and on the smallest split (OCT) it is 1.3.

Usage

Runs via the accompanying code repository (custom multimodal architecture).

git clone https://github.com/Shanghai-Academy-of-AI-For-Science/MKB && cd MKB
pip install -r requirements.txt          # Python 3.10; transformers==5.0.0
hf download sais-org/MKB --local-dir ./model

export PYTHONPATH=$PWD/code
python code/inference.py --model_path model --greedy --max_new_tokens 64 \
  --rna "GGATGCGATCATGTCTGCACTAACACACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGGGAGTAGTACTAGGATGGGCGACCCCTTAGGAAGTACTCGTGTTGCATCCC" \
  --system "You are a non-coding RNA family classifier. Output only the family name, no other text." \
  --prompt $'<rna>\nWhich family does this non-coding RNA sequence belong to?'

All weights are contained in model.safetensors: the scientific encoders/decoders (ESM-2, the Suiren molecular graph encoder, the RNA/DNA ConvFormers, the Swin-ViT weather tower) and the fine-tuned SAM 3 branch used for medical-image segmentation.

Each task has a specific --system prompt that fixes the output format; see run_examples.sh in the repository for per-task examples, weather, and segmentation.

License

Composite license. 神珍's own components — the code, and all weights except the SAM 3 branch — are Apache-2.0, built on Qwen3-VL (Apache-2.0) and including merged ESM-2 (MIT) and Polaris/Suiren-derived encoders.

The medical-image segmentation branch embeds SAM 3 weights, which are governed by Meta's SAM License (SAM_LICENSE.txt, shipped alongside these weights). SAM 3 use is subject to that license, including its acceptable-use restrictions (no military / weapons / illegal uses; Trade-Control compliance). See THIRD_PARTY_LICENSES.md / NOTICE for the full third-party breakdown.

Citation

@misc{chen2026monkeykingbangunified,
  title         = {Monkey King Bang: A Unified Scientific Multimodal Foundation Model},
  author        = {Hesen Chen and Xinyu Su and Xiaomeng Yang and Yuetan Lin and Zixiong Yang and Junyi An and Fenglei Cao and Yifeng Jiao and Yunqi Zhang and Yuan Cheng and Zhiyu Tan and Hao Li and Libo Wu and Yuan Qi},
  year          = {2026},
  eprint        = {2607.20557},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2607.20557}
}