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Luoberta
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Model Details
Model Provider
Luoberta
Model Tree
Qwen/Qwen3-32B
Input Modalities
Output Modalities
Supported Functionality
Abacus-cve is based on Qwen3-32B and fine-tuned using 4,078 distilled agent traces from ~900 CVE reproduction tasks. The traces were generated using Claude Opus 4.5 with a Mini SWE-Agent harness through the CVE-Factory pipeline.
Fine-tuning yields dramatic improvements across security benchmarks:
| Model | LiveCVEBench | PatchEval | Terminal-Bench | Avg |
|---|---|---|---|---|
| Qwen3-32B (base) | 5.29 | 5.66 | 12.50 | 7.82 |
| Abacus-cve (Ours) | 35.79 | 23.58 | 28.75 | 29.37 |
| Qwen3-Coder-30B | 10.58 | 9.91 | 13.75 | 11.41 |
| Qwen3-Coder-480B | 19.58 | 19.34 | 36.25 | 25.06 |
| MiniMax-M2 | 24.87 | 19.34 | 37.50 | 27.24 |
| Claude Sonnet 4 | 20.11 | 22.64 | 33.75 | 25.50 |
| Claude Sonnet 4.5 | 34.39 | 28.77 | 45.00 | 36.05 |
| Claude Opus 4.5 | 41.27 | 32.08 | 48.75 | 40.70 |
Key findings:
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel = AutoModelForCausalLM.from_pretrained("Luoberta/Abacus-cve")tokenizer = AutoTokenizer.from_pretrained("Luoberta/Abacus-cve")
bibtex
@misc{luo2026cvefactory,title={CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability},author={Xianzhen Luo and Jingyuan Zhang and Shiqi Zhou and Rain Huang and Chuan Xiao and Qingfu Zhu and Zhiyuan Ma and Xing Yue and Yang Yue and Wencong Zeng and Wanxiang Che},year={2026},eprint={2602.03012},archivePrefix={arXiv},primaryClass={cs.CR},url={https://arxiv.org/abs/2602.03012}}