Papers by Rongyi Zhu
AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) generate coherent reasoning paths before conclusions, but they introduce new vulnerabilities. |
| Approach: | They propose a framework that leverages a weaker but less-aligned model to simulate execution reasoning for initial hijacking attempts and iteratively refines attacks by exploiting reasoning patterns leaked through the target LRM’s refusals. |
| Outcome: | The proposed framework achieves 100% success rate within one or few turns, neutralizing reasoning-based defenses even when evaluated by robustly aligned external models. |
Scaling Laws for Code: A More Data-Hungry Regime (2026.acl-long)
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Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, Rongyi Zhang, Houyi Li, Siming Huang, YuanTao Fan, Wanxiang Che
| Challenge: | Code Large Language Models (LLMs) are revolutionizing software engineering, but scaling laws are primarily analyzed on Natural Language (NL). |
| Approach: | They fit Chinchilla law and Farsser law to test scaling laws for code . they find code is more data-hungry and requires higher data-to-parameter ratio . |
| Outcome: | The proposed scaling laws show that the more expressive Farsser law offers greater accuracy and scales with model size. |