Papers by Beining Huang
Jailbreak LLMs through Internal Stance Manipulation (2025.emnlp-main)
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| Challenge: | Existing approaches to exploit LLMs' inherent safety mechanism, including GCG and AutoDAN, are ineffective for certain malicious requests. |
| Approach: | They propose a method that generates jailbreak prompts to suppress a refusal stance and induce affirmative responses by modifying adversarial prompts. |
| Outcome: | The proposed method outperforms the best baseline approach in Llama-2-7b-chat and achieves a 92.2% success rate across all models. |
Low-Entropy Watermark Detection via Bayes’ Rule Derived Detector (2025.findings-acl)
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| Challenge: | Existing methods for text watermarking ignore strong evidences embedded in low-entropy tokens, causing statistical measures to falsely indicate the absence of a watermark. |
| Approach: | They propose a Bayes' Rule derived watermark Detector which exploits watermark information from every token by leveraging the posterior probability of watermark’s presence. |
| Outcome: | The proposed method achieves 50% and 70% relative improvements over baselines in code generation and math problem-solving tasks. |
From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning (2026.findings-acl)
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Beining Wu, Fuyou Mao, Jiong Lin, Cheng Yang, Jiaxuan Lu, Yifu Guo, Siyu Zhang, Yifan Wu, Ying Huang, Fu Li
| Challenge: | Generative engines (GEs) are replacing ranked links with citation-grounded answers . current methods are unable to accumulate or transfer effective strategies across tasks and engines . |
| Approach: | They propose a multi-agent framework where planning, editing, and fidelity-aware evaluation serve as the execution layer. |
| Outcome: | The proposed framework outperforms heuristic baselines in visibility and citation fidelity on three mainstream engines. |