Papers by Peixuan Han
SafeSwitch: Steering Unsafe LLM Behavior via Internal Activation Signals (2025.findings-emnlp)
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| Challenge: | Existing safety mechanisms for large language models (LLMs) are inadequate to fully leverage their internal cognitive processes. |
| Approach: | They propose a framework that regulates unsafe outputs by utilizing the prober-based internal state monitor that actively detects harmful intentions. |
| Outcome: | The proposed framework reduces harmful outputs by approximately 80% while maintaining strong utility. |
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)
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Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji
| Challenge: | Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments. |
| Approach: | They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals. |
| Outcome: | The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels. |
DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal (2026.findings-acl)
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| Challenge: | Existing approaches to support academic rebuttal rely on off-the-shelf LLMs or simple pipelines that struggle with long-context understanding. |
| Approach: | They propose an agentic framework for automatic academic rebuttal generation that operates through four steps: Decompose reviews into atomic concerns, Retrieve relevant evidence from the paper, Plan refortations, and Generate responses accordingly. |
| Outcome: | The proposed framework outperforms existing rebuttal pipelines and achieves 98% accuracy beyond the average human level using only an 8B model. |
DecisionFlow: Advancing Large Language Model as Principled Decision Maker (2025.findings-emnlp)
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| Challenge: | Current language models lack the structured deliberation needed for high-stakes tasks such as healthcare and finance. |
| Approach: | They propose a decision-making framework that guides models to reason over structured representations of actions, attributes, and constraints. |
| Outcome: | The proposed framework achieves up to 30% accuracy gains over strong prompting baselines and enhances alignment in outcomes. |
SafeScientist: Enhancing AI Scientist Safety for Risk-Aware Scientific Discovery (2025.emnlp-main)
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Kunlun Zhu, Jiaxun Zhang, Ziheng Qi, Nuoxing Shang, Zijia Liu, Peixuan Han, Yue Su, Haofei Yu, Jiaxuan You
| Challenge: | Recent advances in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet raised critical ethical and safety concerns. |
| Approach: | They propose a framework to enhance safety and ethical responsibility in AI-driven scientific exploration. |
| Outcome: | The proposed framework significantly improves safety performance by 35% compared to traditional frameworks. |