Yuping Lin

Super excited to share that two of our papers have been accepted to the Main Conference at #ACL2026! 🎉 Huge thanks to my amazing co-authors and collaborators. Check out the thread below for a quick TL;DR on our work covering Mechanistic Interpretability and LLM Agents. 👇🧵

📄 2/ How Memory Management Impacts LLM Agents We explored how memory addition/deletion impacts long-term behavior of LLM Agents. And we offer guidance for robust memory design! 🔗 arxiv.org/abs/2505.16067

📄 1/ Retrieval Heads are Dynamic We demonstrate that retrieval heads in LLMs vary dynamically across timesteps. Furthermore, the model's hidden state actually encodes a predictive signal for future retrieval patterns. 🔗 arxiv.org/abs/2602.11162

Super excited to share that two of our papers have been accepted to the Main Conference at #ACL2026! 🎉 Huge thanks to my amazing co-authors and collaborators. Check out the thread below for a quick TL;DR on our work covering Mechanistic Interpretability and LLM Agents. 👇🧵

Our paper for LLM watermark is accepeted by NAACL findings! We proposed a new method to strengthen the robustness of watermark agains paraphrase using the semantics. This is very meaningful factor for the practical application! Please find the paper at openreview.net/forum?id=hbMR7…

🙋‍♂️What is the wildest dream for graph foundation models? 🎯Graph across domains → a single model → all the downstream 🙋‍♀️Can we achieve that? ✅Yes! UniAug: Cross-Domain Graph Data Scaling with Diffusion Models 📃arxiv.org/pdf/2406.01899

🚀 Excited to share our latest research on enhancing privacy in RAG systems! arxiv.org/pdf/2406.14773 Our paper introduces SAGE, a novel approach using synthetic data to protect sensitive information while maintaining high utility. #AI #Privacy #MachineLearning #RAG #DataSecurity

Code available at: github.com/yuplin2333/rep…

🤔 Understanding why some jailbreak methods succeed helps us build more resilient LLMs. This is crucial for safe AI deployment in real-world applications. #Research #AI #LLM #Jailbreak

✨ Excited to share our new preprint "Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis"! arxiv.org/abs/2406.10794 🔍 We delve into why some jailbreak attacks succeed by exploring harmful and harmless prompts in the LLM's representation space.