Papers by Yijun Lin
Self-Evolving Multi-Agent Systems via Textual Backpropagation (2026.findings-acl)
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Xiaowen Ma, Yunpu Ma, Chenyang Lin, Sikuan Yan, Jinhe Bi, Zixuan Cao, Yijun Tian, Volker Tresp, Hinrich Schuetze
| Challenge: | Large Language Models (LLMs) have proven effective for addressing complex, high-dimensional tasks, but current approaches rely on static, manually engineered multi-agent configurations. |
| Approach: | They propose a framework that conceptualizes multi-agent collaboration as a layered neural network architecture. |
| Outcome: | The proposed framework surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements. |
rosaOS: Agentic Operating System for Embodied LLMs (2026.acl-demo)
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| Challenge: | Existing LLM–robotic systems are tightly intertwined, making it difficult to switch hardware, add extra capabilities, or expand to multiple devices without bespoke integration. |
| Approach: | They propose an open-source agentic operating system for embodied LLMs . rosaOS integrates agentic tool-calling and ROS for robot interactions . |
| Outcome: | The proposed system integrates with the Reachy Mini robot and supports a multi-device setup with a quadruped robot, wheeled mobile robot, and smart lamp. |
QuackIR: Retrieval in DuckDB and Other Relational Database Management Systems (2025.emnlp-industry)
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| Challenge: | Existing vector databases for RAG are needed for large language models, but there are no alternatives. |
| Approach: | They propose to leverage existing relational databases for retrieval-augmented generation . they use duckDB, SQLite, and PostgreSQL integrations to demonstrate their effectiveness . |
| Outcome: | The proposed approach is comparable to existing IR tools. |
Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)
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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang
| Challenge: | Large Language Models have been developed to deal with real-world crimes, but it remains unclear whether they internalize authentic knowledge or are forced to simulate toxic language patterns. |
| Approach: | They construct knowledge-intensive Q&A to investigate misuse threats of Large Language Models in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness. |
| Outcome: | The findings raise concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM . |