What If the Bug Had a Dial?
FLAS turns model behavior into a continuous dial, from fixing creature-metaphor bugs to controlled brand …
Read story ↗Research / agents / safety
Xinjie Shen沈鑫杰
As AI becomes embedded everywhere in everyday life, I study how intelligent systems can act safely, protect privacy, and remain reliable over long horizons. My research spans multi-turn attacks, timely defenses, and state-aware safety for tool-using agents; privacy in physical and interactive environments; and the evaluation and training of long-horizon agents. These directions come together in the research initiative Everywhere Safety.
I am a Ph.D. student in Machine Learning at Georgia Tech, advised by Prof. Pan Li. My work has been shaped by research experiences and collaborations across Georgia Tech, CMU, Qwen, Amazon Web Services, and Microsoft Research Asia.
02Recently
All news ↗- Released SEAD, a state-based framework for attack and defense in tool-using agents. Project · Code and data.
- Released VHD-Play, a Qwen technical report on generating verifiable agentic RL environments from solved mechanisms. I also wrote about how we iterated on its teaser figure.
- Qwen3.8-Max-Preview reached No. 1 on NVIDIA’s FlashInfer-Bench during our kernel-optimization work. Our longer real-world self-improvement runs exceeded 60 hours and 10,000 tool calls. Read the report.
- Gave an invited talk at Tsinghua University PI Lab on physical-world privacy awareness in LLMs and VLM agents.
- Released E-Commerce Bench, a 365-day benchmark for long-horizon autonomous business operation.
- Gave the Jiangmen / TechBeat talk Distributed Hidden Malice: Attacks and Defenses, connecting CKA-Agent attacks with TurnGate defenses for multi-turn dialogue.
03Along the way
Qwen
Post-Training Research Intern, Long-Horizon Agents
Built agentic RL data and training pipelines for Qwen3.7-Max and Qwen3.8-Max. My work spanned verifiable environment generation, long-horizon evaluation, kernel optimization, and real-world self-improvement.
Georgia Institute of Technology
Ph.D. Student in Machine Learning
Advised by Prof. Pan Li. I work on trustworthy agentic intelligence, including distributed-intent attacks and defenses, physical-world privacy, and state-aware tool-use safety.
Amazon Web Services
Research Intern, Sparse Retrieval
Developed an $\ell_0$ sparsification method for inference-free sparse retrievers. The released model series averages 1.8 million downloads per month.
Microsoft Research Asia
Research Intern, Autonomous R&D Agents
Worked with Dr. Jiang Bian on data-centric automatic R&D and helped build RD-Agent. Received the Star of Tomorrow award.
Let’s talk.
Research conversations and collaborations are welcome.
xinjie@gatech.edu ↗/Book a conversation ↗