SECOND-YEAR PhD STUDENT

Yang Sheng 盛杨

Fudan UniversityShanghai Innovation Institute

I am a second-year Ph.D. student at Fudan University and Shanghai Innovation Institute, broadly interested in understanding and advancing AI toward safe and capable AGI. My long-term vision is to help build AI systems that are both powerful and trustworthy, with a lasting positive impact on human society.

My interests span mechanistic interpretability, model post-training, and alignment, alongside reinforcement learning, continual learning, and recursive self-improvement (RSI). I want to understand not only how models work, but how that understanding and better verification can improve training and enable reliable, autonomous learning.

I also enjoy building practical AI tools, technologies, and products with real-world impact, and am always happy to connect with people who share these interests or would like to collaborate.

Portrait of Yang Sheng

Based in Shanghai, China

News

  • 2026.09

    Our paper on circuit extraction and comparison was accepted to NeurIPS 2026.

  • 2026.09

    Starting my second year of doctoral research.

  • 2026.04

    FormalJudge was accepted to the ICML 2026 main conference.

01 / Publications

Selected publications

IntentSpecificationVerification

From requirements to checkable behavior

Conceptual illustration
ICML 2026 · Main conference · Accepted

FormalJudge: A Neuro-Symbolic Paradigm for Agentic Oversight

Jiayi Zhou*, Yang Sheng*, Hantao Lou*, Yaodong Yang, Jie Fu

* Equal contribution.

Connecting natural-language requirements to formal specifications, so agent oversight can include checks beyond a model’s judgment alone.

  • Agent oversight
  • Formal verification
BibTeX · ICML 2026
@inproceedings{pmlr-v306-zhou26ao,
  title={{F}ormal{J}udge: A Neuro-Symbolic Paradigm for Agentic Oversight},
  author={Zhou, Jiayi and Sheng, Yang and Lou, Hantao and Yang, Yaodong and Fu, Jie},
  booktitle={Proceedings of the 43rd International Conference on Machine Learning},
  pages={165723--165752},
  year={2026},
  volume={306},
  series={Proceedings of Machine Learning Research},
  publisher={PMLR},
  url={https://proceedings.mlr.press/v306/zhou26ao.html}
}
ExtractCompareInterpret

The object and comparison both matter

Conceptual illustration
NeurIPS 2026 · Poster

Circuit Claims Depend on What Is Extracted and How It Is Compared

Yang Sheng, Jie Fu

A controlled study of how conclusions about extracted circuits depend on the object being reported, the extraction procedure, and the level and protocol of comparison.

  • Mechanistic interpretability
  • Circuit analysis
BibTeX · NeurIPS 2026
@inproceedings{sheng2026circuit,
  title={Circuit Claims Depend on What Is Extracted and How It Is Compared},
  author={Yang Sheng and Jie Fu},
  booktitle={The Fortieth Annual Conference on Neural Information Processing Systems},
  year={2026},
  url={https://openreview.net/forum?id=7RfZ1ThE5z}
}

02 / Research

Research interests

01

Mechanistic interpretability

Understanding how capabilities and behaviors arise inside language models, and using those insights to guide evaluation, training, and intervention.

02

Post-training & alignment

Designing feedback and learning objectives for post-training and reinforcement learning, with attention to both capabilities and reliable behavior.

03

Continual learning & RSI

How models retain and integrate new knowledge, learn beyond a single training run, and improve themselves while remaining reliable.

03 / Projects

Open-source projects

04 / Background

Education

  • 2025 — present

    PhD · Fudan University

    Research at Shanghai Innovation Institute

  • 2021 — 2025

    BSc Mathematics · University of Birmingham

    BSc Statistics · Jinan University
    Jinan University–University of Birmingham Joint Institute

05 / Contact

Get in touch

For research discussions or collaborations, please email me.