Built with and by Teycir Ben Soltane•
How to Use•FAQ•GitHub•arXiv.org•
Share:
ArXivCSExplorer
☆☆Bookmarks🏆RSSHow to UseFAQ
Home/Authors/Yuanfan Li

Yuanfan Li

3 indexed papers

Recent (6 mo)
3
With code
0
Influential cites
0
Benchmarked
0

Publications per year

3
26

Top categories

Crypto×2NLP×2AI×1ML×1

Frequent co-authors

Qi Zhou2×
Zhongyu He1×
Fei Huang1×
Tianyu Chen1×
Siyuan Chen1×
Xingyang Li1×

Research Timeline

2026
MGTEVAL: An Interactive Platform for Systemtic Evaluation of Machine-Generated Text Detectors

The paper introduces MGTEVAL, a comprehensive and extensible platform designed to systematically evaluate the performance, robustness, and efficiency of machine-generated text detectors.

Fight Poison with Poison: Enhancing Robustness in Few-shot Machine-Generated Text Detection with Adversarial Training

The paper proposes REACT, an adversarial training framework that significantly enhances the robustness and few-shot performance of machine-generated text detection by having a Retrieval-Augmented Generation (RAG)-powered attacker co-evolve with the detector.

SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training

SIRI introduces a self-internalizing reinforcement learning framework that allows LLM agents to autonomously discover and integrate reusable skills directly into their core policy, significantly improving performance on complex tasks without external skill generators.

Highlighted terms show continued research focus across papers

Papers

cs.AIcs.LGRecentJun 1, 2026

SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training

Zhongyu He, Yuanfan Li, Fei Huang, Tianyu Chen +8 more

SIRI introduces a self-internalizing reinforcement learning framework that allows LLM agents to autonomously discover and integrate reusable skills directly into their core policy, significantly impro…

View →
cs.CRcs.CLRecentMay 4, 2026

Fight Poison with Poison: Enhancing Robustness in Few-shot Machine-Generated Text Detection with Adversarial Training

Wenjing Duan, Qi Zhou, Yuanfan Li

The paper proposes REACT, an adversarial training framework that significantly enhances the robustness and few-shot performance of machine-generated text detection by having a Retrieval-Augmented Gene…

View →
cs.CRcs.CLRecentApr 28, 2026

MGTEVAL: An Interactive Platform for Systemtic Evaluation of Machine-Generated Text Detectors

Yuanfan Li, Qi Zhou, Chengzhengxu Li, Zhaohan Zhang +4 more

The paper introduces MGTEVAL, a comprehensive and extensible platform designed to systematically evaluate the performance, robustness, and efficiency of machine-generated text detectors.

View →