Zhihao Yang

Hello! Welcome to my homepage.

I am a first-year Ph.D. student in Computer Science at the University of Chinese Academy of Sciences (UCAS), advised by Prof. Min Yang. Prior to this, I obtained my B.Eng. degree in Computer Science from Tongji University. I am generally interested in LLM and VLM Reasoning, especially in advanced inference strategies (such as test-time guidance, collaborative verification, and data synthesis). Previously, I was fortunate to be advised by Prof. Yujun Cai.

Email / Wechat / Scholar / Github

Publications

First Author
	Structuring Reasoning for Complex Rules Beyond Flat Representations Zhihao Yang^, Ancheng Xu^, Jingpeng Li, Liang Yan, Jiehui Zhou, Zhen Qin, Hengyu Chang, Yukun Chen, Longze Chen, Ahmadreza Argha, Hamid Alinejad-Rokny, Minghuan Tan, Yujun Cai, Min Yang arXiv, 2025 [arXiv] An approach which equips LLMs with a structured template to methodically gather and verify evidence within complex rule systems, drastically improving reasoning fidelity.
	Evade: Multimodal benchmark for evasive content detection in e-commerce applications Ancheng Xu^, Zhihao Yang^, Jingpeng Li, Guanghu Yuan, Longze Chen, Liang Yan, Jiehui Zhou, Zhen Qin, Hengyun Chang, Hamid Alinejad-Rokny, Bo Zheng, Min Yang. The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2026. [project page] [arXiv] EVADE-Bench introduces a multimodal benchmark to evaluate evasive content detection and provides a strong foundation for developing more robust detection systems.
Co-authored Papers
	FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration Qiyao Wang, Hongbo Wang, Longze Chen, Zhihao Yang, Guhong Chen, Hamid Alinejad-Rokny, Hui Li, Yuan Lin, Min Yang. arXiv, 2026. [arXiv] FlowPIE tightly couples literature retrieval with idea generation through flow-guided search and test-time evolution, producing research ideas that are more novel, diverse, and feasible than strong LLM- and agent-based baselines.
	RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning Yukun Chen, Jiaming Li, Longze Chen, Ze Gong, Jingpeng Li, Zhen Qin, Hengyu Chang, Ancheng Xu, Zhihao Yang, Hamid Alinejad-Rokny, Qiang Qu, Bo Zheng, Min Yang. International Conference on Machine Learning (ICML), 2026. [arXiv] RuCL reframes curriculum learning around reward design by stratifying rubrics according to model competence, then dynamically reweighting them to progressively improve multimodal reasoning from perception to higher-order logic.

Working Experience

Alibaba

Alibaba Group, Taotian Group – Alimama Advertising Technology Department

Research Intern, 2025
Research Topic: Large Language Models (LLMs) and Multimodal Intelligence

source code

Publications

First Author

Co-authored Papers

Working Experience

Alibaba Group, Taotian Group – Alimama Advertising Technology Department