Zhuohang Jiang

Zhuohang Jiang

I am currently pursuing a PhD degree at the Hong Kong Polytechnic University. My supervisors are Qing Li and Wenqi Fan. My current Cumulative GPA is 3.60/4.00.

I studied at Sichuan University (SCU) from 2020 to 2024, where I majored in Computer Science & Technology. My Major GPA (CS courses): 3.79/4, 89.39/100; Overall GPA: 3.78/4, 89.25/100

During my time at Sichuan University, I worked as a research assistant at MachineILab from 2022 to 2024, advised by Prof. JiZhe Zhou. I participated in one National Natural Science Foundation of China project and one National Key R&D Program of China.

Email / CV / Google Scholar / Github

Research Topics

My research interests lie in large language models (LLMs), retrieval-augmented generation (RAG), and Recommender Systems (RecSys). I focus on both theoretical foundations and practical applications of LLM-based systems.

My previous research was primarily focused on topics within computer vision, such as tampering detection and object recognition tasks. I have contributed to the design of high-impact benchmarks, such as HiBench, and comprehensive surveys like WebAgents. My work has been published in top-tier conferences, including NeurIPS 2024 (spotlight) and AAAI 2025, and has accumulated 80+ citations, with an h-index of 4.

News

🎤 2025-08-07 - Invited to give a talk "Understanding Hierarchical Data with Large Language Models: RAG, Structural Reasoning, and Future Directions" at KDD 2025 Reasoning Day in Toronto, Canada! 🎙️

🏅 2025-06-18 - Achieved 12th place globally in KDD Cup 2025 - Meta CRAG-MM Multimodal Retrieval Challenge among hundreds of international teams! 🌍

🏆 2025-05-16 - Our benchmark paper HiBench was accepted to KDD Benchmark Track! 🎉

🎉 2025-05-07 - Our survey paper A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models was accepted to KDD Tutorial Track! 🎊

📜 2025-03-30 - Completed the survey paper A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models.

📘 2025-03-01 - Completed the HiBench paper and released the code and dataset on GitHub and Hugging Face.

🌟 2025-01-15 - Mesoscopic Insights: Orchestrating Multi-Scale & Hybrid Architecture for Image Manipulation Localization was published in AAAI 2025.

🏆 2024-12-01 - IMDL-BenCo was published in NeurIPS 2024 Benchmark Tracks and received a Spotlight award.

🎓 2024-09-01 - Beginning my pursuit of a PhD degree in Hong Kong PolyU.

🎓 2024-06-26 - Got Outstanding Graduate Award from Sichuan University and Sichuan Province! 🎉

🎓 2024-06-26 - Graduated from Sichuan University with a bachelor's degree.

🛠️ 2024-06-12 - Completed the co-work project IMDLBenCo and finished a paper IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization

🔍 2024-05-24 - Finished a paper Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning

📚 2023-08-01 - Participated in NUS Summer School research program at National University of Singapore, completed face recognition project! 🇸🇬

Selected Publications

[KDD'25] A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models
Liangbo Ning, Ziran Liang, Zhuohang Jiang, Haohao Qu, Yujuan Ding, Wenqi Fan, Xiao-yong Wei, Shanru Lin, Hui Liu, Philip S. Yu, Qing Li

With the advancement of web techniques, they have significantly revolutionized various aspects of people's lives. Despite the importance of the web, many tasks performed on it are repetitive and time-consuming, negatively impacting overall quality of life. To efficiently handle these tedious daily tasks, one of the most promising approaches is to advance autonomous agents based on Artificial Intelligence (AI) techniques, referred to as AI Agents, as they can operate continuously without fatigue or performance degradation. In the context of the web, leveraging AI Agents -- termed WebAgents -- to automatically assist people in handling tedious daily tasks can dramatically enhance productivity and efficiency. Recently, Large Foundation Models (LFMs) containing billions of parameters have exhibited human-like language understanding and reasoning capabilities, showing proficiency in performing various complex tasks. This naturally raises the question: `Can LFMs be utilized to develop powerful AI Agents that automatically handle web tasks, providing significant convenience to users?' To fully explore the potential of LFMs, extensive research has emerged on WebAgents designed to complete daily web tasks according to user instructions, significantly enhancing the convenience of daily human life. In this survey, we comprehensively review existing research studies on WebAgents across three key aspects: architectures, training, and trustworthiness. Additionally, several promising directions for future research are explored to provide deeper insights.

[KDD'25] HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoning
Zhuohang Jiang*, Pangjing Wu*, Ziran Liang*, Peter Q. Chen*, Xu Yuan*, Ye Jia*, Jiancheng Tu*, Chen Li, Peter H.F. Ng, Qing Li

Structure reasoning is a fundamental capability of large language models (LLMs), enabling them to reason about structured commonsense and answer multi-hop questions. However, existing benchmarks for structure reasoning mainly focus on horizontal and coordinate structures (\emph{e.g.} graphs), overlooking the hierarchical relationships within them. Hierarchical structure reasoning is crucial for human cognition, particularly in memory organization and problem-solving. It also plays a key role in various real-world tasks, such as information extraction and decision-making. To address this gap, we propose HiBench, the first framework spanning from initial structure generation to final proficiency assessment, designed to benchmark the hierarchical reasoning capabilities of LLMs systematically. HiBench encompasses six representative scenarios, covering both fundamental and practical aspects, and consists of 30 tasks with varying hierarchical complexity, totaling 39,519 queries. To evaluate LLMs comprehensively, we develop five capability dimensions that depict different facets of hierarchical structure understanding. Through extensive evaluation of 20 LLMs from 10 model families, we reveal key insights into their capabilities and limitations: 1) existing LLMs show proficiency in basic hierarchical reasoning tasks; 2) they still struggle with more complex structures and implicit hierarchical representations, especially in structural modification and textual reasoning. Based on these findings, we create a small yet well-designed instruction dataset, which enhances LLMs' performance on HiBench by an average of 88.84% (Llama-3.1-8B) and 31.38% (Qwen2.5-7B) across all tasks. The HiBench dataset and toolkit are available here, this https URL, to encourage evaluation.

[AAAI'25] Mesoscopic Insights: Orchestrating Multi-Scale & Hybrid Architecture for Image Manipulation Localization
Xuekang Zhu, Xiaochen Ma, Lei Su, Zhuohang Jiang, Bo Du, Xiwen Wang, Zeyu Lei, Wentao Feng, Chi-Man Pun, Jizhe Zhou

The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (microscopic-level) traces. However, in practice, most tampering aims to deceive the audience by altering image semantics. As a result, manipulation commonly occurs at the object level (macroscopic level), which is equally important as microscopic traces. Therefore, integrating these two levels into the mesoscopic level presents a new perspective for IML research. Inspired by this, our paper explores how to simultaneously construct mesoscopic representations of micro and macro information for IML and introduces the Mesorch architecture to orchestrate both. Specifically, this architecture i) combines Transformers and CNNs in parallel, with Transformers extracting macro information and CNNs capturing micro details, and ii) explores across different scales, assessing micro and macro information seamlessly. Additionally, based on the Mesorch architecture, the paper introduces two baseline models aimed at solving IML tasks through mesoscopic representation. Extensive experiments across four datasets have demonstrated that our models surpass the current state-of-the-art in terms of performance, computational complexity, and robustness.

[NIPS'24] IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization
Xiaochen Ma*, Xuekang Zhu*, Lei Su*, Bo Du*, Zhuohang Jiang*, Bingkui Tong*, Zeyu Lei*, Xinyu Yang*, Chi-Man Pun, Jiancheng Lv, Jizhe Zhou

A comprehensive benchmark is yet to be established in the Image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo:i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility;ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark; and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs.

Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning
Zhuohang Jiang*, Bingkui Tong*, Xia Du, Ahmed Alhammadi, Jizhe Zhou

Zhuohang Jiang*, Bingkui Tong*, Xia Du, Ahmed Alhammadi, Jizhe Zhou
To explicitly derive the objects' privacy class from the scene contexts, in this paper, we interpret the POI task as a visual reasoning task aimed at the privacy of each object in the scene. Following this interpretation, we propose the PrivacyGuard framework for POI. PrivacyGuard contains three stages. i) Structuring: an unstructured image is first converted into a structured, heterogeneous scene graph that embeds rich scene contexts. ii) Data Augmentation: a contextual perturbation oversampling strategy is proposed to create slightly perturbed privacy-sensitive objects in a scene graph, thereby balancing the skewed distribution of privacy classes. iii) Hybrid Graph Generation & Reasoning: the balanced, heterogeneous scene graph is then transformed into a hybrid graph by endowing it with extra "node-node" and "edge-edge" homogeneous paths. These homogeneous paths allow direct message passing between nodes or edges, thereby accelerating reasoning and facilitating the capturing of subtle context changes.

[ICONIP'23] TPTGAN: Two-Path Transformer-Based Generative Adversarial Network Using Joint Magnitude Masking and Complex Spectral Mapping for Speech Enhancement
Zhaoyi Liu, Zhuohang Jiang, Wendian Luo, Zhuoyao Fan, Haoda Di, Yufan Long, Haizhou Wang

In this paper, we propose a two-path transformer-based metric generative adversarial network (TPTGAN) for speech enhancement in the time-frequency domain. The generator consists of an encoder, a two-stage transformer module, a magnitude mask decoder and a complex spectrum decoder. Published in ICONIP 2023.

IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer
Xiaochen Ma, Bo Du, Zhuohang Jiang, Ahmed Y. Al Hammadi, Jizhe Zhou

Due to limited datasets, there is currently no pure ViT-based approach for IML to serve as a benchmark, and CNNs dominate the entire task. Nevertheless, CNNs suffer from weak long-range and non-semantic modeling. To bridge this gap, based on the fact that artifacts are sensitive to image resolution, amplified under multi-scale features, and massive at the manipulation border, we formulate the answer to the former question as building a ViT with high-resolution capacity, multi-scale feature extraction capability, and manipulation edge supervision that could converge with a small amount of data. We term this simple but effective ViT paradigm IML-ViT, which has significant potential to become a new benchmark for IML. Extensive experiments on five benchmark datasets verified our model outperforms the state-of-the-art manipulation localization methods.

Perceptual MAE for Image Manipulation Localization: A High-level Vision Learner Focusing on Low-level Features
Xiaochen Ma, Zhuohang Jiang, Xiong Xu, Chi-Man Pun, Jizhe Zhou

This necessitates IML models to carry out a semantic understanding of the entire image. In this paper, we reformulate the IML task as a high‑level vision task that greatly benefits from low‑level features. We propose a method to enhance the Masked Autoencoder (MAE) by incorporating high‑resolution inputs and a perceptual loss supervision module, which we term Perceptual MAE (PMAE). While MAE has demonstrated an impressive understanding of object semantics, PMAE can also comprehend low‑level semantics with our proposed enhancements. This paradigm effectively unites the low‑level and high‑level features of the IML task and outperforms state‑of‑the‑art tampering localization methods on five publicly available datasets, as evidenced by extensive experiments.

Selected Projects

HiBench: Benchmark for Hierarchical Reasoning
First Author & Team Leader • KDD 2025 Benchmark Track • 2025

Designed and developed the first comprehensive benchmark for evaluating LLMs' capability on hierarchical structure reasoning. The benchmark encompasses six representative scenarios with 39,519 queries across varying hierarchical complexity. Key contributions: (1) Led the architectural design and implementation of the evaluation framework, (2) Coordinated a multi-institutional team across different time zones, (3) Open-sourced the complete toolkit including dataset, evaluation metrics, and baseline implementations. The benchmark has been accepted as an oral presentation at KDD 2025 and is being adopted by multiple research groups for hierarchical reasoning evaluation.

Meta CRAG-MM: Multimodal Retrieval Challenge
Team Leader • KDD Cup 2025 • 2025
Led a team to achieve 12th place globally among hundreds of international teams in the Meta CRAG-MM Multimodal Retrieval Challenge. Key contributions: (1) Designed novel multimodal fusion architectures combining vision and language understanding, (2) Implemented efficient retrieval-augmented generation pipelines, (3) Coordinated team efforts in model development, hyperparameter optimization, and submission strategies. The challenge focused on developing AI systems capable of understanding and retrieving information from multimodal content, which aligns with current trends in large multimodal models.

IMDL-BenCo: Benchmark for Image Manipulation Detection & Localization
Co-First Author • NeurIPS 2024 Benchmark Track — Spotlight • 2024

Developed the first comprehensive benchmark and codebase for Image Manipulation Detection & Localization (IMDL). Key contributions: (1) Implemented GPU-accelerated evaluation metrics for fair and efficient comparison, (2) Designed modular codebase architecture enabling easy customization and extension, (3) Co-authored the manuscript that received a Spotlight Award at NeurIPS 2024. The benchmark includes 8 state-of-the-art models, 15 evaluation metrics, and comprehensive robustness evaluation protocols, significantly advancing the field's standardization and reproducibility.

Invited Talks

Understanding Hierarchical Data with Large Language Models: RAG, Structural Reasoning, and Future Directions
Invited Talks • Reasoning Day @ KDD 2025 • Toronto, ON, Canada • Aug 2025
Invited to deliver a presentation at the prestigious KDD 2025 Reasoning Day workshop.
The talk will explore cutting-edge developments in leveraging Large Language Models for hierarchical data understanding,
with particular focus on Retrieval-Augmented Generation (RAG) systems and structural reasoning capabilities.
Key topics:
(1) Novel approaches to hierarchical data representation in LLM contexts,
(2) Integration of structural reasoning with retrieval-augmented generation,
(3) Future research directions in reasoning-enhanced AI systems,
(4) Practical applications and deployment considerations for hierarchical reasoning in real-world scenarios.
This invitation recognizes the impact of our HiBench work and positions our research at the forefront of LLM reasoning capabilities.

Education

Sichuan University, Chengdu, Sichuan, China
B.E. in Computer Science and Technology • Sep. 2020 to Jun. 2024

Hong Kong Polytechnic University, Hongkong, China
PHD. in Computer Science and Technology • Sep. 2024 to Present

Experience

National University of Singapore (NUS)
Summer School Participant • Aug. 2023
• Participated in intensive research program at School of Computing
• Completed face recognition project using CNN-based feature extraction and similarity matching
• Gained international research experience and cross-cultural collaboration skills

DICALab, Sichuan University
Research Assistant • Sep. 2022 to Jun. 2024
Advisor: Prof. JiZhe Zhou
• Developed graph-based frameworks for privacy-sensitive object detection
• Participated in National Natural Science Foundation of China project
• Contributed to National Key R&D Program of China
• Co-authored multiple publications in top-tier conferences and journals

Selected Awards
KDD Cup 2025 — Meta CRAG-MM Toronto, Canada, 2025	12th Place (Global)
NeurIPS 2024 — IMDL‑BenCo (Co‑first Author) Vancouver, Canada, 2024	Spotlight Award
Outstanding Graduate Sichuan University & Sichuan Province, 2024	Top Achievement
Tencent Scholarship Sichuan University, China, 2023	Top 2%
A-Level Certificate Comprehensive Quality Evaluation, China, 2023	Excellence
Computer Design Competition China, 2023	Provincial First Prize
Comprehensive First Class Scholarship Sichuan University, Sichuan, China, 2022	Top 1%
Outstanding Students of Sichuan University Sichuan University, Sichuan, China, 2022	Top 5%

Professional Service
Conference & Journal Reviewer 2023-2025	TIP, CVPR, NeurIPS, KDD
Teaching Assistant The Hong Kong Polytechnic University (PolyU)	Artificial Intelligence (COMP4431) NLP Practicum (COMP5423)

Skills
Research Topics	Large Language Models (LLMs), Retrieval‑Augmented Generation (RAG), Recommender Systems (RecSys)
Frameworks & Tools	PyTorch, Hugging Face, NumPy, Docker, Git, Anaconda
Languages	Mandarin (native), English (fluent, IELTS 6.5)

Updated at Jul. 2025

Thanks Jon Barron for this amazing template