Xikun Zhang

Huanjin Yao, Jiaxing Huang, Yawen Qiu et al.

Reasoning plays a crucial role in advancing Multimodal Large Language Models (MLLMs) toward Artificial General Intelligence. However, existing MLLM benchmarks often fall short in precisely and comprehensively evaluating long-chain reasoning abilities from three key aspects: (1) lack of difficulty and diversity, (2) susceptibility to guessability and memorization, (3) inadequate assessment of intermediate reasoning steps. To fill this gap, we introduce MMReason, a new benchmark designed to precisely and comprehensively evaluate MLLM long-chain reasoning capability with diverse, open-ended, challenging questions. First, we curate challenging questions requiring multi-step reasoning from various fields (i.e., 6 disciplines) and multiple difficulty levels (i.e., from pre-university to university, and from foundational to competition tiers). Second, these questions are reformulated into an open-ended format and filtered using a multi-model voting technique to eliminate shortcut cases related to guessing and memorization, ensuring robust reasoning evaluations. Third, we annotate the questions with detailed step-by-step solutions, and design a reference-based ternary scoring mechanism to reliably assess intermediate reasoning steps. With MMReason, we benchmark popular leading MLLMs and provide an in-depth analysis of their reasoning capabilities. We hope MMReason will serve as a valuable resource for advancing MLLM reasoning research. Code will be available at https://github.com/HJYao00/MMReason.

Renqiang Luo, Ziqi Xu, Xikun Zhang et al.

Augmenting specialised machine learning techniques into traditional graph learning models has achieved notable success across various domains, including federated graph learning, dynamic graph learning, and graph transformers. However, the intricate mechanisms of these specialised techniques introduce significant challenges in maintaining model fairness, potentially resulting in discriminatory outcomes in high-stakes applications such as recommendation systems, disaster response, criminal justice, and loan approval. This paper systematically examines the unique fairness challenges posed by Graph Learning augmented with Machine Learning (GL-ML). It highlights the complex interplay between graph learning mechanisms and machine learning techniques, emphasising how the augmentation of machine learning both enhances and complicates fairness. Additionally, we explore four critical techniques frequently employed to improve fairness in GL-ML methods. By thoroughly investigating the root causes and broader implications of fairness challenges in this rapidly evolving field, this work establishes a robust foundation for future research and innovation in GL-ML fairness.