The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showcasing potential cognitive reasoning abilities in problem-solving and scientific discovery (i.e., AI4Science) once exclusive to human intellect. To comprehensively evaluate current models' performance in cognitive reasoning abilities, we introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities. These challenges encompass a wide range of disciplines spanning seven fields and 62 international Olympic competitions, rigorously examined for data leakage. We argue that the challenges in Olympic competition problems are ideal for evaluating AI's cognitive reasoning due to their complexity and interdisciplinary nature, which are essential for tackling complex scientific challenges and facilitating discoveries. Beyond evaluating performance across various disciplines using answer-only criteria, we conduct detailed experiments and analyses from multiple perspectives. We delve into the models' cognitive reasoning abilities, their performance across different modalities, and their outcomes in process-level evaluations, which are vital for tasks requiring complex reasoning with lengthy solutions. Our extensive evaluations reveal that even advanced models like GPT-4o only achieve a 39.97\% overall accuracy (28.67\% for mathematics and 29.71\% for physics), illustrating current AI limitations in complex reasoning and multimodal integration. Through the OlympicArena, we aim to advance AI towards superintelligence, equipping it to address more complex challenges in science and beyond. We also provide a comprehensive set of resources to support AI research, including a benchmark dataset, an open-source annotation platform, a detailed evaluation tool, and a leaderboard with automatic submission features.

Due to the universality of graph data, node classification shows its great importance in a wide range of real-world applications. Despite the successes of Graph Neural Networks (GNNs), GNN based methods rely heavily on rich connections and perform poorly on low-degree nodes. Since many real-world graphs follow a long-tailed distribution in node degrees, they suffer from a substantial performance bottleneck as a significant fraction of nodes is of low degree. In this paper, we point out that under-represented self-representations and low neighborhood homophily ratio of low-degree nodes are two main culprits. Based on that, we propose a novel method Grace which improves the node representation by self-distillation, and increases neighborhood homophily ratio of low-degree nodes by graph completion. To avoid error propagation of graph completion, label propagation is further leveraged. Experimental evidence has shown that our method well supports real-world graphs, and is superior in balancing degree-related bias and overall performance on node classification tasks.