Graphs are a fundamental data structure for modeling relational systems, where entities (nodes) are connected by pairwise interactions (edges). This representation naturally arises in a wide range of domains, including chemistry (molecular structures) [2], social networks [3], Transportation & Logistics [4], Electrical Grids & Circuits [5], Communication Networks, Finance and Economics [6], and many more. Traditional machine learning models often struggle to process such non-Euclidean data due to their irregular topology. To address this, Graph Neural Networks (GNNs) have emerged as a powerful class of models that learn over graph-structured inputs by iteratively aggregating and transforming information from a node's neighborhood. By capturing both local structure and node features, GNNs enable tasks such as node classification, link prediction, and graph-level regression. Among their many variants, models like Graph Convolutional Networks (GCNs) [7], Graph Attention Networks (GATs) [8], and GraphSAGE [9] have demonstrated strong performance in both transductive and inductive settings, making GNNs a key building block in modern geometric deep learning. While classical GNNs have achieved remarkable success, their scalability and expressiveness can be limited by the classical nature of their computation [10], especially when modeling systems with inherent quantum structure, such as molecules or quantum materials. Quantum Graph Neural Networks (QGNNs) aim to address this by leveraging the computational power of parameterized quantum circuits to encode and process graph data in a quantum-enhanced latent space.

Abstract--Classical reinforcement learning (RL) methods often struggle in complex, high-dimensional environments because of their extensive parameter requirements and challenges posed by stochastic, non-deterministic settings. This study introduces quantum deep reinforcement learning (QDRL) to train humanoid agents efficiently. While previous quantum RL models focused on smaller environments, such as wheeled robots and robotic arms, our work pioneers the application of QDRL to humanoid robotics, specifically in environments with substantial observation and action spaces, such as MuJoCo's Humanoid-v4 and Walker2d-v4. Using parameterized quantum circuits, we explored a hybrid quantum-classical setup to directly navigate high-dimensional state spaces, bypassing traditional mapping and planning. By integrating quantum computing with deep RL, we aim to develop models that can efficiently learn complex navigation tasks in humanoid robots. We evaluated the performance of the Soft Actor-Critic (SAC) in classical RL against its quantum implementation. The results show that the quantum SAC achieves an 8% higher average return (246.40)

In the era of large language model, relation extraction (RE) plays an important role in information extraction through the transformation of unstructured raw text into structured data (Wadhwa et al., 2023). In this paper, we systematically compare the performance of deep supervised learning approaches without transformers and those with transformers. We used a series of non-transformer architectures such as PA-LSTM(Zhang et al., 2017), C-GCN(Zhang et al., 2018), and AGGCN(attention guide GCN)(Guo et al., 2019), and a series of transformer architectures such as BERT, RoBERTa, and R-BERT(Wu and He, 2019). Our comparison included traditional metrics like micro F1, as well as evaluations in different scenarios, varying sentence lengths, and different percentages of the dataset for training. Our experiments were conducted on TACRED, TACREV, and RE-TACRED. The results show that transformer-based models outperform non-transformer models, achieving micro F1 scores of 80-90% compared to 64-67% for non-transformer models. Additionally, we briefly review the research journey in supervised relation classification and discuss the role and current status of large language models (LLMs) in relation extraction.

With the rapid advancement of Large Language Models (LLMs), AI-driven music generation has become a vibrant and fruitful area of research. However, the representation of musical data remains a significant challenge. To address this, a novel, machine-learning-friendly music notation system, YNote, was developed. This study leverages YNote to train an effective classification model capable of distinguishing whether a piece of music was composed by a human (Native), a rule-based algorithm (Algorithm Generated), or an LLM (LLM Generated). We frame this as a text classification problem, applying the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to extract structural features from YNote sequences and using the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The resulting model achieves an accuracy of 98.25%, successfully demonstrating that YNote retains sufficient stylistic information for analysis. More importantly, the model can identify the unique " technological fingerprints " left by different AI generation techniques, providing a powerful tool for tracing the origins of AI-generated content.

We present our systems for Track 2 (General Arabic Health QA, MedArabiQ) of the AraHealthQA-2025 shared task, where our methodology secured 2nd place in both Sub-Task 1 (multiple-choice question answering) and Sub-Task 2 (open-ended question answering) in Arabic clinical contexts. For Sub-Task 1, we leverage the Gemini 2.5 Flash model with few-shot prompting, dataset preprocessing, and an ensemble of three prompt configurations to improve classification accuracy on standard, biased, and fill-in-the-blank questions. For Sub-Task 2, we employ a unified prompt with the same model, incorporating role-playing as an Arabic medical expert, few-shot examples, and post-processing to generate concise responses across fill-in-the-blank, patient-doctor Q&A, GEC, and paraphrased variants.

Understanding human behavior traits is central to applications in human-computer interaction, computational social science, and personalized AI systems. Such understanding often requires integrating multiple modalities to capture nuanced patterns and relationships. However, existing resources rarely provide datasets that combine behavioral descriptors with complementary modalities such as facial attributes and biographical information. To address this gap, we present PersonaX, a curated collection of multimodal datasets designed to enable comprehensive analysis of public traits across modalities. PersonaX consists of (1) CelebPersona, featuring 9444 public figures from diverse occupations, and (2) AthlePersona, covering 4181 professional athletes across 7 major sports leagues. Each dataset includes behavioral trait assessments inferred by three high-performing large language models, alongside facial imagery and structured biographical features. We analyze PersonaX at two complementary levels. First, we abstract high-level trait scores from text descriptions and apply five statistical independence tests to examine their relationships with other modalities. Second, we introduce a novel causal representation learning (CRL) framework tailored to multimodal and multi-measurement data, providing theoretical identifiability guarantees. Experiments on both synthetic and real-world data demonstrate the effectiveness of our approach. By unifying structured and unstructured analysis, PersonaX establishes a foundation for studying LLM-inferred behavioral traits in conjunction with visual and biographical attributes, advancing multimodal trait analysis and causal reasoning.

Convolutional Neural Networks (CNNs) excel at image classification but remain vulnerable to common corruptions that humans handle with ease. A key reason for this fragility is their reliance on local texture cues rather than global object shapes -- a stark contrast to human perception. To address this, we propose two complementary regularization strategies designed to encourage shape-biased representations and enhance robustness. The first introduces an auxiliary loss that enforces feature consistency between original and low-frequency filtered inputs, discouraging dependence on high-frequency textures. The second incorporates supervised contrastive learning to structure the feature space around class-consistent, shape-relevant representations. Evaluated on the CIFAR-10-C benchmark, both methods improve corruption robustness without degrading clean accuracy. Our results suggest that loss-level regularization can effectively steer CNNs toward more shape-aware, resilient representations.

Self-supervised learning (SSL) conventionally relies on the instance consistency paradigm, assuming that different views of the same image can be treated as positive pairs. However, this assumption breaks down for non-iconic data, where different views may contain distinct objects or semantic information. In this paper, we investigate the effectiveness of SSL when instance consistency is not guaranteed. Through extensive ablation studies, we demonstrate that SSL can still learn meaningful representations even when positive pairs lack strict instance consistency. Furthermore, our analysis further reveals that increasing view diversity, by enforcing zero overlapping or using smaller crop scales, can enhance downstream performance on classification and dense prediction tasks. However, excessive diversity is found to reduce effectiveness, suggesting an optimal range for view diversity. To quantify this, we adopt the Earth Mover's Distance (EMD) as an estimator to measure mutual information between views, finding that moderate EMD values correlate with improved SSL learning, providing insights for future SSL framework design. We validate our findings across a range of settings, highlighting their robustness and applicability on diverse data sources.

Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper focuses on comparing different distributed adversarial training algorithms--including centralized and decentralized strategies--within multi-agent learning environments. Previous studies have highlighted the importance of model flatness in determining robustness. To this end, we develop a general theoretical framework to study the escaping efficiency of these algorithms from local minima, which is closely related to the flatness of the resulting models. We show that when the perturbation bound is sufficiently small (i.e., when the attack strength is relatively mild) and a large batch size is used, decentralized adversarial training algorithms--including consensus and diffusion--are guaranteed to escape faster from local minima than the centralized strategy, thereby favoring flatter minima. However, as the perturbation bound increases, this trend may no longer hold. In the simulation results, we illustrate our theoretical findings and systematically compare the performance of models obtained through decentralized and centralized adversarial training algorithms. The results highlight the potential of decentralized strategies to enhance the robustness of models in distributed settings.

Recently, large language models (LLMs) have achieved remarkable breakthroughs in general domains such as programming and writing, and have demonstrated strong potential in various scientific research scenarios. However, the capabilities of AI models in the highly specialized field of materials characterization and analysis have not yet been systematically or sufficiently validated. To address this gap, we present MatQnA, the first multi-modal benchmark dataset specifically designed for material characterization techniques. MatQnA includes ten mainstream characterization methods, such as X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), etc. We employ a hybrid approach combining LLMs with human-in-the-loop validation to construct high-quality question-answer pairs, integrating both multiple-choice and subjective questions. Our preliminary evaluation results show that the most advanced multi-modal AI models (e.g., GPT-4.1, Claude 4, Gemini 2.5, and Doubao Vision Pro 32K) have already achieved nearly 90% accuracy on objective questions in materials data interpretation and analysis tasks, demonstrating strong potential for applications in materials characterization and analysis. The MatQnA dataset is publicly available at https://huggingface.co/datasets/richardhzgg/matQnA.