General practitioners (GPs) serve as the cornerstone of primary healthcare systems by providing continuous and comprehensive medical services. However, due to community-oriented nature of their practice, uneven training and resource gaps, the clinical proficiency among GPs can vary significantly across regions and healthcare settings. Currently, Large Language Models (LLMs) have demonstrated great potential in clinical and medical applications, making them a promising tool for supporting general practice. However, most existing benchmarks and evaluation frameworks focus on exam-style assessments-typically multiple-choice question-lack comprehensive assessment sets that accurately mirror the real-world scenarios encountered by GPs. To evaluate how effectively LLMs can make decisions in the daily work of GPs, we designed GPBench, which consists of both test questions from clinical practice and a novel evaluation framework. The test set includes multiple-choice questions that assess fundamental knowledge of general practice, as well as realistic, scenario-based problems. All questions are meticulously annotated by experts, incorporating rich fine-grained information related to clinical management. The proposed LLM evaluation framework is based on the competency model for general practice, providing a comprehensive methodology for assessing LLM performance in real-world settings. As the first large-model evaluation set targeting GP decision-making scenarios, GPBench allows us to evaluate current mainstream LLMs. Expert assessment and evaluation reveal that in areas such as disease staging, complication recognition, treatment detail, and medication usage, these models exhibit at least ten major shortcomings. Overall, existing LLMs are not yet suitable for independent use in real-world GP working scenarios without human oversight.

Recent advancements in large language models and their multi-modal extensions have demonstrated the effectiveness of unifying generation and understanding through autoregressive next-token prediction. However, despite the critical role of 3D structural generation and understanding (3D GU) in AI for science, these tasks have largely evolved independently, with autoregressive methods remaining underexplored. To bridge this gap, we introduce Uni-3DAR, a unified framework that seamlessly integrates 3D GU tasks via autoregressive prediction. At its core, Uni-3DAR employs a novel hierarchical tokenization that compresses 3D space using an octree, leveraging the inherent sparsity of 3D structures. It then applies an additional tokenization for fine-grained structural details, capturing key attributes such as atom types and precise spatial coordinates in microscopic 3D structures. We further propose two optimizations to enhance efficiency and effectiveness. The first is a two-level subtree compression strategy, which reduces the octree token sequence by up to 8x. The second is a masked next-token prediction mechanism tailored for dynamically varying token positions, significantly boosting model performance. By combining these strategies, Uni-3DAR successfully unifies diverse 3D GU tasks within a single autoregressive framework. Extensive experiments across multiple microscopic 3D GU tasks, including molecules, proteins, polymers, and crystals, validate its effectiveness and versatility. Notably, Uni-3DAR surpasses previous state-of-the-art diffusion models by a substantial margin, achieving up to 256\% relative improvement while delivering inference speeds up to 21.8x faster. The code is publicly available at https://github.com/dptech-corp/Uni-3DAR.

Molecular pretrained representations (MPR) has emerged as a powerful approach for addressing the challenge of limited supervised data in applications such as drug discovery and material design. While early MPR methods relied on 1D sequences and 2D graphs, recent advancements have incorporated 3D conformational information to capture rich atomic interactions. However, these prior models treat molecules merely as discrete atom sets, overlooking the space surrounding them. We argue from a physical perspective that only modeling these discrete points is insufficient. We first present a simple yet insightful observation: naively adding randomly sampled virtual points beyond atoms can surprisingly enhance MPR performance. In light of this, we propose a principled framework that incorporates the entire 3D space spanned by molecules. We implement the framework via a novel Transformer-based architecture, dubbed SpaceFormer, with three key components: (1) grid-based space discretization; (2) grid sampling/merging; and (3) efficient 3D positional encoding. Extensive experiments show that SpaceFormer significantly outperforms previous 3D MPR models across various downstream tasks with limited data, validating the benefit of leveraging the additional 3D space beyond atoms in MPR models.

Alzheimer's disease, a neurodegenerative disorder, is associated with neural, genetic, and proteomic factors while affecting multiple cognitive and behavioral faculties. Traditional AD prediction largely focuses on univariate disease outcomes, such as disease stages and severity. Multimodal data encode broader disease information than a single modality and may, therefore, improve disease prediction; but they often contain missing values. Recent "deeper" machine learning approaches show promise in improving prediction accuracy, yet the biological relevance of these models needs to be further charted. Integrating missing data analysis, predictive modeling, multimodal data analysis, and explainable AI, we propose OPTIMUS, a predictive, modular, and explainable machine learning framework, to unveil the many-to-many predictive pathways between multimodal input data and multivariate disease outcomes amidst missing values. OPTIMUS first applies modality-specific imputation to uncover data from each modality while optimizing overall prediction accuracy. It then maps multimodal biomarkers to multivariate outcomes using machine-learning and extracts biomarkers respectively predictive of each outcome. Finally, OPTIMUS incorporates XAI to explain the identified multimodal biomarkers. Using data from 346 cognitively normal subjects, 608 persons with mild cognitive impairment, and 251 AD patients, OPTIMUS identifies neural and transcriptomic signatures that jointly but differentially predict multivariate outcomes related to executive function, language, memory, and visuospatial function. Our work demonstrates the potential of building a predictive and biologically explainable machine-learning framework to uncover multimodal biomarkers that capture disease profiles across varying cognitive landscapes. The results improve our understanding of the complex many-to-many pathways in AD.

Autism spectrum disorder(ASD) is a pervasive developmental disorder that significantly impacts the daily functioning and social participation of individuals. Despite the abundance of research focused on supporting the clinical diagnosis of ASD, there is still a lack of systematic and comprehensive exploration in the field of methods based on Large Language Models (LLMs), particularly regarding the real-world clinical diagnostic scenarios based on Autism Diagnostic Observation Schedule, Second Edition (ADOS-2). Therefore, we have proposed a framework called ADOS-Copilot, which strikes a balance between scoring and explanation and explored the factors that influence the performance of LLMs in this task. The experimental results indicate that our proposed framework is competitive with the diagnostic results of clinicians, with a minimum MAE of 0.4643, binary classification F1-score of 81.79\%, and ternary classification F1-score of 78.37\%. Furthermore, we have systematically elucidated the strengths and limitations of current LLMs in this task from the perspectives of ADOS-2, LLMs' capabilities, language, and model scale aiming to inspire and guide the future application of LLMs in a broader fields of mental health disorders. We hope for more research to be transferred into real clinical practice, opening a window of kindness to the world for eccentric children.

Recent breakthroughs in Large Language Models (LLMs) have revolutionized natural language understanding and generation, sparking significant interest in applying them to scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handling of multimodal data. In response to this gap, we introduce SciAssess, a benchmark specifically designed for the comprehensive evaluation of LLMs in scientific literature analysis. SciAssess aims to thoroughly assess the efficacy of LLMs by focusing on their capabilities in Memorization (L1), Comprehension (L2), and Analysis \& Reasoning (L3). It encompasses a variety of tasks drawn from diverse scientific fields, including fundamental science, alloy materials, biomedicine, drug discovery, and organic materials. To ensure the reliability of SciAssess, rigorous quality control measures have been implemented, ensuring accuracy, anonymization, and compliance with copyright standards. SciAssess evaluates 11 LLMs, including GPT, Claude, and Gemini, highlighting their strengths and areas for improvement. This evaluation supports the ongoing development of LLM applications in the analysis of scientific literature. SciAssess and its resources are available at \url{https://sci-assess.github.io/}.

In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientific knowledge has led to a massive increase in scholarly articles, making in-depth literature analysis increasingly challenging and time-consuming. The emergence of Large Language Models (LLMs) has offered a new way to address this challenge. Known for their strong abilities in summarizing texts, LLMs are seen as a potential tool to improve the analysis of scientific literature. However, existing LLMs have their own limits. Scientific literature often includes a wide range of multimodal elements, such as tables, charts, and molecule, which are hard for text-focused LLMs to understand and analyze. This issue points to the urgent need for new solutions that can fully understand and analyze multimodal content in scientific literature. To answer this demand, we present \textbf{Uni-SMART} (Universal Science Multimodal Analysis and Research Transformer), an innovative model designed for in-depth understanding of multimodal scientific literature. Through rigorous quantitative evaluation across several domains, Uni-SMART demonstrates superior performance over other text-focused LLMs. Furthermore, our exploration extends to practical applications, including patent infringement detection and nuanced analysis of charts. These applications not only highlight Uni-SMART's adaptability but also its potential to revolutionize how we interact with scientific literature.

Powder X-ray diffraction (PXRD) is a crucial means for crystal structure determination. Such determination often involves external database matching to find a structural analogue and Rietveld refinement to obtain finer structure. However, databases may be incomplete and Rietveld refinement often requires intensive trial-and-error efforts from trained experimentalists, which remains ineffective in practice. To settle these issues, we propose XtalNet, the first end-to-end deep learning-based framework capable of ab initio generation of crystal structures that accurately match given PXRD patterns. The model employs contrastive learning and Diffusion-based conditional generation to enable the simultaneous execution of two tasks: crystal structure retrieval based on PXRD patterns and conditional structure generations. To validate the effectiveness of XtalNet, we curate a much more challenging and practical dataset hMOF-100, XtalNet performs well on this dataset, reaching 96.3\% top-10 hit ratio on the database retrieval task and 95.0\% top-10 match rate on the ranked structure generation task.

Vision-language pre-training (VLP) on large-scale datasets has shown premier performance on various downstream tasks. In contrast to plenty of available benchmarks with English corpus, large-scale pre-training datasets and downstream datasets with Chinese corpus remain largely unexplored. In this work, we build a large-scale high-quality Chinese Cross-Modal Benchmark named CCMB for the research community, which contains the currently largest public pre-training dataset Zero and five human-annotated fine-tuning datasets for downstream tasks. Zero contains 250 million images paired with 750 million text descriptions, plus two of the five fine-tuning datasets are also currently the largest ones for Chinese cross-modal downstream tasks. Along with the CCMB, we also develop a VLP framework named R2D2, applying a pre-Ranking + Ranking strategy to learn powerful vision-language representations and a two-way distillation method (i.e., target-guided Distillation and feature-guided Distillation) to further enhance the learning capability. With the Zero and the R2D2 VLP framework, we achieve state-of-the-art performance on twelve downstream datasets from five broad categories of tasks including image-text retrieval, image-text matching, image caption, text-to-image generation, and zero-shot image classification. The datasets, models, and codes are available at https://github.com/yuxie11/R2D2

Single-step retrosynthesis is a crucial task in organic chemistry and drug design, requiring the identification of required reactants to synthesize a specific compound. with the advent of computer-aided synthesis planning, there is growing interest in using machine-learning techniques to facilitate the process. Existing template-free machine learning-based models typically utilize transformer structures and represent molecules as ID sequences. However, these methods often face challenges in fully leveraging the extensive topological information of the molecule and aligning atoms between the production and reactants, leading to results that are not as competitive as those of semi-template models. Our proposed method, Node-Aligned Graph-to-Graph (NAG2G), also serves as a transformer-based template-free model but utilizes 2D molecular graphs and 3D conformation information. Furthermore, our approach simplifies the incorporation of production-reactant atom mapping alignment by leveraging node alignment to determine a specific order for node generation and generating molecular graphs in an auto-regressive manner node-by-node. This method ensures that the node generation order coincides with the node order in the input graph, overcoming the difficulty of determining a specific node generation order in an auto-regressive manner. Our extensive benchmarking results demonstrate that the proposed NAG2G can outperform the previous state-of-the-art baselines in various metrics.