npj Artificial Intelligence

Abstract We introduce a sustainable neuromorphic approach for numerical simulations in Engineering Mechanics. The finite element method (FEM) is widely used in engineering design; however, worldwide, there is no neuromorphic technology available in mechanics, even though the need for computational capacity with complex mechanical models is increasing 1 . AI-enhanced engineering approaches, such a…

Abstract Estimating causal effects is vital for decision making. In standard causal effect estimation, treatments are usually binary- or continuous-valued. However, in many important real-world settings, treatments can be structured, high-dimensional objects, such as text, video, or audio. This provides a challenge to traditional causal effect estimation. While leveraging the shared structure acr…

Abstract Data assimilation (DA) combines observations with numerical models to estimate evolving Earth system states for forecasting and monitoring. Machine learning (ML) enables surrogate modeling, pattern recognition and Bayesian inference. These fields are converging: ML accelerates DA, while DA provides uncertainty quantification and physical constraints. Hybrid DA-ML systems are promising, y…

Fake news detection has garnered the attention of an increasing number of researchers in recent years, particularly in the context of multimodal fake news that combines text and images. However, existing methods only focus on cross-modal feature fusion guided by a consistency matrix and make predictions based on shallow semantic modeling. This type of method relies on the invisible interaction of…

Abstract Linear error-correcting codes form the mathematical backbone of modern digital communication and storage systems, but identifying champion linear codes (linear codes achieving or exceeding the best known minimum Hamming distance) remains challenging. By training a transformer to predict the minimum Hamming distance of a class of linear codes and pairing it with a genetic algorithm over t…

Abstract Cognitive agents, powered by Large Language Models (LLMs), possess advanced reasoning and communication capabilities that fundamentally distinguish them from non-cognitive particles, which rely solely on formal rules. While their ability to replicate human individual and social behaviors is still under scrutiny, the impact of their embedded “intelligence” on emergent behaviors remains po…

Abstract Time series forecasting plays a vital role across scientific, industrial, and environmental domains, especially when dealing with high-dimensional and nonlinear systems. While Transformer models have recently achieved state-of-the-art performance in long-range forecasting, they often suffer from interpretability issues and instability in the presence of noise or dynamical uncertainty. We…

Although research in metaheuristics has become increasingly automated, considerable effort is still devoted to identifying new sources of inspiration for the manual design of so-called metaphor-based algorithms. In this work, we critically examine the use of metaphor-based design and its ongoing influence on the field of metaheuristics. We argue that manually designing algorithms based on metapho…

Human motion intent prediction (HMIP) combines data sources, feature extraction, modeling and prediction algorithms, etc., which is the key for providing artificial intelligent assistance and protection from intelligent wearable systems. HMIP has been intensively explored. A timely and comprehensive overview of this field is provided. Several main aspects are covered: biomechanical features of hu…

Residual-based adaptive strategies are widely used in scientific machine learning yet remain largely heuristic. We introduce a variational framework that formalizes these methods through convex transformations of the residual, where different transformations correspond to distinct objective functionals. For instance, exponential weights target uniform error minimization, while linear weights reco…

With the rapid advancement of large language model (LLM) technologies, AI agents have rapidly emerged in healthcare. This review traces the historical evolution and core characteristics of AI agents, and systematically examines their applications in assisted diagnosis, clinical decision support, medical report generation, patient-facing chatbots, healthcare system management, and medical educatio…

Traditional machine learning has advanced polymer discovery, yet direct generation of chemically valid and synthesizable polymers without exhaustive enumeration remains a challenge. Here we present POLYT5, an encoder-decoder chemical language model based on the T5 architecture, trained to understand and generate polymer structures. POLYT5 enables both property prediction and the targeted generati…

Abstract This paper aims to study a self-supervised 3D clothing reconstruction method, which recovers the geometric shape and texture of human clothing from a single image. Compared with existing methods, we observe that three primary challenges remain: (1) 3D ground-truth meshes of clothing are usually inaccessible due to annotation difficulties and time costs; (2) Conventional template-based me…

Comprehensive characterization of the tumor microenvironment (TME) is essential for understanding cancer progression and developing effective, patient-specific therapies. Spatial context of the TME is particularly important, and exists across multiple scales-from the molecular to cellular to tissue levels. However, current methods are modality-specific and lack flexibility in effectively modeling…

AI language generators are now ubiquitous but typically produce generic text that fails to reflect individual differences. Here, we introduce PsychAdapter, a lightweight LLM architectural modification that uses empirically derived links between language and personality, demographic, and mental health traits to generate trait-reflective text, regardless of prompt. PsychAdapter was applied to GPT-2…

In this study, we present the AIM Review Tool, a modern web-based application that integrates active and supervised machine learning to accelerate the screening of publications for systematic reviews. AIM Review combines advanced text vectorization methods with machine learning models executed directly in the web browser, enabling rapid and privacy-preserving analysis. Unlike existing tools, AIM …

With EDs increasingly overburdened, Large Language Models (LLMs) may help streamline workflow and decision-making. We evaluated their emergency medicine knowledge and performance in simulated ED tasks. This two-part study first tested factual knowledge of 18 LLMs using a curated MedMCQA subset covering 12 ED chief complaints, assessing accuracy, precision, and recall. Five models (GPT-5, GPT-4, C…

Amid the heated debate on whether artificial intelligence possesses a human-like capacity for understanding, the compatibility and interaction between human and algorithmic visual attention remain unclear. Here, we address this issue through the lens of spatial and feature-based attention. Using autonomous driving as an epitome of safety-critical domains, we show that human attention in driving t…

Advancing evidence-based medicine requires integrating clinical expertise with data analysis. While clinicians contribute essential domain knowledge, applying modern data science methods often requires specialized training, creating a barrier to adoption. To bridge this gap, we developed ChatDA, an artificial intelligence agent enabling large language model-mediated conversational analysis of de-…