A few years ago, we introduced an article format called Reusability Reports to highlight good practices in code sharing and reporting. A renewed focus on reproducibility and transparency in code reporting seems warranted, as research output has accelerated with the widespread adoption of large language models.

Madduri et al. introduce a computational framework grounded in control and game theory to model co-adaptation between users and decoders in neural interfaces. This framework enables a principled design of closed-loop systems that improve usability and personalization.

Wang et al. introduce Mac-Diff, a conditional diffusion model with locality-aware attention across modalities, to generate diverse conformational ensembles for unseen proteins, capturing both dynamic flexibility and structural heterogeneity.

Alsulami et al. present PrePR-CT, a computational approach that predicts how different cell types respond to drug-like compounds using limited data. By integrating biological networks with machine learning, it improves accuracy, interpretability and efficiency in early drug discovery.

Cheon and Paik show that overconfidence in deep neural networks arises from standard initialization practices, and that brief warm-up training with random noise improves uncertainty calibration and meta-cognitive recognition of unknown inputs.

Wang et al. introduce TDFold, which reformulates 3D protein structure prediction as a 2D image-like diffusion task. Its geometric template diffusion framework offers greater accuracy, speed and efficiency than leading models.

Marwitz et al. demonstrate the use of large language models to build semantic concept graphs from materials science abstracts and train a machine learning model to predict emerging topic combinations from historical data. They show that the model enables experts to find suggestions that can inspire new research.

Frank et al. introduce Euclidean fast attention, a linear-scaling framework for 3D data. By leveraging Euclidean rotary encodings, the method overcomes the quadratic cost of standard attention to accurately capture long-range effects in physical systems.

Muzellec and Kar use reverse predictivity to show that only a subset of artificial neural network (ANN) units align with primate brain responses. This reveals a substantial misalignment between ANNs and brains compared with the strong bidirectional alignment observed between two primate brains.

Madduri et al. introduce a computational framework grounded in control and game theory to model co-adaptation between users and decoders in neural interfaces. This framework enables a principled design of closed-loop systems that improve usability and personalization.

A few years ago, we introduced an article format called Reusability Reports to highlight good practices in code sharing and reporting. A renewed focus on reproducibility and transparency in code reporting seems warranted, as research output has accelerated with the widespread adoption of large language models.

We introduce a framework to analyse interpretability in deep learning, by drawing on a formal notion of model semantics from the philosophy of science. We argue that interpretability is only one aspect of a model’s semantics and illustrate our framework with examples from biomedicine.

Large language models (LLMs) include not only social stereotypes but also cognitive biases. As researchers work to identify, characterize and rectify these biases, we encourage the scientific community to recognize that, although often seen as errors, cognitive biases can also reflect functional, context-specific adaptations in reasoning.

Almost 10 years ago, AlphaGo defeated one of the world’s best professional players in the complex, ancient game of Go. It was a pivotal moment that spawned new research directions and marked the beginning of a busy decade in AI development.