Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s41598-026-62801-y

Model-free multiparametric analysis of SHG images reveals collagen signatures in breast cancer

Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s41598-026-66901-7

Development of samarium-153 oxide loaded polystyrene radiotracer particles for gamma scintigraphy of whole gastrointestinal transit study

Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s41598-026-64954-2

AlzheiNN: a convolutional neural network-based model for Alzheimer’s disease classification

Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1057/s41599-026-08691-x

Service trade liberalization and manufacturing value chain upgrading: a quasi-natural experiment from China

Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1057/s41599-026-08677-9

The impact of CEOs’ multidisciplinary backgrounds on corporate financing and investment maturity mismatch

Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s41598-026-66659-y

Experiences of kinesiophobia in patients with chronic obstructive pulmonary disease: a qualitative phenomenological study

Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s41598-026-66068-1

Multi-level violence recognition via hybrid convolutional-attention and recurrent architectures

Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s41598-026-63846-9

Correction: A five-dimensional classical framework for gravitational and quantum phenomena

Nature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s42256-026-01292-y

Wang et al. design efficient robot–environment interaction representations that achieve generalization across articulated robotic hand models and task adaptability in diverse cluttered grasping scenarios, suggesting a path towards general-purpose robotics.

Nature Machine Intelligence, Published online: 10 August 2026; doi:10.1038/s42256-026-01280-2

Johnson et al. demonstrate an autonomous three-bar tensegrity robot capable of robust locomotion across varied terrains even after extreme impacts, including a 5.7-m drop onto asphalt.

Nature Machine Intelligence, Published online: 05 August 2026; doi:10.1038/s42256-026-01275-z

A new deep learning method enables molecular dynamics simulations over longer time scales while still achieving accurate physical property prediction.

Nature Machine Intelligence, Published online: 03 August 2026; doi:10.1038/s42256-026-01282-0

Generative machine learning methods have led to progress in crystal discovery, but cannot fully explore the space of material candidates that are both novel and useful. A reinforcement learning-based method steers candidate generation to these areas, enabling the design of novel functional materials.

Nature Machine Intelligence, Published online: 03 August 2026; doi:10.1038/s42256-026-01278-w

Xiao et al. show that large language models partially align with human brain activity during deductive reasoning. They further show that brain signals can directly guide and improve model performance, with transfer across reasoning types.

Nature Machine Intelligence, Published online: 30 July 2026; doi:10.1038/s42256-026-01284-y

Gao et al. introduce a quantum data augmentation method to enable neural networks to classify multipartite entanglement structures in infinite-dimensional systems, substantially improving accuracy and reducing the data acquisition costs that typically limit training.

Nature Machine Intelligence, Published online: 30 July 2026; doi:10.1038/s42256-026-01277-x

Lai et al. extend and evaluate a unified framework for liquid electrolyte design, showing how data size and composition affect robustness, and demonstrating improved cross-system transferability and multiscale performance over baselines.

Nature Machine Intelligence, Published online: 24 July 2026; doi:10.1038/s42256-026-01288-8

Training machine learning models on high-quality biological datasets can quickly produce abundant results. But as datasets often evolve over time, further work is required to update models and maintain alignment between models and datasets.

Nature Machine Intelligence, Published online: 24 July 2026; doi:10.1038/s42256-026-01268-y

A controlled study of large language model agents across 260 configurations shows when multi-agent collaboration helps or hurts performance, and introduces a predictive model that selects the best architecture in 87% of held-out within-domain configurations.

Nature Machine Intelligence, Published online: 21 July 2026; doi:10.1038/s42256-026-01254-4

Lin et al. introduce a brain-inspired generative model that provides two key features of intelligence: planning and problem-solving. It uses cognitive maps, stochastic computing and compositional coding, and requires only local synaptic plasticity.

Nature Machine Intelligence, Published online: 20 July 2026; doi:10.1038/s42256-026-01274-0

Li et al. show that recurrent neural networks optimized for free recall discover diverse, human-like memory strategies beyond classical temporal context models, with top models using an index-based mechanism resembling the memory palace technique.

Nature Machine Intelligence, Published online: 15 July 2026; doi:10.1038/s42256-026-01265-1

Moving beyond brute-force simulations, local neural operators—combined with equation-free methods and Krylov subspace techniques—enable system-level stability and bifurcation analysis of complex spatiotemporal systems directly from data.