Nature Machine Intelligence, Published online: 18 August 2026; doi:10.1038/s42256-026-01301-0
Frontier large language models (LLMs) have rapidly developed from helpful coding assistants to highly capable cybersecurity systems. After several security incidents in the past few months, more oversight and a strong focus on safe testing and deployment seem needed.
Nature Machine Intelligence, Published online: 18 August 2026; doi:10.1038/s42256-026-01285-x
Pan et al. present CoxRTL, a recalibrated transfer learning strategy that leverages external cohorts to improve survival prediction under covariate shift when target training data are limited and deployment outcomes are unavailable.Nature Machine Intelligence, Published online: 17 August 2026; doi:10.1038/s42256-026-01279-9
Cheston et al. develop a machine learning pipeline that identifies 20 iconic jazz pianists from audio recordings with up to 94% accuracy, revealing how melody, harmony, rhythm and dynamics shape each performer’s individual musical fingerprint.Nature Machine Intelligence, Published online: 14 August 2026; doi:10.1038/s42256-026-01276-y
Chen et al. explore limitations of current knowledge editing techniques in large language models and propose three promising research directions that respect the complexity of knowledge representation in a real-world setting.Nature Machine Intelligence, Published online: 14 August 2026; doi:10.1038/s42256-026-01281-1
Wang et al. summarize advances in machine listening and speaking, speech-based interaction, and audio–visual understanding.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 cancerNature 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 studyNature 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 classificationNature 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 ChinaNature 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 mismatchNature 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 studyNature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s41598-026-66068-1
Multi-level violence recognition via hybrid convolutional-attention and recurrent architecturesNature Machine Intelligence, Published online: 12 August 2026; doi:10.1038/s41598-026-63846-9
Correction: A five-dimensional classical framework for gravitational and quantum phenomenaNature 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.
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