Nature Machine Intelligence, Published online: 28 August 2026; doi:10.1038/s42256-026-01299-5
AI integration in Earth system models enhances prediction and modelling capabilities but also amplifies challenges for reproducibility. This Perspective introduces a framework for assessing reproducibility and provides practical ways to strengthen reproducibility in hybrid Earth system models.
Nature Machine Intelligence, Published online: 28 August 2026; doi:10.1038/s42256-026-01283-z
Although language models can be helpful in molecular design, they are not typically calibrated for uncertainty. Rankovic and colleagues present a method to train language models while taking into account the uncertainty of the data.Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01294-w
When authors use generative AI in cognitive tasks, without spending time and effort to understand the output, a gap opens between what they present and what they can defend. This gap widens as further work is built on top, resulting in epistemic debt.Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01296-8
Lee, Oh et al. propose interoception as a biologically inspired framework for building more autonomous and adaptive AI agents, learning from living organisms to build autonomous and adaptive intelligence.Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01286-w
Feng et al. introduce MAP, an artificial intelligence framework that integrates biological mechanism knowledge to predict how cells respond to chemical perturbation, improving generalization to untested drugs and prioritizing cancer drug candidates in virtual screening.Nature Machine Intelligence, Published online: 24 August 2026; doi:10.1038/s42256-026-01290-0
Implicit neural representations can turn massive scientific measurements into compact functions. A hierarchical approach now helps these representations preserve richer fine-scale detail.Nature Machine Intelligence, Published online: 24 August 2026; doi:10.1038/s42256-026-01287-9
Ni et al. present WIEN-INR, an implicit neural representation for scientific data compression. It operates in the multiscale wavelet domain to improve compression as well as preserve fine details and signal fidelity.Nature Machine Intelligence, Published online: 24 August 2026; doi:10.1038/s42256-026-01297-7
Shepherd et al. propose end-to-end AI control of lower-limb exoskeletons based on real-time estimates of physiological signals.Nature Machine Intelligence, Published online: 19 August 2026; doi:10.1038/s42256-026-01291-z
Chen, Wang, Li et al. introduce VITAL, a dual-channel deep learning framework that co-learns sequence and structural contexts to quantitatively predict peptide–protein interactions, map binding interfaces and estimate affinity.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 study
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