Nature Machine Intelligence, Published online: 10 September 2026; doi:10.1038/s42256-026-01298-6

Shi et al. demonstrate a dual architecture approach for materials research, integrating two lightweight large language models for collaborative reasoning and scientific tool execution. The method achieves competitive performance while remaining affordable and locally deployable.

Nature Machine Intelligence, Published online: 07 September 2026; doi:10.1038/s42256-026-01293-x

Kumaran et al. show that large language models making decisions on when to answer a question or abstain from answering can be influenced by boosting or suppressing confidence signals in the model.

Nature Machine Intelligence, Published online: 03 September 2026; doi:10.1038/s42256-026-01289-7

Wang et al. introduce a hardware-efficient quantum neural operator that overcomes classical linear capacity limits. Using an implicit quadratic frame, it offers accelerated expressivity for solving differential equations in the noisy intermediate-scale quantum era.

Nature Machine Intelligence, Published online: 03 September 2026; doi:10.1038/s42256-026-01295-9

RNA structure and function are hard to infer because annotations are scarce, despite abundant sequence data. Upadhyay et al. trained a self-supervised model on large-scale RNA data that derives biologically meaningful patterns from sequence correlations.

Nature Machine Intelligence, Published online: 01 September 2026; doi:10.1038/s42256-026-01302-z

Representational alignment can reveal similarities between human brain activity and language models. Work now demonstrates that it can also guide learning, improving the reliability of artificial reasoning.

Nature Machine Intelligence, Published online: 01 September 2026; doi:10.1038/s42256-026-01300-1

Lee and Lai study bias-like processing differences in large language reasoning models and find that, for most models, processing stereotypical information takes less computational effort than processing counter-stereotypical information.

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.