Nature Machine Intelligence

Cybernetics, interoception, and the art of embodiment

2026-09-18

Living organisms continuously monitor their internal physiological states while interacting with complex environments and adapt their actions in response to changes in these internal signals. New work combines such biological principles with cybernetics, reinforcement learning and neuroscience to develop a framework for autonomous and adaptive artificial embodied agents.

Publisher Correction: Quantum neural operators with implicit quadratic frame and expressivity advantages

2026-09-14

Ruocheng Wang, Xiaoqiu Zhong, Zhuo Xia, Junchi Yan

A collaborative agent with two lightweight synergistic models for autonomous crystal materials research

2026-09-10

Tongyu Shi, Yutang Li, Zhanyuan Li, Qian Liu, Jie Zhou, Wenhe Xu, Yang Li, Dawei Dai, Rui He, Wenhua Zhou, Jiahong Wang, Xue-Feng Yu

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.

Causal evidence that language models use confidence to drive behaviour

2026-09-07

Dharshan Kumaran, Nathaniel Daw, Simon Osindero, Petar Veličković, Viorica Patraucean

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.

Quantum neural operators with implicit quadratic frame and expressivity advantages

2026-09-03

Ruocheng Wang, Xiaoqiu Zhong, Zhuo Xia, Junchi Yan

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.

NucleicBERT interprets RNA sequence space through self-supervised language modelling

2026-09-03

Utkarsh Upadhyay, Julian Herold, Markus Götz, Alexander Schug

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.

Implicit-bias-like patterns in reasoning models

2026-09-01

Messi H. J. Lee, Calvin K. Lai

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.

Steering machine reasoning with brain signals

2026-09-01

Changde Du, Huiguang He

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.