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.Nature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01271-3
Realigning AI technology towards the Sustainable Development GoalsNature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01259-z
Kindt et al. present a unifying framework for superposition in neural networks. Their three-step approach clarifies how latent features can be identified, disentangled and assessed.Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01273-1
The brain’s architecture exhibits diversity across many temporal and spatial scales, yet our computing architectures remain largely homogeneous. Low-powered neuromorphic hardware offers a path towards energy-efficient AI, but could these approaches be improved with heterogeneous computing architectures?Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01272-2
Sartore et al. present ergoCub, a humanoid robot that prioritizes human safety at hardware and motion levels. Using a shared embodied intelligence framework, design and control are jointly optimized with human-related metrics such as back stress alongside locomotion objectives, reducing spinal load and improving walking robustness.Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01260-6
Song et al. propose Sustainability Robotics as a new discipline to overcome fragmentation and enhance societal and environmental impact. They define three guiding principles, alongside two dimensions spanning sustainable design and robotics for sustainability.Nature Machine Intelligence, Published online: 10 July 2026; doi:10.1038/s42256-026-01270-4
The push to make biological data ‘AI-ready’ is accelerating worldwide. But to understand what AI-ready means for biological data, there are several questions to answer.Nature Machine Intelligence, Published online: 06 July 2026; doi:10.1038/s42256-026-01262-4
Park and Walsh introduce a reinforcement learning framework that could accelerate the discovery of new, thermodynamically stable and diverse crystalline materials with desired properties.Nature Machine Intelligence, Published online: 03 July 2026; doi:10.1038/s42256-026-01267-z
Berner et al. show how to adapt popular neural networks into discretization-agnostic neural operators that learn from continuous scientific data, enabling scientific simulations that generalize more reliably across resolutions.Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01266-0
Wang et al. develop DeepEvidence, a biomedical deep research agent for exploring and synthesizing evidence across various knowledge sources to support drug discovery, clinical trials and evidence-based medicine.Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01264-2
Liu and colleagues introduce HelixFold-S1, a guided sampling strategy for biomolecular complex structure prediction that targets high-probability interaction regions. The method achieves higher accuracy than traditional unguided methods while reducing computational costs.Nature Machine Intelligence, Published online: 01 July 2026; doi:10.1038/s42256-026-01261-5
Chen et al. demonstrate an AI X-ray scientist that autonomously aligns single crystals at a real synchrotron beamline, showing how large language models can enable adaptive closed-loop experimentation at large-scale scientific facilities.Nature Machine Intelligence, Published online: 25 June 2026; doi:10.1038/s42256-026-01258-0
Rising pathogen drug resistance makes next-generation antimicrobial peptides a global priority. Generative AI accelerates discovery by rapidly proposing new peptides with high therapeutic potential. The key question is no longer whether broad data-driven exploration is possible, but whether it can refine biologically complex activity scaffolds.Nature Machine Intelligence, Published online: 23 June 2026; doi:10.1038/s42256-026-01269-x
Recent breakthroughs in mathematical research show that AI is transforming the field at a remarkable pace. In an open letter published this month, an international group of mathematicians argue that the field needs to remain a human endeavour.Nature Machine Intelligence, Published online: 23 June 2026; doi:10.1038/s42256-026-01263-3
Nassour, Berberich and colleagues present a soft robotic hand exoskeleton that restores grasping ability in individuals with severe hand paralysis, enabling meaningful tasks such as feeding. A lightweight textile glove with wrist dorsiflexion and an active opposable thumb increases hand articulations to enable more dexterous grasping.Nature Machine Intelligence, Published online: 22 June 2026; doi:10.1038/s42256-026-01252-6
An, Luo, Zhang and colleagues present Turbo, a transformer-based reinforcement learning framework that enables simulation-to-real transfer for autonomous navigation and obstacle avoidance in physical microrobotic swarms operating in unknown environments.Nature Machine Intelligence, Published online: 16 June 2026; doi:10.1038/s42256-026-01255-3
Pengfei Sun et al. develop a spiking neural network with a dual memory pathway, co-designed with a custom neuromorphic chip. The approach delivers over 4× throughput and 5x energy efficiency gains while using 40–60% fewer parameters than state-of-the-art implementations.Nature Machine Intelligence, Published online: 12 June 2026; doi:10.1038/s42256-026-01256-2
Psychiatric disorders are heterogeneous, and care depends on interpreting unstructured longitudinal narratives, creating variability that hinders standardization. A study now shows that a psychiatry-specific large language model (LLM) may help clinicians to deliver more consistent, high-quality care.Nature Machine Intelligence, Published online: 11 June 2026; doi:10.1038/s42256-026-01244-6
Synthetic datasets are becoming crucial for the development of biomedical machine learning models. Victoriano et al. discuss the persistent simulation-to-reality gap that limits how well synthetic performance predicts real-world performance.Nature Machine Intelligence, Published online: 11 June 2026; doi:10.1038/s42256-026-01250-8
Xiong et al. introduce ConfSeq, a molecular conformation description language that enables language models to perform three-dimensional molecular modelling tasks, including conformer prediction, three-dimensional molecular generation and representation, with strong performance.
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