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.Nature Machine Intelligence, Published online: 24 July 2026; doi:10.1038/s42256-026-01288-8
Training machine learning models on high-quality biological datasets can quickly produce abundant results. But as datasets often evolve over time, further work is required to update models and maintain alignment between models and datasets.Nature Machine Intelligence, Published online: 24 July 2026; doi:10.1038/s42256-026-01268-y
A controlled study of large language model agents across 260 configurations shows when multi-agent collaboration helps or hurts performance, and introduces a predictive model that selects the best architecture in 87% of held-out within-domain configurations.Nature Machine Intelligence, Published online: 21 July 2026; doi:10.1038/s42256-026-01254-4
Lin et al. introduce a brain-inspired generative model that provides two key features of intelligence: planning and problem-solving. It uses cognitive maps, stochastic computing and compositional coding, and requires only local synaptic plasticity.Nature Machine Intelligence, Published online: 20 July 2026; doi:10.1038/s42256-026-01274-0
Li et al. show that recurrent neural networks optimized for free recall discover diverse, human-like memory strategies beyond classical temporal context models, with top models using an index-based mechanism resembling the memory palace technique.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.
Login