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.Nature Machine Intelligence, Published online: 10 June 2026; doi:10.1038/s42256-026-01257-1
Based on independent reproduction and external benchmarks, this study shows that RXNGraphormer is reproducible and transferable across reaction prediction tasks, while revealing limitations under heterogeneous and distribution-shifted settings.Nature Machine Intelligence, Published online: 09 June 2026; doi:10.1038/s42256-026-01251-7
Machine learning methods for drug discovery often face difficulties in identifying novel bioactive molecules that do not belong to the training data distribution. A new metric can now quantify chemical distribution shift and evaluate the generalization capability of molecular machine learning models.Nature Machine Intelligence, Published online: 04 June 2026; doi:10.1038/s42256-026-01249-1
Yang et al. developed CrossDNA, a parameter-efficient language model for cross-strand DNA modelling inspired by double-strand dynamics. It performs strongly in benchmarks and supports regulatory region interpretation and non-coding variant prioritization.Nature Machine Intelligence, Published online: 01 June 2026; doi:10.1038/s42256-026-01242-8
Driessen et al. present a conditional optimal transport method that can model the distribution shift between perturbed and unperturbed cell transcriptomes and that can generalize to unseen contexts.Nature Machine Intelligence, Published online: 01 June 2026; doi:10.1038/s42256-026-01245-5
Yuan et al. develop UniCM, a deep learning framework that models the coupled dynamics of global climate modes as an interconnected system, extending skilful forecasts for multiple climate modes and revealing cross-basin interactions.
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