Companies, tech workers and researchers are in a frenzy to embed agentic AI into their workflows, locked in a self-imposed race not to fall behind. There must be a better way to make use of AI technology.

Del Dottore et al. present an octopus-inspired soft robotic arm that uses optoelectronic mechanosensors in its suction cups to detect contact forces and infer object positions.

Thiemann et al. introduce TrajCast, a neural network that bypasses force calculations to directly predict atomic trajectories, enabling time steps up to 30 times longer while accurately reproducing physical properties of molecules and materials.

Torres et al. present ApexGO, a generative approach capable of redesigning peptide antibiotics to better kill drug-resistant bacteria. They validated candidates in laboratory tests and mouse infections and matching or outperforming standard antibiotics.

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.

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.

Han et al. introduce UniAIR, a unified AI framework that predicts mutation effects across diverse immune recognition settings. The approach enables more generalizable modelling of antibody, antigen and T cell receptor interactions.

pUniFind is a large-scale deep learning model for proteomics that unifies peptide–spectrum scoring and open de novo sequencing. Trained on over 100 million spectra, it substantially improves peptide identification and modification discovery.

Long et al. introduce a neural operator method to solve free boundary problems with high precision. The framework shows promise for real-time predictions in clinical applications, particularly in simulating tumour growth.

SpecGP enhances fragment ion coverage to enable the prediction of N-glycopeptide structural spectra across diverse collision energies, thereby improving isomer discrimination and boosting identification confidence through rescoring.

Generative artificial intelligence (GenAI) tools are challenging our understanding of plagiarism. How should we deal with plagiarism of ideas if this misbehaviour is increasingly common, and it is extremely difficult to prove when GenAI is involved? Definitions of research misconduct that specifically address the use of GenAI tools are needed.

Companies, tech workers and researchers are in a frenzy to embed agentic AI into their workflows, locked in a self-imposed race not to fall behind. There must be a better way to make use of AI technology.

Several frameworks from different disciplines are converging on the scientific question of what it takes for a system to not just predict, simulate or reason about the world, but to act physically and intelligently within it.