Frontier LLMs are trained to solve problems in a step-by-step way, generating reasoning text in human-understandable language. However, these reasoning tokens are not accurate explanations of LLMs’ internal computations, raising challenges for monitorability.

This work studies the reusability of CORNETO, a previously developed framework for learning biological networks from multi-omics data. Robust network inference is found to depend on data availability, prior knowledge, regularization and biological context.

Alonso-Monsalve et al. demonstrate that self-supervised pretraining helps deep learning models to interpret complex neutrino detector events, improving classification, reconstruction and data efficiency while enabling transfer across different neutrino detector technologies.

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.

Hsu et al. present EVENET, a foundation model pretrained on simulated particle collisions using both self-supervised learning and physics labels, which transfers across many analysis tasks and works on real collider data.

Li et al. introduce EdenGNN-Uni, a universal charge density model for the periodic table that is over an order of magnitude faster than some existing methods, facilitating efficient and accurate electronic structure calculations of materials.

Wan et al. introduce a physics-informed learning model that simultaneously predicts long-range particle paths and flow fields based on very limited trajectory measurements, validated in both simulations and blood-flow experiments.

This work studies the reusability of CORNETO, a previously developed framework for learning biological networks from multi-omics data. Robust network inference is found to depend on data availability, prior knowledge, regularization and biological context.

Li et al. introduce a neural-hierarchical skin that combines high-density iontronic sensor array, fast readout and data compression, enabling humanoid robots to perceive fine textures, manipulate delicate objects and respond to nociceptive stimuli.

Cao et al. present AMIFound, a foundation model that unifies medical image restoration across seven imaging modalities, delivering robust enhancement across diverse degradations and preserving clinically relevant information for diagnosis.

Frontier LLMs are trained to solve problems in a step-by-step way, generating reasoning text in human-understandable language. However, these reasoning tokens are not accurate explanations of LLMs’ internal computations, raising challenges for monitorability.

When newsroom artificial intelligence (AI) engages in emotional packaging, it changes how a story feels, shaping whether people approach, trust and share it. As these systems are designed and deployed, AI and behavioural researchers should work together to test whether emotional packaging helps accurate information reach readers, and whether it deepens division.

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.

When authors use generative AI in cognitive tasks, without spending time and effort to understand the output, a gap opens between what they present and what they can defend. This gap widens as further work is built on top, resulting in epistemic debt.

Frontier large language models (LLMs) have rapidly developed from helpful coding assistants to highly capable cybersecurity systems. After several security incidents in the past few months, more oversight and a strong focus on safe testing and deployment seem needed.

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.