arXiv:2603.28925v1 Announce Type: cross Abstract: Safety fine-tuning in Large Language Models (LLMs) seeks to suppress potentially harmful forms of mind-attribution such as models asserting their own consciousness or claiming to experience emotions. We investigate whether suppressing mind-attribution tendencies degrades intimately related socio-cognitive abilities such as Theory of Mind (ToM). Through safety ablation and mechanistic analyses of representational similarity, we demonstrate that LLM attributions of mind to themselves and to technological artefacts are behaviorally and mechanistically dissociable from ToM capabilities. Nevertheless, safety fine-tuned models under-attribute mind to non-human animals relative to human baselines and are less likely to exhibit spiritual belief, suppressing widely shared perspectives regarding the distribution and nature of non-human minds.

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10203-5

A study of reproducibility in a stratified random sample of 600 papers published from 2009 to 2018 in 62 journals spanning the social and behavioural sciences finds higher reproducibility among more recent papers and papers from journals that require data sharing.

Nature, Published online: 30 March 2026; doi:10.1038/d41586-026-00973-3

Anne Poelina gives first authorship to a source with deep knowledge about water — the river itself.

Nature Biotechnology, Published online: 27 March 2026; doi:10.1038/s41587-026-03046-y

Variant mapping of sorghum promoters identifies CRISPR-accessible mutations that upregulate gene expression.