Zhang and colleagues engineered the poly(A) tail as a programmable regulatory element, showing that embedding cell-type-specific microRNA target sites directly within it confers robust, position-dependent silencing in off-target tissues while preserving activity in target cells. This strategy offers a new modular tool to enhance mRNA therapeutic safety and precision.

Zhang and colleagues reveal that GPX4 serves as a critical regulator of NRF2-mediated otoprotection against aminoglycoside-induced hair cell injury. Their findings identify a GPX4-dependent antioxidant mechanism that enables therapeutic activation of NRF2 and provides new insights into strategies for preventing drug-induced hearing loss.

Within the skin, glutamatergic neurons expressing neurofilament heavy chain (Nefh) play a role in aging. Loss of Nefh during aging drives skin fibroblast senescence and collagen loss, whereas glutamate supplementation improves skin aging phenotypes.

Nature, Published online: 16 March 2026; doi:10.1038/s41586-026-10372-3

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arXiv:2509.22989v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong persuasive capabilities comparable to those of humans, offering promising benefits while raising societal concerns. However, systematically evaluating the persuasive capabilities of LLMs is inherently challenging, as the effectiveness of persuasion among humans varies significantly across different domains. In this paper, we take a theory-driven approach to provide a scalable and principled framework for studying the persuasive capabilities of LLMs. Grounded in Bayesian persuasion theory, we repurpose human-human persuasion datasets to construct environments for evaluating and training LLMs as strategic persuaders. Our results reveal that frontier models can consistently achieve high persuasion gains and exhibit sophisticated persuasion strategies that align with theoretical characterizations. Building on this, we use reinforcement learning to train LLMs for strategic persuasion in our environments. Our results also demonstrate that even small LLMs can obtain significantly higher persuasion gains through reinforcement learning.

arXiv:2602.19313v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have seen rapid progress in pretraining, their advancement in Reinforcement Learning (RL) remains hampered by low sample efficiency and sparse rewards in real-world settings. Developing generalizable process reward models is essential for providing the fine-grained feedback necessary to bridge this gap, yet existing temporal value functions often fail to generalize beyond their training domains. We introduce TOPReward, a novel, probabilistically grounded temporal value function that leverages the latent world knowledge of pretrained video Vision-Language Models (VLMs) to estimate robotic task progress. Unlike prior methods that prompt VLMs to directly output progress values, which are prone to numerical misrepresentation, TOPReward extracts task progress directly from the VLM's internal token logits. In zero-shot evaluations across 130+ distinct real-world tasks and multiple robot platforms (e.g., Franka, YAM, SO-100/101), TOPReward achieves 0.947 mean Value-Order Correlation (VOC) on Qwen3-VL, dramatically outperforming the state-of-the-art GVL baseline which achieves near-zero correlation on the same open-source model. We further demonstrate that TOPReward serves as a versatile tool for downstream applications, including success detection and reward-aligned behavior cloning.

arXiv:2310.01770v5 Announce Type: replace-cross Abstract: Despite extensive study, the significance of sharpness -- the trace of the loss Hessian at local minima -- remains unclear. We investigate an alternative perspective: how sharpness relates to the geometric structure of neural representations, specifically representation compression, defined as how strongly neural activations concentrate under local input perturbations. We introduce three measures -- Local Volumetric Ratio (LVR), Maximum Local Sensitivity (MLS), and Local Dimensionality -- and derive upper bounds showing these are mathematically constrained by sharpness: flatter minima necessarily limit compression. We extend these bounds to reparametrization-invariant sharpness and introduce network-wide variants (NMLS, NVR) that provide tighter, more stable bounds than prior single-layer analyses. Empirically, we validate consistent positive correlations across feedforward, convolutional, and transformer architectures. Our results suggest that sharpness fundamentally quantifies representation compression, offering a principled resolution to contradictory findings on the sharpness-generalization relationship.