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Received — 10 September 2026 ⏭

Author Correction: A mouse brain stereotaxic topographic atlas with isotropic 1-μm resolution

✇Nature - Issue - nature.com science feeds
By: Zhao Feng · Xiangning Li · Yue Luo · Xin Liu · Ben Long · Tao Jiang · Xueyan Jia · Xiaowei Chen · Jie Luo · Xiaokang Chai · Zhen Wang · Miao Ren · Xin Lu · Gang Yao · Mengting Zhao · Yuxin Li · Zhixiang Liu · Hong Ni · Chuhao Dou · Shengda Bao · Shicheng Yang · Zoutao Zhang · Jiandong Zhou · Lingyi Cai · Qi Zhang · Ayizuohere Tudi · Chaozhen Tan · Zhengchao Xu · Siqi Chen · Wenxiang Ding · Wenjuan Shi · Anan Li · Hong-wei Dong · Hui Gong · Qingming Luo
7 September 2026 at 08:00

Nature, Published online: 07 September 2026; doi:10.1038/s41586-026-11094-2

Author Correction: A mouse brain stereotaxic topographic atlas with isotropic 1-μm resolution
Received — 13 April 2026 ⏭

Deoxynivalenol drives liver injury progression by dysregulating core molecular networks: integrated multi-omics, network toxicology and molecular docking analysis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yunfeng Fu · Sicheng Yang · Yating Pan · Runwei Yan · Fan Du · Xiaodong Zhou
12 April 2026 at 18:00

Environ Int. 2026 Apr 8;210:110249. doi: 10.1016/j.envint.2026.110249. Online ahead of print.

ABSTRACT

BACKGROUND: Deoxynivalenol (DON), a prevalent food-borne mycotoxin, increasingly recognized as a potent driver in the progression of chronic liver disease to cirrhosis and hepatocellular carcinoma (HCC); however, its systematic role is unclear. This study aims to decode the pathogenic networks of DON through an integrated multi-omics and toxicological framework.

METHODS: We integrated transcriptomic datasets from public repositories (GSE139602 and GSE25097) and single-cell RNA-seq data (GSE136103 and GSE149614) with toxicogenomics data. Analytical approaches included differential expression analysis, protein-protein interaction networks, profiling, single-cell trajectory analysis, trend testing, and machine learning modeling, and molecular docking. Key findings were validated through in vitro assays in human hepatocytes (THLE-2), as well as in vivo mouse models.

RESULTS: Five core hub genes (FAT1, CCND1, FOS, GADD45G, and PHLDA1) were identified as consistent drivers of DON-induced liver injury progression. Longitudinal analysis revealed that FAT1 and CCND1 underwent progressive upregulation, while GADD45G, and PHLDA1 were significantly suppressed across disease stages. Molecular docking and Cellular Thermal Shift Assays (CETSA) provided physical evidence of direct binding between DON and these hub proteins. Furthermore, prolonged DON exposure induced significant G2/M phase arrest in hepatocytes, consistent with the sustained dysregulation of the GADD45G/CCND1 axis. In vivo results corroborated that DON triggers noticeable hepatic structural damage and inflammatory infiltration, synchronized with hub protein dysregulation.

CONCLUSION: Chronic DON exposure drives liver disease progression by dysregulating core molecular networks and direct interaction with key hub proteins. Our integrated approach provides novel mechanistic insights and highlights potential biomarkers for DON-induced hepatotoxicity.

PMID:41967175 | DOI:10.1016/j.envint.2026.110249

Deoxynivalenol drives liver injury progression by dysregulating core molecular networks: integrated multi-omics, network toxicology and molecular docking analysis

✇Omics in Hepatocellular
By: Yunfeng Fu · Sicheng Yang · Yating Pan · Runwei Yan · Fan Du · Xiaodong Zhou
12 April 2026 at 18:00

Environ Int. 2026 Apr 8;210:110249. doi: 10.1016/j.envint.2026.110249. Online ahead of print.

ABSTRACT

BACKGROUND: Deoxynivalenol (DON), a prevalent food-borne mycotoxin, increasingly recognized as a potent driver in the progression of chronic liver disease to cirrhosis and hepatocellular carcinoma (HCC); however, its systematic role is unclear. This study aims to decode the pathogenic networks of DON through an integrated multi-omics and toxicological framework.

METHODS: We integrated transcriptomic datasets from public repositories (GSE139602 and GSE25097) and single-cell RNA-seq data (GSE136103 and GSE149614) with toxicogenomics data. Analytical approaches included differential expression analysis, protein-protein interaction networks, profiling, single-cell trajectory analysis, trend testing, and machine learning modeling, and molecular docking. Key findings were validated through in vitro assays in human hepatocytes (THLE-2), as well as in vivo mouse models.

RESULTS: Five core hub genes (FAT1, CCND1, FOS, GADD45G, and PHLDA1) were identified as consistent drivers of DON-induced liver injury progression. Longitudinal analysis revealed that FAT1 and CCND1 underwent progressive upregulation, while GADD45G, and PHLDA1 were significantly suppressed across disease stages. Molecular docking and Cellular Thermal Shift Assays (CETSA) provided physical evidence of direct binding between DON and these hub proteins. Furthermore, prolonged DON exposure induced significant G2/M phase arrest in hepatocytes, consistent with the sustained dysregulation of the GADD45G/CCND1 axis. In vivo results corroborated that DON triggers noticeable hepatic structural damage and inflammatory infiltration, synchronized with hub protein dysregulation.

CONCLUSION: Chronic DON exposure drives liver disease progression by dysregulating core molecular networks and direct interaction with key hub proteins. Our integrated approach provides novel mechanistic insights and highlights potential biomarkers for DON-induced hepatotoxicity.

PMID:41967175 | DOI:10.1016/j.envint.2026.110249

Satellite imagery reveals increasing volatility in human night-time activity

✇Nature - Issue - nature.com science feeds
By: Tian Li · Zhuosen Wang · Christopher C. M. Kyba · Miguel O. Román · Karen C. Seto · Yun Yang · Shi Qiu · Theres Kuester · Michail Fragkias · Xiang Chen · Thomas H. Meyer · Chadwick D. Rittenhouse · Xiaonan Tai · Mari Cullerton · Falu Hong · Ashley Grinstead · Kexin Song · Ji Won Suh · Xiucheng Yang · Virginia L. Kalb · Chengbin Deng · Zhe Zhu
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10260-w

Daily satellite data reveal that Earth’s artificial lights at night are highly volatile, with frequent brightening and dimming between 2014 and 2022.
Received — 8 April 2026 ⏭

One Model for All: Multi-Objective Controllable Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qiang He · Yucheng Yang · Tianyi Zhou · Meng Fang · Mykola Pechenizkiy · Setareh Maghsudi
7 April 2026 at 12:00
arXiv:2604.04497v1 Announce Type: cross Abstract: Aligning large language models (LLMs) with human preferences is critical for enhancing LLMs' safety, helpfulness, humor, faithfulness, etc. Current reinforcement learning from human feedback (RLHF) mainly focuses on a fixed reward learned from average human ratings, which may weaken the adaptability and controllability of varying preferences. However, creating personalized LLMs requires aligning LLMs with individual human preferences, which is non-trivial due to the scarce data per user and the diversity of user preferences in multi-objective trade-offs, varying from emphasizing empathy in certain contexts to demanding efficiency and precision in others. Can we train one LLM to produce personalized outputs across different user preferences on the Pareto front? In this paper, we introduce Multi-Objective Control (MOC), which trains a single LLM to directly generate responses in the preference-defined regions of the Pareto front. Our approach introduces multi-objective optimization (MOO) principles into RLHF to train an LLM as a preference-conditioned policy network. We improve the computational efficiency of MOC by applying MOO at the policy level, enabling us to fine-tune a 7B-parameter model on a single A6000 GPU. Extensive experiments demonstrate the advantages of MOC over baselines in three aspects: (i) controllability of LLM outputs w.r.t. user preferences on the trade-off among multiple rewards; (ii) quality and diversity of LLM outputs, measured by the hyper-volume of multiple solutions achieved; and (iii) generalization to unseen preferences. These results highlight MOC's potential for real-world applications requiring scalable and customizable LLMs.
Received — 4 April 2026 ⏭

The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinlei Yu · Zhangquan Chen · Yongbo He · Tianyu Fu · Cheng Yang · Chengming Xu · Yue Ma · Xiaobin Hu · Zhe Cao · Jie Xu · Guibin Zhang · Jiale Tao · Jiayi Zhang · Siyuan Ma · Kaituo Feng · Haojie Huang · Youxing Li · Ronghao Chen · Huacan Wang · Chenglin Wu · Zikun Su · Xiaogang Xu · Kelu Yao · Kun Wang · Chen Gao · Yue Liao · Ruqi Huang · Tao Jin · Cheng Tan · Jiangning Zhang · Wenqi Ren · Yanwei Fu · Yong Liu · Yu Wang · Xiangyu Yue · Yu-Gang Jiang · Shuicheng Yan
3 April 2026 at 12:00
arXiv:2604.02029v1 Announce Type: new Abstract: Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work shows that many critical internal processes are more naturally carried out in continuous latent space than in human-readable verbal traces. This shift is driven by the structural limitations of explicit-space computation, including linguistic redundancy, discretization bottlenecks, sequential inefficiency, and semantic loss. This survey aims to provide a unified and up-to-date landscape of latent space in language-based models. We organize the survey into five sequential perspectives: Foundation, Evolution, Mechanism, Ability, and Outlook. We begin by delineating the scope of latent space, distinguishing it from explicit or verbal space and from the latent spaces commonly studied in generative visual models. We then trace the field's evolution from early exploratory efforts to the current large-scale expansion. To organize the technical landscape, we examine existing work through the complementary lenses of mechanism and ability. From the perspective of Mechanism, we identify four major lines of development: Architecture, Representation, Computation, and Optimization. From the perspective of Ability, we show how latent space supports a broad capability spectrum spanning Reasoning, Planning, Modeling, Perception, Memory, Collaboration, and Embodiment. Beyond consolidation, we discuss the key open challenges, and outline promising directions for future research. We hope this survey serves not only as a reference for existing work, but also as a foundation for understanding latent space as a general computational and systems paradigm for next-generation intelligence.
Received — 26 March 2026 ⏭

ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hao Wang · Haocheng Yang · Licheng Pan · Lei Shen · Xiaoxi Li · Yinuo Wang · Zhichao Chen · Yuan Lu · Haoxuan Li · Zhouchen Lin
25 March 2026 at 12:00
arXiv:2603.23184v1 Announce Type: cross Abstract: Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling is heavily contingent upon experimental feedback data with high collection costs. In this work, we study \textit{implicit reward modeling} -- learning reward models from implicit human feedback (e.g., clicks and copies) -- as a cost-effective alternative. We identify two fundamental challenges in implicit reward modeling: (1) Implicit preference data lacks definitive negative samples, which makes standard positive-negative classification methods inapplicable; (2) Implicit preference data suffers from user preference bias, where different responses have different propensities to elicit user feedback actions, which exacerbates the difficulty of distinguishing definitive negative samples. To address these challenges, we propose ImplicitRM, which aims to learn unbiased reward models from implicit preference data. ImplicitRM stratifies training samples into four latent groups via a stratification model. Building on this, it derives a learning objective through likelihood maximization, which we prove is theoretically unbiased, effectively resolving both challenges. Experiments demonstrate that ImplicitRM learns accurate reward models across implicit preference datasets. Code is available on our project website.

Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinrong Chen · Xu Chu · Yingmin Qiu · Hengyuan Zhang · Jing Xiong · Shiyu Tang · Shuai Liu · Shaokang Yang · Cheng Yang · Hayden Kwok-Hay So · Ngai Wong
25 March 2026 at 12:00
arXiv:2602.01047v3 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) can reason from image-text inputs and perform well in various multimodal tasks. Despite this success, they are affected by language priors and often produce hallucinations. Hallucinations denote generated content that is grammatically and syntactically coherent, yet bears no match or direct relevance to visual input. To address this problem, we propose Residual Decoding (ResDec). It is a novel training-free method that uses historical information to aid decoding. The method relies on the internal implicit reasoning mechanism and token logits evolution mechanism of LVLMs to correct biases. Extensive experiments demonstrate that ResDec effectively suppresses hallucinations induced by language priors, significantly improves visual grounding, and reduces object hallucinations. In addition to mitigating hallucinations, ResDec also performs exceptionally well on comprehensive LVLM benchmarks, highlighting its broad applicability.
Received — 11 March 2026 ⏭

Goal Alignment in LLM-Based User Simulators for Conversational AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shuhaib Mehri, Xiaocheng Yang, Takyoung Kim, Gokhan Tur, Shikib Mehri, Dilek Hakkani-T\"ur
10 March 2026 at 12:00
arXiv:2507.20152v2 Announce Type: replace-cross Abstract: User simulators are essential to conversational AI, enabling scalable agent development and evaluation through simulated interactions. While current Large Language Models (LLMs) have advanced user simulation capabilities, we reveal that they struggle to consistently demonstrate goal-oriented behavior across multi-turn conversations--a critical limitation that compromises their reliability in downstream applications. We introduce User Goal State Tracking (UGST), a novel framework that tracks user goal progression throughout conversations. Leveraging UGST, we present a three-stage methodology for developing user simulators that can autonomously track goal progression and reason to generate goal-aligned responses. Moreover, we establish comprehensive evaluation metrics for measuring goal alignment in user simulators, and demonstrate that our approach yields substantial improvements across two benchmarks (MultiWOZ 2.4 and {\tau}-Bench). Our contributions address a critical gap in conversational AI and establish UGST as an essential framework for developing goal-aligned user simulators.
Received — 5 March 2026 ⏭

From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruilin Luo · Chufan Shi · Yizhen Zhang · Cheng Yang · Songtao Jiang · Tongkun Guan · Ruizhe Chen · Ruihang Chu · Peng Wang · Mingkun Yang · Yujiu Yang · Junyang Lin · Zhibo Yang
5 March 2026 at 13:00
arXiv:2603.03825v1 Announce Type: cross Abstract: The cold-start initialization stage plays a pivotal role in training Multimodal Large Reasoning Models (MLRMs), yet its mechanisms remain insufficiently understood. To analyze this stage, we introduce the Visual Attention Score (VAS), an attention-based metric that quantifies how much a model attends to visual tokens. We find that reasoning performance is strongly correlated with VAS (r=0.9616): models with higher VAS achieve substantially stronger multimodal reasoning. Surprisingly, multimodal cold-start fails to elevate VAS, resulting in attention distributions close to the base model, whereas text-only cold-start leads to a clear increase. We term this counter-intuitive phenomenon Lazy Attention Localization. To validate its causal role, we design training-free interventions that directly modulate attention allocation during inference, performance gains of 1$-$2% without any retraining. Building on these insights, we further propose Attention-Guided Visual Anchoring and Reflection (AVAR), a comprehensive cold-start framework that integrates visual-anchored data synthesis, attention-guided objectives, and visual-anchored reward shaping. Applied to Qwen2.5-VL-7B, AVAR achieves an average gain of 7.0% across 7 multimodal reasoning benchmarks. Ablation studies further confirm that each component of AVAR contributes step-wise to the overall gains. The code, data, and models are available at https://github.com/lrlbbzl/Qwen-AVAR.

PlaneCycle: Training-Free 2D-to-3D Lifting of Foundation Models Without Adapters

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yinghong Yu · Guangyuan Li · Jiancheng Yang
5 March 2026 at 13:00
arXiv:2603.04165v1 Announce Type: cross Abstract: Large-scale 2D foundation models exhibit strong transferable representations, yet extending them to 3D volumetric data typically requires retraining, adapters, or architectural redesign. We introduce PlaneCycle, a training-free, adapter-free operator for architecture-agnostic 2D-to-3D lifting of foundation models. PlaneCycle reuses the original pretrained 2D backbone by cyclically distributing spatial aggregation across orthogonal HW, DW, and DH planes throughout network depth, enabling progressive 3D fusion while preserving pretrained inductive biases. The method introduces no additional parameters and is applicable to arbitrary 2D networks. Using pretrained DINOv3 models, we evaluate PlaneCycle on six 3D classification and three 3D segmentation benchmarks. Without any training, the lifted models exhibit intrinsic 3D fusion capability and, under linear probing, outperform slice-wise 2D baselines and strong 3D counterparts, approaching the performance of fully trained models. With full fine-tuning, PlaneCycle matches standard 3D architectures, highlighting its potential as a seamless and practical 2D-to-3D lifting operator. These results demonstrate that 3D capability can be unlocked from pretrained 2D foundation models without structural modification or retraining. Code is available at https://github.com/HINTLab/PlaneCycle.
Received — 25 February 2026 ⏭

Recurrent Structural Policy Gradient for Partially Observable Mean Field Games

✇cs.AI, q-bio.NC updates on arXiv.org
By: Clarisse Wibault · Johannes Forkel · Sebastian Towers · Tiphaine Wibault · Juan Duque · George Whittle · Andreas Schaab · Yucheng Yang · Chiyuan Wang · Michael Osborne · Benjamin Moll · Jakob Foerster
24 February 2026 at 13:00
arXiv:2602.20141v1 Announce Type: new Abstract: Mean Field Games (MFGs) provide a principled framework for modeling interactions in large population models: at scale, population dynamics become deterministic, with uncertainty entering only through aggregate shocks, or common noise. However, algorithmic progress has been limited since model-free methods are too high variance and exact methods scale poorly. Recent Hybrid Structural Methods (HSMs) use Monte Carlo rollouts for the common noise in combination with exact estimation of the expected return, conditioned on those samples. However, HSMs have not been scaled to Partially Observable settings. We propose Recurrent Structural Policy Gradient (RSPG), the first history-aware HSM for settings involving public information. We also introduce MFAX, our JAX-based framework for MFGs. By leveraging known transition dynamics, RSPG achieves state-of-the-art performance as well as an order-of-magnitude faster convergence and solves, for the first time, a macroeconomics MFG with heterogeneous agents, common noise and history-aware policies. MFAX is publicly available at: https://github.com/CWibault/mfax.

A Secure and Private Distributed Bayesian Federated Learning Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Nuocheng Yang · Sihua Wang · Zhaohui Yang · Mingzhe Chen · Changchuan Yin · Kaibin Huang
24 February 2026 at 13:00
arXiv:2602.20003v1 Announce Type: cross Abstract: Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server. However, DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy. To address these issues, we propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration. Within this framework, each device trains a local model using a Bayesian approach and independently selects an optimal subset of neighbors for posterior exchange. We formulate this neighbor selection as an optimization problem to minimize the global loss function under security and privacy constraints. Solving this problem is challenging because devices only possess partial network information, and the complex coupling between topology, security, and convergence remains unclear. To bridge this gap, we first analytically characterize the trade-offs between dynamic connectivity, Byzantine detection, privacy levels, and convergence speed. Leveraging these insights, we develop a fully distributed Graph Neural Network (GNN)-based Reinforcement Learning (RL) algorithm. This approach enables devices to make autonomous connection decisions based on local observations. Simulation results demonstrate that our method achieves superior robustness and efficiency with significantly lower overhead compared to traditional security and privacy schemes.
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