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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

Circadian-based individualised protection against inflammation-cancer transition in atrophic gastritis patients

✇Omics in Gastric
By: Ceng Shan · Min Guo · Feifan Feng · Xihong Wang · Mohang Li · Weichao Xu · Keyu Jia · Yifei Liu · Yifan Jia
2 September 2026 at 18:00

EPMA J. 2026 Aug 21;17(3):665-700. doi: 10.1007/s13167-026-00465-4. eCollection 2026 Sep.

ABSTRACT

Chronic atrophic gastritis (CAG) is a critical precancerous stage in the development of gastric cancer (GC). Circadian rhythm disruption perturbs the core clock gene network, including circadian locomotor output cycles kaput (CLOCK), brain and muscle ARNT-like 1 (BMAL1), period circadian protein homolog (PER), and cryptochrome (CRY). These alterations contribute to a multi-layered pathological cascade involving DNA damage accumulation, epigenetic remodeling, altered epithelial cell plasticity, cellular senescence, microbiota dysbiosis, tumor microenvironment remodeling, metabolic reprogramming, aberrant angiogenesis, and dysregulated cell death, thereby accelerating CAG to GC progression. However, existing studies have predominantly treated the circadian rhythm as a passive risk factor for disease onset and have yet to elevate it to an actionable interventional target within the full-course management of gastric precancerous lesions. Building on a systematic synthesis of the mechanistic evidence outlined above, this review proposes a predictive, preventive and personalised medicine (PPPM/3PM) three-tier management framework grounded in circadian-based individualised protection. At the predictive level, digital biomarkers (sleep-wake rhythms, light exposure, physical activity, and dietary behavior), multi-omics profiles, and circadian-related molecular signatures are integrated to achieve dynamic risk stratification of CAG populations. At the targeted prevention level, pharmacological agents and natural compounds with circadian-regulating potential are deployed to develop proactive protective strategies tailored to distinct pathological stages and circadian phenotypes. At the personalised treatment level, lifestyle interventions, chronotherapy, nano-carrier-based circadian-synchronised delivery, and dynamic biomarker monitoring are combined to formulate precision intervention regimens informed by individual circadian phenotypes. This framework repositions the circadian rhythm from a latent risk factor to a protectable and therapeutically targetable axis, offering new insights into time-optimised intervention strategies for the inflammation to cancer transition in CAG.

PMID:42682657 | PMC:PMC13530114 | DOI:10.1007/s13167-026-00465-4

Received — 27 May 2026 ⏭

A Sober Look at Agentic Misalignment in Automated Workflows

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenqian Ye · Bo Yuan · Zhichao Xu · Yijun Tian · Yawei Wang · Henry Kautz · Aidong Zhang
26 May 2026 at 12:00
arXiv:2605.24197v1 Announce Type: new Abstract: We study a class of emergent misalignment in multi-agent systems (MAS), with a focus on automated workflows, which we refer to agentic misalignment. Although these systems can solve complex tasks, they often fail because agents act according to implicit proxy utilities that do not align with the intended human goals. We formally define these behaviors and analyze them within a Bayesian framework, showing that generic utilities naturally lead to posterior collapse of agents in automated workflows. To address this issue, we propose Agentic Evidence Attribution (AEA), a novel alignment paradigm that improves agent posteriors using context-specific evidence. AEA reasons over agent actions and provides structured evidence to correct misaligned behavior during collaboration. To better understand the role of evidence, we study two instantiations of AEA: self-reflection (internal evidence from the model) and weak-to-strong generalization (external evidence on the agentic trajectory). We show that a small evidence model effectively aligns the MAS by providing orthogonal failure attribution. Our results clarify the sources of agentic misalignment in automated workflows and show that evidence-based alignment can effectively improve agent collaboration and leads to reliable multi-agent systems built on automated workflows.

BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bo Zou · Chao Xu
26 May 2026 at 12:00
arXiv:2605.25549v1 Announce Type: cross Abstract: High-quality expert chain-of-thought (CoT) data is one of the core bottlenecks in large language model (LLM) post-training. Existing data production methods each have structural limitations: crowdsourced annotation lacks deep reasoning paths; expert solo writing is constrained by the "expert blind spot" -- experts structurally skip reasoning steps they consider obvious; RLHF only produces preference signals rather than reasoning chains. This paper proposes the BC Protocol -- a structured dual-expert elicitation method for LLM post-training data production. The method carefully pairs a domain expert (crystallized intelligence) with a knowledge engineer (fluid intelligence), systematically externalizing the expert's implicit judgments as natural language reasoning chains. We introduce the Participant Aptitude Model, which defines six participant characteristic dimensions that affect elicitation quality. "Calibrated Ignorance" is an original concept proposed in this paper. We further propose "Selection-over-Prescription" as a methodological principle: for implicit knowledge elicitation tasks, investing quality-control resources in personnel selection yields a higher return than investing the same resources in process design. In a controlled experiment in the narrative fiction domain, we directly compared CoT produced by BC Protocol dual dialogue (Group A, (n=20)) against CoT written independently by the same domain expert (Group B, (n=20)). Three cross-vendor judge models -- GPT-4o, Claude Opus 4.5, and Gemini 2.5 Pro -- conducted blind evaluation across five dimensions (600 ratings total). Results show that the BC Protocol achieves an overwhelming advantage in "naturalness of reasoning process" (Group A mean 4.80 vs. Group B mean 1.30, (p=2.4\times10^{-8}), Cliff's (\delta=1.0)).

QUIET: A Multi-Blank Cascaded Story Cloze Benchmark for LLM Creative Generation Capability

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bo Zou · Chao Xu
26 May 2026 at 12:00
arXiv:2605.25955v1 Announce Type: cross Abstract: Large language models (LLMs) face a dual challenge in creative capability evaluation: existing benchmarks (e.g., Story Cloze Test, HellaSwag) measure models' discriminative ability over narrative continuation using multiple-choice recognition paradigms, rather than directly measuring creative generation capability; rubric-based scoring and LLM-as-Judge methods rely on subjective dimension assessment or natural language model outputs, and cannot provide objective, automated scoring mechanisms. This paper proposes QUIET (Quality Understanding via Interlocked Evaluation Testing), a diagnostic benchmark for LLM creative capability based on multi-blank cascaded story cloze. QUIET sets N blanks (10-20) in a story with complete structure, with each blank accompanied by an explicit content constraint, and cascade dependency relationships between blanks -- the content filled into earlier blanks constrains the feasible solution space for later blanks. The evaluated model (or human participants) fills all blanks in open-ended generation mode; the results are scored by an information-theoretic automated scoring protocol without human grading. The scoring protocol directly operationalizes the "calibrated surprise" theoretical framework (Zou & Xu, 2026a). For each blank k, a composite score is computed: score = satisfy * (1 + lambda * surprise), where lambda = 1.0. Here, "satisfy" measures how well the blank filling satisfies the content constraint (objective logical reasoning judgment, not subjective aesthetic scoring), and "surprise" measures the degree of surprise given that the constraint is satisfied. Creative answers that do not satisfy the constraint score zero; answers that satisfy the constraint but are mediocre score low; answers that satisfy the constraint and are surprising score high.

Creative Quality Alignment: Expert Tacit Knowledge Transfer via Chain-of-Thought Fine-Tuning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bo Zou · Chao Xu
26 May 2026 at 12:00
arXiv:2605.25977v1 Announce Type: cross Abstract: This paper provides an empirical implementation of the creative quality metric proposed in Calibrated Surprise (Zou & Xu, 2026a). The question this paper addresses is: does this mathematical claim hold at the engineering level? To make the answer as general as possible, we deliberately choose the strictest engineering conditions: low data cost and a small base model. Training data comes from approximately 100 expert chain-of-thought (CoT) annotations produced by the BC Protocol (Zou & Xu, 2026b). We also identify a data bias: most publicly available alignment datasets are skewed toward craft-related knowledge, while audience modeling and reality-logic coverage are systematically weak. We use the term Creative Quality Alignment (CQA) to describe this class of engineering methods. We also offer a supporting theoretical observation: in an LLM with a single conditional distribution architecture, calibrating the appreciation side automatically transfers to the generation side via architectural duality. This is the structural reason why ~100 CoT examples are sufficient -- not a purely empirical observation like LIMA (Zhou et al., 2023).
Received — 13 April 2026 ⏭

Clinical application of base editing for treating β-thalassaemia

✇Nature - Issue - nature.com science feeds
By: Yongrong Lai · Rongrong Liu · Lijie Wang · Xu-Kai Ma · Yaliang Li · Gaohui Yang · Lingling Shi · Yi-Lin Guo · Zhenbin Wei · Xuemei Zhou · Wenchao Xu · Yaofeng Hou · Annarita Miccio · Bei Yang · Xiaodun Mou · Li Yang · Jia Chen
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10342-9

A clinical phase 1 trial of a single infusion of CS-101, CD34+ cells modified using a transformer base editor to reactivate fetal haemoglobin production, led to early and enduring transfusion independence in patients with β-thalassaemia.
Received — 11 March 2026 ⏭

Fibration Policy Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chang Li · Tshihao Tsu · Yaren Zhang · Chao Xue · Xiaodong He
10 March 2026 at 12:00
arXiv:2603.08239v1 Announce Type: cross Abstract: Large language models are increasingly trained as heterogeneous systems spanning multiple domains, expert partitions, and agentic pipelines, yet prevalent proximal objectives operate at a single scale and lack a principled mechanism for coupling token-level, trajectory-level, and higher-level hierarchical stability control. To bridge this gap, we derive the Aggregational Policy Censoring Objective (APC-Obj), the first exact unconstrained reformulation of sample-based TV-TRPO, establishing that clipping-based surrogate design and trust-region optimization are dual formulations of the same problem. Building on this foundation, we develop Fiber Bundle Gating (FBG), an algebraic framework that organizes sampled RL data as a fiber bundle and decomposes ratio gating into a base-level gate on trajectory aggregates and a fiber-level gate on per-token residuals, with provable first-order agreement with the true RL objective near on-policy. From APC-Obj and FBG we derive Fibration Policy Optimization (or simply, FiberPO), a concrete objective whose Jacobian is block-diagonal over trajectories, reduces to identity at on-policy, and provides better update direction thus improving token efficiency. The compositional nature of the framework extends beyond the trajectory-token case: fibrations compose algebraically into a Fibration Gating Hierarchy (FGH) that scales the same gating mechanism to arbitrary hierarchical depth without new primitives, as demonstrated by FiberPO-Domain, a four-level instantiation with independent trust-region budgets at the domain, prompt group, trajectory, and token levels. Together, these results connect the trust-region theory, a compositional algebraic structure, and practical multi-scale stability control into a unified framework for LLM policy optimization.

Oscillatory shear stress-driven endothelial-to-mesenchymal transition: a critical mechanical signal transduction mechanism in atherosclerosis progression

✇Cell Death Discovery nature.com science feeds
By: Jie Li · Wenchao Xu · Jie Ju · Min Liu · Wenxu Wang · Min Cheng · Xiaoyun Zhang · Xiaodong Cui · Hao Chen
10 March 2026 at 08:00

Cell Death Discovery, Published online: 10 March 2026; doi:10.1038/s41420-026-03000-6

Oscillatory shear stress-driven endothelial-to-mesenchymal transition: a critical mechanical signal transduction mechanism in atherosclerosis progression
Received — 5 March 2026 ⏭

HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dahai Yu · Lin Jiang · Rongchao Xu · Guang Wang
5 March 2026 at 13:00
arXiv:2602.05286v2 Announce Type: replace-cross Abstract: Healthcare facility visit prediction is essential for optimizing healthcare resource allocation and informing public health policy. Despite advanced machine learning methods being employed for better prediction performance, existing works usually formulate this task as a time-series forecasting problem without considering the intrinsic spatial dependencies of different types of healthcare facilities, and they also fail to provide reliable predictions under abnormal situations such as public emergencies. To advance existing research, we propose HealthMamba, an uncertainty-aware spatiotemporal framework for accurate and reliable healthcare facility visit prediction. HealthMamba comprises three key components: (i) a Unified Spatiotemporal Context Encoder that fuses heterogeneous static and dynamic information, (ii) a novel Graph State Space Model called GraphMamba for hierarchical spatiotemporal modeling, and (iii) a comprehensive uncertainty quantification module integrating three uncertainty quantification mechanisms for reliable prediction. We evaluate HealthMamba on four large-scale real-world datasets from California, New York, Texas, and Florida. Results show HealthMamba achieves around 6.0% improvement in prediction accuracy and 3.5% improvement in uncertainty quantification over state-of-the-art baselines.
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