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MCAT-mediated mitochondrial fatty acid metabolism regulates Lauren subtype divergence and suppresses gastric cancer progression through ROS/P53-dependent mitophagy and ferroptosis

✇Omics in Gastric
By: Yufei Wang · Meng Zhu · Xin Yin · Tianyi Fang · Lei Zhang · Yujuan Jiao · Hongxin Wang · Zipu Dong · Tengteng Liu · Yingwei Xue

Cell Death Differ. 2026 Sep 8. doi: 10.1038/s41418-026-01867-7. Online ahead of print.

ABSTRACT

Gastric cancer (GC) displays marked heterogeneity under the Lauren classification, yet the metabolic determinants of subtype divergence remain unclear. Here, we identify Malonyl-CoA:ACP transacylase (MCAT), a Lauren subtype-associated gene encoding a key mitochondrial fatty acid synthesis (mtFAS) enzyme, as a subtype-specific tumor suppressor in GC. Integrative multi-omics profiling revealed that MCAT expression is enriched in intestinal-type GC and correlates with favorable prognosis. Mechanistically, MCAT overexpression drives metabolic reprogramming through mitochondrial free fatty acid overload, suppressing β-oxidation while elevating mitochondrial reactive oxygen species (ROS), which triggers P53 phosphorylation at Ser15. This event concurrently activates PINK1/Parkin-mediated mitophagy and suppresses the SLC7A11/GPX4 axis to induce ferroptosis. Genetic rescue experiments confirmed that P53-Ser15 phosphorylation is essential for both mitophagy and ferroptosis induction. Endogenous MCAT levels are sufficient to determine basal ROS/P53/mitophagy/ferroptosis axis activity, and knockdown in high-expressing cells reverses these phenotypes, supporting a physiological, threshold-dependent role. In vivo, MCAT overexpression suppresses tumor growth and enhances mitophagy and ferroptosis markers. Collectively, these findings establish MCAT as a metabolic switch that links mtFAS to ROS/P53-dependent cell death, providing a potential biomarker and therapeutic target for GC.

PMID:42711380 | DOI:10.1038/s41418-026-01867-7

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MCAT-mediated mitochondrial fatty acid metabolism regulates Lauren subtype divergence and suppresses gastric cancer progression through ROS/P53-dependent mitophagy and ferroptosis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yufei Wang · Meng Zhu · Xin Yin · Tianyi Fang · Lei Zhang · Yujuan Jiao · Hongxin Wang · Zipu Dong · Tengteng Liu · Yingwei Xue

Cell Death Differ. 2026 Sep 8. doi: 10.1038/s41418-026-01867-7. Online ahead of print.

ABSTRACT

Gastric cancer (GC) displays marked heterogeneity under the Lauren classification, yet the metabolic determinants of subtype divergence remain unclear. Here, we identify Malonyl-CoA:ACP transacylase (MCAT), a Lauren subtype-associated gene encoding a key mitochondrial fatty acid synthesis (mtFAS) enzyme, as a subtype-specific tumor suppressor in GC. Integrative multi-omics profiling revealed that MCAT expression is enriched in intestinal-type GC and correlates with favorable prognosis. Mechanistically, MCAT overexpression drives metabolic reprogramming through mitochondrial free fatty acid overload, suppressing β-oxidation while elevating mitochondrial reactive oxygen species (ROS), which triggers P53 phosphorylation at Ser15. This event concurrently activates PINK1/Parkin-mediated mitophagy and suppresses the SLC7A11/GPX4 axis to induce ferroptosis. Genetic rescue experiments confirmed that P53-Ser15 phosphorylation is essential for both mitophagy and ferroptosis induction. Endogenous MCAT levels are sufficient to determine basal ROS/P53/mitophagy/ferroptosis axis activity, and knockdown in high-expressing cells reverses these phenotypes, supporting a physiological, threshold-dependent role. In vivo, MCAT overexpression suppresses tumor growth and enhances mitophagy and ferroptosis markers. Collectively, these findings establish MCAT as a metabolic switch that links mtFAS to ROS/P53-dependent cell death, providing a potential biomarker and therapeutic target for GC.

PMID:42711380 | DOI:10.1038/s41418-026-01867-7

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Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuheng Jing · Kai Li · Ziwen Zhang · Jiajun Zhang · Zeyao Ma · Jiaxi Yang · Lei Zhang · Zhe Wu · Jinmin He · Junliang Xing · Jian Cheng
arXiv:2605.24423v1 Announce Type: new Abstract: In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordination with unknown partners is required-remains unexplored. To rigorously evaluate this, we introduce a large-scale benchmark ICRL4AHT, built upon a high-throughput JAX implementation of Overcooked-V2. Our benchmark includes a large, diverse teammate suite spanning both RL and heuristic policies, enabling controlled train-test shifts, and provides a reproducible end-to-end pipeline for teammate generation, learning-history collection, dataset construction, and online multi-episode evaluation. We evaluate representative history-conditioned ICRL algorithms, including Algorithm Distillation (AD) and Decision-Pretrained Transformer (DPT), across millions of transitions. Results reveal notable limitations: contrary to their success in single-agent domains, these baselines fail to exhibit robust test-time adaptation in multi-agent settings. Specifically, these methods frequently underperform random baselines across both unseen teammate and unseen layout tracks, with no clear in-context improvement over long horizons. These findings highlight the challenges of strategic inference under partial observability within the OvercookedV2 AHT protocol, establishing our benchmark as a critical testbed for next-generation coordination algorithms.
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Agent Manufacturing: Foundation-Model Agents as First-Class Industrial Entities

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yilei Zhang
arXiv:2605.24823v1 Announce Type: new Abstract: Manufacturing has passed through four widely recognized paradigms - mechanization, electrification, programmable automation, and Smart Manufacturing - each defined by the kind of work it shifted from humans to machines. In every case, one layer of industrial work remained fundamentally human: the coordinative cognition of production, comprising the interpretive, allocative, diagnostic, negotiative, and governance work exercised by engineers, planners, and operational managers. We argue that a fifth transition is now underway in which this layer, rather than the physical or routine-cognitive layers below it, is what foundation-model-based autonomous agents primarily redistribute. We name this paradigm Agent Manufacturing and define it operationally: a manufacturing system is an instance of Agent Manufacturing when its principal coordination mechanism is reasoning performed by foundation-model agents that can interpret open-ended goals, plan over long horizons, invoke tools and machines, and negotiate with other agents and humans. This is a narrower and more falsifiable definition than the existing literature on cognitive manufacturing or Industry 5.0 provides, and it distinguishes the paradigm sharply from classical multi-agent manufacturing systems, which were autonomous only within closed protocol spaces.
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A SAUR gene enhances maize drought resilience by promoting silk elongation

✇Nature - Issue - nature.com science feeds
By: Chaohui Zhu · Zhirui Yang · Shiping Yang · Xueyan Zhou · Boxin Liu · Tian Tian · Bochen Zhao · Yingying Xie · Yujun Liu · Jinkui Cheng · Huaijun Tang · Yanjun Zhang · Xiaoqing Xie · Lei Zhang · Cheng Liu · Xingrong Wang · Shengxue Liu · Feng Qin

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10566-9

The Small Auxin Up RNA (SAUR) protein ZmSAUR72 in maize (Zea mays) promotes silk growth via regulation of H+-ATPase activity, and is a key determinant of the anthesis-silking interval and thus resilience to drought.
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Transplantation of encapsulated mitochondria alleviates dysfunction in mitochondrial and Parkinson’s disease models

✇Cell
By: Shiwei Du · Qi Long · Yanshuang Zhou · Jiangqin Fu · Hao Wu · Liang Yang · Yaohang Xie · Yingzhe Ding · Maolei Zhang · Jingyi Guo · Mengfei Wang · Jiajun Lin · Mingli Hu · Jian Zhang · Deyang Yao · Wei Li · Feixiang Bao · Ge Xiang · Yi Wu · Yile Huang · Haozhao Liang · Rui Wang · Heying Li · Baodan Chen · Chong Li · Junwei Wang · Jiwei Zhang · Dajiang Qin · Jianwei Sun · Yun Zhu · Fei Sun · Wuming Wang · Gang Lu · Wai-Yee Chan · Hui Zhao · Chenli Liu · Xingguo Liu
A mitochondrial transplantation approach rescues mitochondrial deficiency and prevents mitochondrial DNA depletion syndrome, Leigh syndrome, and Parkinson’s disease in cellular and mouse models.
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V-Reflection: Transforming MLLMs from Passive Observers to Active Interrogators

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiazhou Zhou · Yucheng Chen · Hongyang Li · Qing Jiang · Hu Zhou · Ying-Cong Chen · Lei Zhang
arXiv:2604.03307v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success, yet they remain prone to perception-related hallucinations in fine-grained tasks. This vulnerability arises from a fundamental limitation: their reasoning is largely restricted to the language domain, treating visual input as a static, reasoning-agnostic preamble rather than a dynamic participant. Consequently, current models act as passive observers, unable to re-examine visual details to ground their evolving reasoning states. To overcome this, we propose V-Reflection, a framework that transforms the MLLM into an active interrogator through a "think-then-look" visual reflection mechanism. During reasoning, latent states function as dynamic probes that actively interrogate the visual feature space, grounding each reasoning step for task-critical evidence. Our approach employs a two-stage distillation strategy. First, the Box-Guided Compression (BCM) module establishes stable pixel-to-latent targets through explicit spatial grounding. Next, a Dynamic Autoregressive Compression (DAC) module maps the model's hidden states into dynamic probes that interrogate the global visual feature map. By distilling the spatial expertise of the BCM teacher into the DAC student, V-Reflection internalizes the ability to localize task-critical evidence. During inference, both modules remain entirely inactive, maintaining a purely end-to-end autoregressive decoding in the latent space with optimal efficiency. Extensive experiments demonstrate the effectiveness of our V-Reflection across six perception-intensive benchmarks, significantly narrowing the fine-grained perception gap. Visualizations confirm that latent reasoning autonomously localizes task-critical visual evidence.
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Stabilizing Rubric Integration Training via Decoupled Advantage Normalization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zelin Tan · Zhouliang Yu · Bohan Lin · Zijie Geng · Hejia Geng · Yudong Zhang · Mulei Zhang · Yang Chen · Shuyue Hu · Zhenfei Yin · Chen Zhang · Lei Bai
arXiv:2603.26535v2 Announce Type: replace Abstract: We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) through decoupled advantage normalization, to address two limitations of existing reward designs. Outcome reward models (ORM) evaluate only final-answer correctness, treating all correct responses identically regardless of reasoning quality, and gradually lose the advantage signal as groups become uniformly correct. Process reward models (PRM) offer richer supervision, but directly using PRM scores causes reward hacking, where models exploit verbosity to inflate scores while accuracy collapses. PAPO resolves both by composing the advantage from an outcome component Aout, derived from ORM and normalized over all responses, and a process component Aproc, derived from a rubric-based PRM and normalized exclusively among correct responses. This decoupled design ensures that Aout anchors training on correctness while Aproc differentiates reasoning quality without distorting the outcome signal. Experiments across multiple model scales and six benchmarks demonstrate that PAPO consistently outperforms ORM, reaching 51.3% vs.\ 46.3% on OlympiadBench while continuing to improve as ORM plateaus and declines.
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WFR-FM: Simulation-Free Dynamic Unbalanced Optimal Transport

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qiangwei Peng · Zihan Wang · Junda Ying · Yuhao Sun · Qing Nie · Lei Zhang · Tiejun Li · Peijie Zhou
arXiv:2601.06810v2 Announce Type: replace-cross Abstract: The Wasserstein-Fisher-Rao (WFR) metric extends dynamic optimal transport (OT) by coupling displacement with change of mass, providing a principled geometry for modeling unbalanced snapshot dynamics. Existing WFR solvers, however, are often unstable, computationally expensive, and difficult to scale. Here we introduce WFR Flow Matching (WFR-FM), a simulation-free training algorithm that unifies flow matching with dynamic unbalanced OT. Unlike classical flow matching which regresses only a transport vector field, WFR-FM simultaneously regresses a vector field for displacement and a scalar growth rate function for birth-death dynamics, yielding continuous flows under the WFR geometry. Theoretically, we show that minimizing the WFR-FM loss exactly recovers WFR geodesics. Empirically, WFR-FM yields more accurate and robust trajectory inference in single-cell biology, reconstructing consistent dynamics with proliferation and apoptosis, estimating time-varying growth fields, and applying to generative dynamics under imbalanced data. It outperforms state-of-the-art baselines in efficiency, stability, and reconstruction accuracy. Overall, WFR-FM establishes a unified and efficient paradigm for learning dynamical systems from unbalanced snapshots, where not only states but also mass evolve over time. The Python code is available at https://github.com/QiangweiPeng/WFR-FM.
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TrafficMoE: Heterogeneity-aware Mixture of Experts for Encrypted Traffic Classification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qing He · Xiaowei Fu · Lei Zhang
arXiv:2603.29520v1 Announce Type: cross Abstract: Encrypted traffic classification is a critical task for network security. While deep learning has advanced this field, the occlusion of payload semantics by encryption severely challenges standard modeling approaches. Most existing frameworks rely on static and homogeneous pipelines that apply uniform parameter sharing and static fusion strategies across all inputs. This one-size-fits-all static design is inherently flawed: by forcing structured headers and randomized payloads into a unified processing pipeline, it inevitably entangles the raw protocol signals with stochastic encryption noise, thereby degrading the fine-grained discriminative features. In this paper, we propose TrafficMoE, a framework that breaks through the bottleneck of static modeling by establishing a Disentangle-Filter-Aggregate (DFA) paradigm. Specifically, to resolve the structural between-components conflict, the architecture disentangles headers and payloads using dual-branch sparse Mixture-of-Experts (MoE), enabling modality-specific modeling. To mitigate the impact of stochastic noise, an uncertainty-aware filtering mechanism is introduced to quantify reliability and selectively suppress high-variance representations. Finally, to overcome the limitations of static fusion, a routing-guided strategy aggregates cross-modality features dynamically, that adaptively weighs contributions based on traffic context. With this DFA paradigm, TrafficMoE maximizes representational efficiency by focusing solely on the most discriminative traffic features. Extensive experiments on six datasets demonstrate TrafficMoE consistently outperforms state-of-the-art methods, validating the necessity of heterogeneity-aware modeling in encrypted traffic analysis. The source code is publicly available at https://github.com/Posuly/TrafficMoE_main.
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Mean Masked Autoencoder with Flow-Mixing for Encrypted Traffic Classification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiao Liu · Xiaowei Fu · Fuxiang Huang · Lei Zhang
arXiv:2603.29537v1 Announce Type: cross Abstract: Network traffic classification using self-supervised pre-training models based on Masked Autoencoders (MAE) has demonstrated a huge potential. However, existing methods are confined to isolated byte-level reconstruction of individual flows, lacking adequate perception of the multi-granularity contextual relationship in traffic. To address this limitation, we propose Mean MAE (MMAE), a teacher-student MAE paradigm with flow mixing strategy for building encrypted traffic pre-training model. MMAE employs a self-distillation mechanism for teacher-student interaction, where the teacher provides unmasked flow-level semantic supervision to advance the student from local byte reconstruction to multi-granularity comprehension. To break the information bottleneck in individual flows, we introduce a dynamic Flow Mixing (FlowMix) strategy to replace traditional random masking mechanism. By constructing challenging cross-flow mixed samples with interferences, it compels the model to learn discriminative representations from distorted tokens. Furthermore, we design a Packet-importance aware Mask Predictor (PMP) equipped with an attention bias mechanism that leverages packet-level side-channel statistics to dynamically mask tokens with high semantic density. Numerous experiments on a number of datasets covering encrypted applications, malware, and attack traffic demonstrate that MMAE achieves state-of-the-art performance. The code is available at https://github.com/lx6c78/MMAE
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Building evidence-based knowledge graphs from full-text literature for disease-specific biomedical reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chang Zong · Sicheng Lv · Si-tu Xue · Huilin Zheng · Jian Wan · Lei Zhang
arXiv:2603.28325v2 Announce Type: replace-cross Abstract: Biomedical knowledge resources often either preserve evidence as unstructured text or compress it into flat triples that omit study design, provenance, and quantitative support. Here we present EvidenceNet, a framework and dataset for building disease-specific knowledge graphs from full-text biomedical literature. EvidenceNet uses a large language model (LLM)-assisted pipeline to extract experimentally grounded findings as structured evidence nodes, normalize biomedical entities, score evidence quality, and connect evidence records through typed semantic relations. We release two resources: EvidenceNet-HCC with 7,872 evidence records, 10,328 graph nodes, and 49,756 edges, and EvidenceNet-CRC with 6,622 records, 8,795 nodes, and 39,361 edges. Technical validation shows high component fidelity, including 98.3% field-level extraction accuracy, 100.0% high-confidence entity-link accuracy, 87.5% fusion integrity, and 90.0% semantic relation-type accuracy. In downstream evaluation, EvidenceNet improves internal and external retrieval-augmented question answering and retains structural signal for future link prediction and target prioritization. These results establish EvidenceNet as a disease-specific resource for evidence-aware biomedical reasoning and hypothesis generation.
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Gut-Brain Axis Dysregulation in Inflammatory Bowel Disease: Implications for Coagulation Abnormalities and Extraintestinal Manifestations

✇(Multiomics OR Omics) AND (Pancreatic)
By: Peng Lu · Maolin Liu · Lei Zhang · Jing-Jing Fan · Guoda Han · Baozhou Hou · Yu Meng · Liang Wang · Yan Sun

Int J Gen Med. 2026 Mar 24;19:590621. doi: 10.2147/IJGM.S590621. eCollection 2026.

ABSTRACT

Inflammatory bowel disease (IBD) involves chronic intestinal inflammation driven by gut-brain axis imbalance, fostering complications through an "inflammation-neuro-coagulation" triad. Current staging systems inadequately capture the dynamics of this multidimensional network. Therefore, integrated multi-omics analyses-including metagenomics, metabolomics, and single-cell transcriptomics-are essential to construct dynamic models that monitor coagulation, microbiome, and metabolism for precise assessment of disease activity and thrombotic or bleeding risks. Interventions targeting gut-brain axis nodes, such as eliminating tissue factor-positive (TF⁺) T cells or modulating vagal activity, show potential to disrupt the inflammation-coagulation cycle, although rigorous randomized trials are still needed. Artificial intelligence (AI)-assisted systems that integrate real-time biomarker monitoring with multi-omics predictions represent a novel paradigm for managing IBD-related coagulation dysfunction. Key challenges include elucidating gut-brain-liver axis regulation of coagulation and characterizing platelet functional heterogeneity. Future efforts must prioritize ethically compliant multi-omics platforms and racially stratified risk models to advance personalized coagulation management in IBD.

PMID:41913906 | PMC:PMC13033200 | DOI:10.2147/IJGM.S590621

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Androgen activity in the male embryonic hindbrain drives lethal PFA ependymoma

✇Nature - Issue - nature.com science feeds
By: Jiao Zhang · Winnie Ong · Alexandra Rasnitsyn · Ricardo Daniel Gonzalez · Rodrigo Lopez Gutierrez · Polina Balin · Amr Saadeldin · Xiaochong Wu · Maria C. Vladoiu · Vicente Santa-Maria Lopez · Fernando Gonzalez-Salinas · Navneesh Yadav · Dinesh Mohanakrishnan · Kannan Boosi Narayana Rao · Raja Gopal Reddy Mooli · Hinda Najem · Sebastian Pacheco · Kaitlin Kharas · Cory Richman · David Przelicki · Evan Y. Wang · Haipeng Su · Rachel Naomi Curry · Runze Yang · Michelle Masayo Kameda-Smith · Bryn Livingston · David Scott · Zaili Luo · Mingyang Xia · Namal Abeysundara · Anders W. Erickson · Ncedile Mankahla · Lucas ZhongMing Hu · Chu Pan · Raul Suarez · Ning Huang · Yihao Wu · Hao Wang · Tajana Douglas · Jonelle Pallota · Steven Hébert · Karen Ng · Krystin Mantione · Heather Whetstone · Hassaan Maan · Hussein Lakkis · Juyeun Lee · Sadeesh K. Ramakrishnan · Yanxin Pei · Yujie Tang · Frank Y. Lin · Guillermo Aldave · Marco Gallo · Robert M. Friedlander · Faiyaz Notta · Laura K. Donovan · Murali Chintagumpala · Bo Wang · Yun Li · Daniel D. De Carvalho · Zhaolei Zhang · Ying M

Nature, Published online: 25 March 2026; doi:10.1038/s41586-026-10264-6

Androgen activity in the male embryonic hindbrain prolongs hindbrain differentiation in male individuals and drives sex differences in the incidence and prognosis of posterior fossa type A (PFA) ependymoma, an aggressive childhood brain tumour.
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Human-specific features of the cerebellum and ZP2-regulated synapse development

✇Cell
By: Suel-Kee Kim · Adriana Cherskov · Aastha Sindhwani · Sang-Hun Choi · Hyojin Kim · Ming-Li Li · Menglei Zhang · Xoel Mato-Blanco · Yuting Liu · Nicola Micali · David M. Young · Mark Estacion · Yueqi Zhang · José Manuel Ruiz-Jiménez · Anandita Nadkarni · Victor Luria · Suvimal Kumar Sindhu · Ipsita Chatterjee · Akemi Shibata · Dan Liang · Hyesun Cho · Saejeong Park · Ana Spajic · Rothem Kovner · Martina Glavan · Rachel J. Chen · Ryan D. Risgaard · Xinyun Li · Sirisha Pochareddy · Amir Karger · Anita Huttner · Yury M. Morozov · Etienne W. Daadi · Carlo Colantuoni · Kevin T. Gobeske · John J. Ely · Patrick R. Hof · Marcel M. Daadi · Chet C. Sherwood · Alvaro Duque · Shaojie Ma · Andre M.M. Sousa · Stephen G. Waxman · Pasko Rakic · Gabriel Santpere · Stephan J. Sanders · Nenad Sestan
Human-specific transcriptomic and regulatory features are present in the cerebellum, with ZP2 playing a key role in synapse regulation. ZP2 expression is induced by pontine mossy fibers, leading to decreased synaptic proteins and neuronal activity, which provides insights into the evolutionary development of the human cerebellum.
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Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kenechi Omeke · Attai Abubakar · Michael Mollel · Lei Zhang · Qammer H. Abbasi · Muhammad Ali Imran
arXiv:2603.07413v1 Announce Type: cross Abstract: The Internet of Underwater Things (IoUT) is becoming a critical infrastructure for ocean observation, marine resource management, and climate science. Its development is hindered by severe acoustic attenuation, propagation delays far exceeding those of terrestrial wireless systems, strict energy constraints, and dynamic topologies shaped by ocean currents. Machine learning (ML) has emerged as a key enabler for addressing these limitations, offering data driven mechanisms that enhance performance across all layers of underwater wireless sensor networks. This tutorial survey synthesises ML methodologies supervised, unsupervised, reinforcement, and deep learning specifically contextualised for underwater communication environments. It outlines the algorithmic principles of each paradigm and examines the conditions under which particular approaches deliver superior performance. A layer wise analysis highlights physical layer gains in localisation and channel estimation, MAC layer adaptations that improve channel utilisation, network layer routing strategies that extend operational lifetime, and transport layer mechanisms capable of reducing packet loss by up to 91 percent. At the application layer, ML enables substantial data compression and object detection accuracies reaching 92 percent. Drawing on 300 studies from 2012 to 2025, the survey documents energy efficiency gains of 7 to 29 times, throughput improvements over traditional protocols, and cross layer optimisation benefits of up to 42 percent. It also identifies persistent barriers, including limited datasets, computational constraints, and the gap between theoretical models and real world deployment. The survey concludes with emerging research directions and a technology roadmap supporting ML adoption in operational underwater networks.
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From Thinker to Society: Security in Hierarchical Autonomy Evolution of AI Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaolei Zhang · Lu Zhou · Xiaogang Xu · Jiafei Wu · Tianyu Du · Heqing Huang · Hao Peng · Zhe Liu
arXiv:2603.07496v1 Announce Type: cross Abstract: Artificial Intelligence (AI) agents have evolved from passive predictive tools into active entities capable of autonomous decision-making and environmental interaction, driven by the reasoning capabilities of Large Language Models (LLMs). However, this evolution has introduced critical security vulnerabilities that existing frameworks fail to address. The Hierarchical Autonomy Evolution (HAE) framework organizes agent security into three tiers: Cognitive Autonomy (L1) targets internal reasoning integrity; Execution Autonomy (L2) covers tool-mediated environmental interaction; Collective Autonomy (L3) addresses systemic risks in multi-agent ecosystems. We present a taxonomy of threats spanning cognitive manipulation, physical environment disruption, and multi-agent systemic failures, and evaluate existing defenses while identifying key research gaps. The findings aim to guide the development of multilayered, autonomy-aware defense architectures for trustworthy AI agent systems.
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MetaWorld-X: Hierarchical World Modeling via VLM-Orchestrated Experts for Humanoid Loco-Manipulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yutong Shen · Hangxu Liu · Penghui Liu · Jiashuo Luo · Yongkang Zhang · Rex Morvley · Chen Jiang · Jianwei Zhang · Lei Zhang
arXiv:2603.08572v1 Announce Type: cross Abstract: Learning natural, stable, and compositionally generalizable whole-body control policies for humanoid robots performing simultaneous locomotion and manipulation (loco-manipulation) remains a fundamental challenge in robotics. Existing reinforcement learning approaches typically rely on a single monolithic policy to acquire multiple skills, which often leads to cross-skill gradient interference and motion pattern conflicts in high-degree-of-freedom systems. As a result, generated behaviors frequently exhibit unnatural movements, limited stability, and poor generalization to complex task compositions. To address these limitations, we propose MetaWorld-X, a hierarchical world model framework for humanoid control. Guided by a divide-and-conquer principle, our method decomposes complex control problems into a set of specialized expert policies (Specialized Expert Policies, SEP). Each expert is trained under human motion priors through imitation-constrained reinforcement learning, introducing biomechanically consistent inductive biases that ensure natural and physically plausible motion generation. Building upon this foundation, we further develop an Intelligent Routing Mechanism (IRM) supervised by a Vision-Language Model (VLM), enabling semantic-driven expert composition. The VLM-guided router dynamically integrates expert policies according to high-level task semantics, facilitating compositional generalization and adaptive execution in multi-stage loco-manipulation tasks.
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M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ju Dong · Lei Zhang · Liding Zhang · Yao Ling · Yu Fu · Kaixin Bai · Zolt\'an-Csaba M\'arton · Zhenshan Bing · Zhaopeng Chen · Alois Christian Knoll · Jianwei Zhang
arXiv:2509.14980v2 Announce Type: replace-cross Abstract: Mobile manipulation requires the coordinated control of a mobile base and a robotic arm while simultaneously perceiving both global scene context and fine-grained object details. Existing single-view approaches often fail in unstructured environments due to limited fields of view, exploration, and generalization abilities. Moreover, classical controllers, although stable, struggle with efficiency and manipulability near singularities. To address these challenges, we propose M4Diffuser, a hybrid framework that integrates a Multi-View Diffusion Policy with a novel Reduced and Manipulability-aware QP (ReM-QP) controller for mobile manipulation. The diffusion policy leverages proprioceptive states and complementary camera perspectives with both close-range object details and global scene context to generate task-relevant end-effector goals in the world frame. These high-level goals are then executed by the ReM-QP controller, which eliminates slack variables for computational efficiency and incorporates manipulability-aware preferences for robustness near singularities. Comprehensive experiments in simulation and real-world environments show that M4Diffuser achieves 7 to 56 percent higher success rates and reduces collisions by 3 to 31 percent over baselines. Our approach demonstrates robust performance for smooth whole-body coordination, and strong generalization to unseen tasks, paving the way for reliable mobile manipulation in unstructured environments. Details of the demo and supplemental material are available on our project website https://sites.google.com/view/m4diffuser.
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