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Intranasal delivery of a vasoactive intestinal peptide-based circRNA vaccine induces systemic and mucosal immunity against RSV in mice

✇Molecular Therapy
By: Junqi Xiang · Jie Xu · Jing Zhu · Bo Yang · Mingling Wan · Jiangyu Cai · Shuying Chen · Yuncheng Wang · Shanrong Yang · Yaokai Wang · Li Lin · Qiqi Zhang · Na Zang · Enmei Liu · Yushun Wan
A vasoactive intestinal peptide (VIP)-based protein carrier self-assembles with respiratory syncytial virus circular RNA vaccines for intranasal delivery, inducing systemic antibodies, mucosal IgA, and Th1-biased protection in mice. This platform offers a protein-guided strategy for respiratory mucosal RNA vaccination and broadens the application of VIP in vaccine delivery.
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Influence-Oriented Personalized Federated Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yue Tan · Guodong Long · Jing Jiang · Chengqi Zhang
arXiv:2410.03315v2 Announce Type: replace-cross Abstract: Federated learning (FL) is a machine learning paradigm where clients with different behaviors and preferences can learn collaboratively without compromising data privacy. Typical FL methods often rely on fixed weighting for parameter aggregation, thereby neglecting the mutual influence among clients. In practice, clients with similar preferences or backgrounds may provide more useful knowledge to each other, which can be leveraged to improve local performance. However, how to quantify such cross-client influence and how to exploit it for personalized aggregation remain underexplored. To address this gap, we propose an influence-oriented Federated learning framework which quantitatively measures Client-level and Class-level Influence to realize adaptive parameter aggregation for each client (FedC^2I for short). Our core idea is to explicitly model the inter-client influence within an FL system via the well-crafted influence vector and influence matrix. Specifically, FedC^2I incorporate influence vectors to quantify client-level influence, enables clients to selectively acquire knowledge from others, and guides the aggregation of feature representation layers. Meanwhile, the influence matrix captures class-level influence in a more fine-grained manner to achieve personalized classifier aggregation. We evaluate the performance of FedC^2I against existing federated learning methods under non-IID settings, and the results demonstrate the superiority of our method in terms of effectiveness, robustness, and interpretability.
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FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haihao Lin · Xiangsheng Huang · Xiao Yang · Weibang Zhou · Yiqi Zhang · Bo Yang · Simin Zeng · Jiawei Yang · Zhengyang Wang · Jiahui Du
arXiv:2606.08653v2 Announce Type: replace-cross Abstract: Action-supervised fine-tuning of vision-language-action (VLA) policies fits demonstrations effectively but constrains only the directions that change predicted actions, leaving visual structure consistent across action-equivalent states free to collapse. We formalize this as residual visual collapse along local action fibers and propose FiberTune, a training-time objective that preserves teacher-structured visual residuals without adding inference-time overhead. FiberTune uses an online action probe to estimate action-predictive feature directions, filters them from intermediate visual-token representations, and aligns the resulting probe-filtered residuals to a frozen visual teacher while regularizing their effective rank. Under identical training conditions, FiberTune improves over task-loss-only fine-tuning in every one of six controlled simulation settings spanning two benchmarks and two architectures (pi_0.5 and OpenVLA-OFT), as well as on physical SO-101 pick-place; representative gains include +10.7 percentage points SR(5) on long-horizon CALVIN ABC-to-D and physical SO-101 task success rising from 72.7% to 78.1%. Residual diagnostics show that these gains coincide with increased probe-filtered residual teacher alignment and effective rank, consistent with the action-fiber motivation.
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Early stage nonsmall cell lung cancer: Toward a risk-adaptive paradigm in the era of biologic precision

✇Omics In Lung
By: Chaoqi Zhang · Xuanyu Gu · Pan Wang · Dongyu Li · Guochao Zhang · Ting Wang · Peng Wu · Wei Rao · Shan Muhammad · Binghua Tan · Yun Che · Mobai Zhu · Qilin Qiao · Fanbo Lai · Bohui Zhao · Kailun Fei · Yue Wu · Zhihui Zhang · Min Dai · Wanqing Chen · Yawei Zhang · Nan Bi · Jie Wang · Jie He

CA Cancer J Clin. 2026 Sep-Oct;76(5):e70100. doi: 10.3322/caac.70100.

ABSTRACT

The clinical landscape of early stage nonsmall cell lung cancer is at transformative crossroads. Driven by the widespread adoption of low-dose computed tomography screening, the frequent detection of ground-glass opacities, and a rising incidence among never-smokers, the diagnostic center of gravity has shifted toward earlier, potentially curable disease. This shift has been accompanied by equally important therapeutic advances, including parenchyma-sparing surgical techniques, minimally invasive platforms enhanced by digital navigation, and the transformative integration of perioperative immunotherapy and targeted agents. Concurrently, noninvasive monitoring approaches, such as liquid biopsy, have emerged as powerful tools to guide precision management. Despite this progress, substantial barriers to achieving a universal cure persist. Clinicians continue to face uncertainty in the management of ground-glass opacities, the anatomy-based TNM staging system fails to capture the biologic heterogeneity of early tumors, and global disparities in access to innovation remain unresolved. To address these challenges, the authors propose a shift toward a risk-adaptive management paradigm that harnesses artificial intelligence-driven analytics and multi-omics profiling to tailor treatment intensity according to each patient's biologic risk. Such an approach would enable appropriate escalation for high-risk individuals while permitting safe de-escalation for those at low risk. This holistic, lifespan-oriented strategy must be embraced to deliver equitable and durable cures for patients with early stage nonsmall cell lung cancer.

PMID:42713910 | PMC:PMC13555834 | DOI:10.3322/caac.70100

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Early stage nonsmall cell lung cancer: Toward a risk-adaptive paradigm in the era of biologic precision

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Chaoqi Zhang · Xuanyu Gu · Pan Wang · Dongyu Li · Guochao Zhang · Ting Wang · Peng Wu · Wei Rao · Shan Muhammad · Binghua Tan · Yun Che · Mobai Zhu · Qilin Qiao · Fanbo Lai · Bohui Zhao · Kailun Fei · Yue Wu · Zhihui Zhang · Min Dai · Wanqing Chen · Yawei Zhang · Nan Bi · Jie Wang · Jie He

CA Cancer J Clin. 2026 Sep-Oct;76(5):e70100. doi: 10.3322/caac.70100.

ABSTRACT

The clinical landscape of early stage nonsmall cell lung cancer is at transformative crossroads. Driven by the widespread adoption of low-dose computed tomography screening, the frequent detection of ground-glass opacities, and a rising incidence among never-smokers, the diagnostic center of gravity has shifted toward earlier, potentially curable disease. This shift has been accompanied by equally important therapeutic advances, including parenchyma-sparing surgical techniques, minimally invasive platforms enhanced by digital navigation, and the transformative integration of perioperative immunotherapy and targeted agents. Concurrently, noninvasive monitoring approaches, such as liquid biopsy, have emerged as powerful tools to guide precision management. Despite this progress, substantial barriers to achieving a universal cure persist. Clinicians continue to face uncertainty in the management of ground-glass opacities, the anatomy-based TNM staging system fails to capture the biologic heterogeneity of early tumors, and global disparities in access to innovation remain unresolved. To address these challenges, the authors propose a shift toward a risk-adaptive management paradigm that harnesses artificial intelligence-driven analytics and multi-omics profiling to tailor treatment intensity according to each patient's biologic risk. Such an approach would enable appropriate escalation for high-risk individuals while permitting safe de-escalation for those at low risk. This holistic, lifespan-oriented strategy must be embraced to deliver equitable and durable cures for patients with early stage nonsmall cell lung cancer.

PMID:42713910 | DOI:10.3322/caac.70100

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Protein glycosylation profiling in lung adenocarcinoma and precursor lesions: analysis of FFPE tissue sections

✇Pulmonary nodule
By: Jinggang Cui · Yan Chen · Shuang Yue · Jianbo Deng · Wenqi Zhang · Shengye Wen · Lingbo Zhao · Rumeng Zhang · Shuang Yang · Junhong Jiang

Anal Bioanal Chem. 2026 Jul 27. doi: 10.1007/s00216-026-06702-z. Online ahead of print.

ABSTRACT

Protein glycosylation is a major post-translational modification that regulates tumor initiation and progression; however, its dynamic modeling during multistep evolution of lung adenocarcinoma (LUAD) remains poorly understood, particularly in clinically archived tissues. Here, we established an integrated multi-omics workflow combining global proteomes, N-glycans, and site-specific intact N-glycopeptides to comprehensively characterize glycosylation in formalin-fixed paraffin-embedded (FFPE) specimens spanning four pathological stages of LUAD progression: inflammatory nodules (IN), atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC). Using optimized protein extraction, hydrophilic interaction liquid chromatography (HILIC)-based glycopeptide enrichment, and high-resolution LC-MS/MS, we achieved large-scale identification of proteins, N-glycans, and intact glycopeptides from archival clinical samples. Integrated analyses revealed progressive remodeling of site-specific N-glycosylation during malignant transformation, characterized by increased glycan branching, fucosylation, and sialylation during the transition from premalignant lesions to invasive cancer. Sialylated glycans reached their highest abundance in the premalignant AAH stage, whereas highly branched and fucosylated complex N-glycans predominated in invasive adenocarcinoma, indicating stage-dependent glycan remodeling throughout disease progression. Functional enrichment analyses linked these glycosylation alterations to extracellular matrix organization, neutrophil degranulation, and immune-associated pathways, while representative glycoproteins, including CEACAM6 and FGB, exhibited coordinated changes in protein abundance and site-specific glycoform micro-heterogeneity across pathological stages. Collectively, this study demonstrates the feasibility of deep glycoproteomic profiling using archived FFPE tissues and provides a comprehensive molecular atlas of glycosylation remodeling during LUAD progression. These findings establish a valuable resource for elucidating disease mechanisms and identifying stage-specific glycosylation biomarkers and potential glycan-targeted therapeutic candidates for early lung adenocarcinoma.

PMID:42509285 | DOI:10.1007/s00216-026-06702-z

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Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hao Yan · Xuanru Wang · Jun Yin · Shirui Pan · Senzhang Wang · Chengqi Zhang
arXiv:2605.24684v1 Announce Type: cross Abstract: Multimodal Attributed Graph Learning (MAGL) integrates intrinsic node attributes with structural topology via graph aggregation. However, as pretrained encoders evolve into Large Foundation Models (LFMs), the landscape of MAGL fundamentally shifts: under high-confidence LFM priors, mandatory aggregation introduces topological noise that overwhelms discriminative signals, triggering a counter-intuitive performance inversion where sophisticated MAGL architectures underperform simple topology-agnostic MLPs. Through systematic empirical and theoretical analysis, we identify that this inversion stems from a fundamental aggregation dilemma characterized by two concurrent pathologies: (1) Representational Pathology (SNR Degradation) - mandatory aggregation dilutes robust intrinsic features with topological noise, causing the noise penalty to outweigh its collaborative benefit; and (2) Optimization Pathology (Gradient Starvation) - topological aggregation attenuates gradient flow, while a shared task loss causes dominant modalities to prematurely suppress weaker ones. To resolve this dilemma, we propose SUPRA (Shared-Unique Prior-Retaining Architecture), a decoupled dual-pathway paradigm. SUPRA processes modality-specific features through topology-agnostic MLPs while capturing structural synergy via a lightweight shared GNN, with auxiliary deep supervision counteracting gradient starvation. Extensive evaluations demonstrate that SUPRA achieves state-of-the-art performance while requiring 3.5x lower peak GPU memory and up to 4.4x faster training time than Multimodal Graph Transformers.
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Profiling-Driven Adaptive Distributed Transformer Inference on Embedded Edge Deployment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Muhammad Azlan Qazi · Alexandros Iosifidis · Qi Zhang
arXiv:2605.25682v1 Announce Type: cross Abstract: Distributing Transformer inference across embedded edge devices can alleviate individual memory and compute constraints, yet practical benefits on real hardware remain unclear: prior work relies largely on simulations that overlook hardware-specific communication overheads. We present a hardware prototype study on NVIDIA Jetson Orin Nano devices connected over WiFi. Our key finding is that the dominant bottleneck is not just network bandwidth but also the CPU-GPU staging during communication. Because Jetson's integrated GPU architecture lacks the PCIe/NVLink pathway that NCCL requires, all inter-device data communication should be routed through GLOO and staged in CPU memory; an overhead that scales with communication data volume and makes full-tensor exchange slower than single-device inference across the batch sizes for medium sized models such as ViT. We therefore evaluate Prism by combining Segment Means compression with lightweight offline profiling to adaptively select between local and distributed execution at runtime. Experiments show that this strategy reduces latency by 65%-77% and energy consumption by 34%-52% relative to full-tensor exchange in static distributed execution setup, demonstrating that profiling-driven adaptation is essential for practical distributed Transformer inference on embedded hardware.
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AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yinyi Luo · Yiqiao Jin · Weichen Yu · Mengqi Zhang · Srijan Kumar · Xiaoxiao Li · Weijie Xu · Xin Chen · Jindong Wang
arXiv:2602.03955v3 Announce Type: replace Abstract: While large language model (LLM) multi-agent systems achieve superior reasoning performance through iterative debate, practical deployment is limited by their high computational cost and error propagation. This paper proposes AgentArk, a novel framework to distill multi-agent dynamics into the weights of a single model, effectively transforming explicit test-time interactions into implicit model capabilities. This equips a single agent with the intelligence of multi-agent systems while remaining computationally efficient. Specifically, we investigate three hierarchical distillation strategies across various models, tasks, scaling, and scenarios: reasoning-enhanced fine-tuning; trajectory-based augmentation; and process-aware distillation. By shifting the burden of computation from inference to training, the distilled models preserve the efficiency of one agent while exhibiting strong reasoning and self-correction performance of multiple agents. They further demonstrate enhanced robustness and generalization across diverse reasoning tasks. We hope this work can shed light on future research on efficient and robust multi-agent development. Our code is at https://github.com/AIFrontierLab/AgentArk.
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiao Li · Xiang Zheng · Yifeng Gao · Xinyu Xia · Yixu Wang · Xin Wang · Ye Sun · Yunhan Zhao · Ming Wen · Jiayu Li · Zixing Chen · Xun Gong · Yi Liu · Yige Li · Yutao Wu · Cong Wang · Jun Sun · Yixin Cao · Zhineng Chen · Jingjing Chen · Tao Gui · Qi Zhang · Zuxuan Wu · Xipeng Qiu · Xuanjing Huang · Tiehua Zhang · Zhipeng Wei · Kun Wang · Xinfeng Li · Hanxun Huang · Sarah Erfani · James Bailey · Jianping Wang · Chaowei Xiao · Ran He · Bo Li · Xingjun Ma · Yu-Gang Jiang
arXiv:2605.02900v2 Announce Type: replace-cross Abstract: Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As these systems gain autonomy and enter domains such as transportation, healthcare, and industrial or assistive robotics, ensuring their safety becomes both technically challenging and socially indispensable. Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions, where failures can directly lead to physical harm. This survey provides a comprehensive and structured review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline, from perception and cognition to planning, action and interaction, and agentic system. We introduce a multi-level taxonomy that unifies fragmented lines of work and connects embodied-specific safety findings with broader advances in vision, language, and multimodal foundation models. Our review synthesizes insights from over 500 papers spanning adversarial, backdoor, jailbreak, and hardware-level attacks; attack detection, safe training and robust inference; and risk-aware human-agent interaction. This analysis reveals several overlooked challenges, including the fragility of multimodal perception fusion, the instability of planning under jailbreak attacks, and the trustworthiness of human-agent interaction in open-ended scenarios. By organizing the field into a coherent framework and identifying critical research gaps, this survey provides a roadmap for building embodied agents that are not only capable and autonomous but also safe, robust, and reliable in real-world deployment.
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PRXL2B facilitates the progression of hepatocellular carcinoma and the therapeutic efficacy of oncolytic adenovirus H101 through the PI3K/AKT/PD-L1 axis

✇Omics in Hepatocellular
By: Ziqi Zhang · Hongli Chen · Yangming Liu · Hao Wang · You Yan · Yanxi Yu · Zuotian Huang · Yanyao Liu · Wei Li · Zhongjun Wu · Meiqing Kang · Xiaoyong Luo · Ping Che · Rui Liao

Biosci Trends. 2026 May 21. doi: 10.5582/bst.2026.01000. Online ahead of print.

ABSTRACT

Oncolytic adenovirus H101 has shown antitumor activity in hepatocellular carcinoma (HCC), but the molecular determinants of treatment response remain unclear. In this study, a Hepa1-6 subcutaneous tumor model was established in C57BL/6 mice and treated with intratumoral H101, followed by integrated transcriptomic and proteomic analyses to identify candidate genes associated with H101 response. PRXL2B was selected for further investigation using public multi-omics datasets, tissue microarray-based immunohistochemistry, in vitro functional assays, mechanistic analyses, and in vivo validation experiments. Integrated multi-omics analyses identified PRXL2B as a candidate gene downregulated after H101 treatment. Public datasets and tissue-based validation further showed that PRXL2B was upregulated in HCC tissues. In MHCC97H and HCCLM3 cells, PRXL2B knockdown inhibited proliferation, migration, and invasion, promoted apoptosis and cell-cycle arrest, and enhanced the antitumor effect of H101. Mechanistically, PRXL2B silencing reduced AKT phosphorylation and PD-L1 expression. In vivo, PRXL2B knockdown suppressed tumor growth, and the combination of PRXL2B knockdown and H101 produced the strongest antitumor effect. These findings indicate that PRXL2B promotes malignant phenotypes in HCC and may modulate H101 efficacy through the PI3K/AKT/PD-L1 axis. Targeting PRXL2B may therefore represent a potential strategy to enhance the therapeutic efficacy of oncolytic virus therapy in HCC.

PMID:42161529 | DOI:10.5582/bst.2026.01000

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GA-GS: Generation-Assisted Gaussian Splatting for Static Scene Reconstruction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yedong Shen · Shiqi Zhang · Sha Zhang · Yifan Duan · Xinran Zhang · Wenhao Yu · Lu Zhang · Jiajun Deng · Yanyong Zhang
arXiv:2604.04331v1 Announce Type: cross Abstract: Reconstructing static 3D scene from monocular video with dynamic objects is important for numerous applications such as virtual reality and autonomous driving. Current approaches typically rely on background for static scene reconstruction, limiting the ability to recover regions occluded by dynamic objects. In this paper, we propose GA-GS, a Generation-Assisted Gaussian Splatting method for Static Scene Reconstruction. The key innovation of our work lies in leveraging generation to assist in reconstructing occluded regions. We employ a motion-aware module to segment and remove dynamic regions, and thenuse a diffusion model to inpaint the occluded areas, providing pseudo-ground-truth supervision. To balance contributions from real background and generated region, we introduce a learnable authenticity scalar for each Gaussian primitive, which dynamically modulates opacity during splatting for authenticity-aware rendering and supervision. Since no existing dataset provides ground-truth static scene of video with dynamic objects, we construct a dataset named Trajectory-Match, using a fixed-path robot to record each scene with/without dynamic objects, enabling quantitative evaluation in reconstruction of occluded regions. Extensive experiments on both the DAVIS and our dataset show that GA-GS achieves state-of-the-art performance in static scene reconstruction, especially in challenging scenarios with large-scale, persistent occlusions.
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Protective Effects of the Ethyl Acetate Fraction from Madeng'ai on Lipopolysaccharide-Induced Acute Lung Injury in Mice: Insights from Integrated Multi-Omics Analysis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Ziyang Zhang · Tianyi Zhu · Liang Cao · Yan Wang · Xiao-Liang Xing · Zaiqi Zhang

J Ethnopharmacol. 2026 Apr 4:121650. doi: 10.1016/j.jep.2026.121650. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Madeng'ai (MDA) is a traditional medicinal plant of the Dong ethnic group. Its roots have been widely used in folk medicine for clearing heat and removing toxins, alleviating swelling and relieving pain, dispersing blood stasis and arresting bleeding, as well as promoting wound healing. It is taxonomically classified as a variety of Potentilla freyniana Bornm.

AIM OF THE STUDY: Acute lung injury (ALI) is a life-threatening pulmonary disorder associated with high mortality, underscoring the urgent need to explore novel therapeutic strategies. This study aimed to evaluate the protective effects of the ethyl acetate fraction of MDA (MEA) against LPS-induced ALI in mice and to investigate its underlying mechanisms.

MATERIALS AND METHODS: LC-MS/MS was employed to tentatively identify the bioactive components of MEA. A mouse model of ALI was established by LPS induction. The protective effects of MEA were evaluated through assessments of lung histopathology, inflammatory cytokine levels, and oxidative stress markers. The underlying mechanisms were systematically investigated by integrating transcriptomics, metabolomics, network pharmacology, molecular docking, and Western blotting.

RESULTS: MEA significantly attenuated LPS-induced pulmonary pathological lesions, pulmonary edema, and excessive inflammatory responses in ALI mice. Comprehensive bioinformatics analyses predicted potential mechanisms involving oxidative stress and the regulation of metabolic pathways. Experimental validation via Western blotting confirmed that MEA inhibited TLR4-mediated inflammatory signaling and modulated the PI3K/AKT pathway, thereby exerting multi-pathway protective effects against ALI.

CONCLUSIONS: Collectively, this study confirms that MEA, as a traditional herbal extract, holds potential as an adjuvant therapeutic agent for ALI, providing experimental evidence for the modernization and development of ethnic medicines.

PMID:41941987 | DOI:10.1016/j.jep.2026.121650

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Protective Effects of the Ethyl Acetate Fraction from Madeng'ai on Lipopolysaccharide-Induced Acute Lung Injury in Mice: Insights from Integrated Multi-Omics Analysis

✇Omics In Lung
By: Ziyang Zhang · Tianyi Zhu · Liang Cao · Yan Wang · Xiao-Liang Xing · Zaiqi Zhang

J Ethnopharmacol. 2026 Apr 4:121650. doi: 10.1016/j.jep.2026.121650. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Madeng'ai (MDA) is a traditional medicinal plant of the Dong ethnic group. Its roots have been widely used in folk medicine for clearing heat and removing toxins, alleviating swelling and relieving pain, dispersing blood stasis and arresting bleeding, as well as promoting wound healing. It is taxonomically classified as a variety of Potentilla freyniana Bornm.

AIM OF THE STUDY: Acute lung injury (ALI) is a life-threatening pulmonary disorder associated with high mortality, underscoring the urgent need to explore novel therapeutic strategies. This study aimed to evaluate the protective effects of the ethyl acetate fraction of MDA (MEA) against LPS-induced ALI in mice and to investigate its underlying mechanisms.

MATERIALS AND METHODS: LC-MS/MS was employed to tentatively identify the bioactive components of MEA. A mouse model of ALI was established by LPS induction. The protective effects of MEA were evaluated through assessments of lung histopathology, inflammatory cytokine levels, and oxidative stress markers. The underlying mechanisms were systematically investigated by integrating transcriptomics, metabolomics, network pharmacology, molecular docking, and Western blotting.

RESULTS: MEA significantly attenuated LPS-induced pulmonary pathological lesions, pulmonary edema, and excessive inflammatory responses in ALI mice. Comprehensive bioinformatics analyses predicted potential mechanisms involving oxidative stress and the regulation of metabolic pathways. Experimental validation via Western blotting confirmed that MEA inhibited TLR4-mediated inflammatory signaling and modulated the PI3K/AKT pathway, thereby exerting multi-pathway protective effects against ALI.

CONCLUSIONS: Collectively, this study confirms that MEA, as a traditional herbal extract, holds potential as an adjuvant therapeutic agent for ALI, providing experimental evidence for the modernization and development of ethnic medicines.

PMID:41941987 | DOI:10.1016/j.jep.2026.121650

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Captioning Daily Activity Images in Early Childhood Education: Benchmark and Algorithm

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sixing Li · Zhibin Gu · Ziqi Zhang · Weiguo Pan · Bing Li · Ying Wang · Hongzhe Liu
arXiv:2604.01941v1 Announce Type: cross Abstract: Image captioning for Early Childhood Education (ECE) is essential for automated activity understanding and educational assessment. However, existing methods face two key challenges. First, the lack of large-scale, domain-specific datasets limits the model's ability to capture fine-grained semantic concepts unique to ECE scenarios, resulting in generic and imprecise descriptions. Second, conventional training paradigms exhibit limitations in enhancing professional object description capability, as supervised learning tends to favor high-frequency expressions, while reinforcement learning may suffer from unstable optimization on difficult samples. To address these limitations, we introduce ECAC, a large-scale benchmark for ECE daily activity image captioning, comprising 256,121 real-world images annotated with expert-level captions and fine-grained labels. ECAC is further equipped with a domain-oriented evaluation protocol, the Teaching Toy Recognition Score (TTS), to explicitly measure professional object naming accuracy. Furthermore, we propose RSRS (Reward-Conditional Switch of Reinforcement Learning and Supervised Fine-Tuning), a hybrid training framework that dynamically alternates between RL and supervised optimization. By rerouting hard samples with zero rewards to supervised fine-tuning, RSRS effectively mitigates advantage collapse and enables stable optimization for fine-grained recognition. Leveraging ECAC and RSRS, we develop KinderMM-Cap-3B, a domain-adapted multimodal large language model. Extensive experiments demonstrate that our model achieves a TTS of 51.06, substantially outperforming state-of-the-art baselines while maintaining superior caption quality, highlighting its potential for specialized educational applications.
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JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuhui Wang · Zhixiong Yang · Ming Zhang · Shihan Dou · Zhiheng Xi · Enyu Zhou · Senjie Jin · Yujiong Shen · Dingwei Zhu · Yi Dong · Tao Gui · Qi Zhang · Xuanjing Huang
arXiv:2603.22978v1 Announce Type: new Abstract: In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by large language models, which can assist in tracking and analyzing malfunctions, we propose a novel textual representation of fault trees. Building on it, we construct a benchmark for multi-turn dialogue systems that emphasizes robust interaction in complex environments, evaluating a model's ability to assist in malfunction localization, which contains $3130$ entries and $40.75$ turns per entry on average. We train an end-to-end model to generate vague information to reflect user behavior and introduce long-range rollback and recovery procedures to simulate user error scenarios, enabling assessment of a model's integrated capabilities in task tracking and error recovery, and Gemini 2.5 pro archives the best performance.
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SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiqi Zhang · Huiqiang Jiang · Xufang Luo · Zhihe Yang · Chengruidong Zhang · Yifei Shen · Dongsheng Li · Yuqing Yang · Lili Qiu · Yang You
arXiv:2603.23414v1 Announce Type: cross Abstract: Scaling reinforcement learning (RL) has shown strong promise for enhancing the reasoning abilities of large language models (LLMs), particularly in tasks requiring long chain-of-thought generation. However, RL training efficiency is often bottlenecked by the rollout phase, which can account for up to 70% of total training time when generating long trajectories (e.g., 16k tokens), due to slow autoregressive generation and synchronization overhead between rollout and policy updates. We propose SortedRL, an online length-aware scheduling strategy designed to address this bottleneck by improving rollout efficiency and maintaining training stability. SortedRL reorders rollout samples based on output lengths, prioritizing short samples forming groups for early updates. This enables large rollout batches, flexible update batches, and near on-policy micro-curriculum construction simultaneously. To further accelerate the pipeline, SortedRL incorporates a mechanism to control the degree of off-policy training through a cache-based mechanism, and is supported by a dedicated RL infrastructure that manages rollout and update via a stateful controller and rollout buffer. Experiments using LLaMA-3.1-8B and Qwen-2.5-32B on diverse tasks, including logical puzzles, and math challenges like AIME 24, Math 500, and Minerval, show that SortedRL reduces RL training bubble ratios by over 50%, while attaining 3.9% to 18.4% superior performance over baseline given same amount of data.
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SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jingxiao Yang · DaLin He · Miao Pan · Ge Su · Wenqi Zhang · Yifeng Hu · Tangwei Li · Yuke Li · Xuhong Zhang
arXiv:2603.17729v2 Announce Type: replace-cross Abstract: Recent advances in Large Vision-Language Models (LVLMs) have enabled training-free Fine-Grained Visual Recognition (FGVR). However, effectively exploiting LVLMs for FGVR remains challenging due to the inherent visual ambiguity of subordinate-level categories. Existing methods predominantly adopt either retrieval-oriented or reasoning-oriented paradigms to tackle this challenge, but both are constrained by two fundamental limitations:(1) They apply the same inference pipeline to all samples without accounting for uneven recognition difficulty, thereby leading to suboptimal accuracy and efficiency; (2) The lack of mechanisms to consolidate and reuse error-specific experience causes repeated failures on similar challenging cases. To address these limitations, we propose SARE, a Sample-wise Adaptive textbfREasoning framework for training-free FGVR. Specifically, SARE adopts a cascaded design that combines fast candidate retrieval with fine-grained reasoning, invoking the latter only when necessary. In the reasoning process, SARE incorporates a self-reflective experience mechanism that leverages past failures to provide transferable discriminative guidance during inference, without any parameter updates. Extensive experiments across 14 datasets substantiate that SARE achieves state-of-the-art performance while substantially reducing computational overhead.
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