❌

Normal view

Received — 27 May 2026 ⏭

CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bowen Wang · Dunjie Lu · Junli Wang · Tianyi Bai · Shixuan Liu · Zhipeng Zhang · Haiquan Wang · Hao Hu · Tianbao Xie · Shuai Bai · Dayiheng Liu · Que Shen · Junyang Lin · Tao Yu
26 May 2026 at 12:00
arXiv:2605.25624v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents (CUAs) has been bottlenecked by the scarcity of scalable training data with deterministic rewards. Constructing such data for CUAs requires consistent task instruction, executable environment, and verifiable reward. However, hand-curated benchmarks achieve high reward fidelity but cover few applications and LLM-as-judge-based datasets scale broadly but lack reliable verification. We present CUA-Gym, a scalable pipeline that co-generates task instructions, environment states, and reward functions. Concretely, a Generator agent constructs the initial and golden environment states, and a separate Discriminator agent writes the reward function from the task specification. An orchestrator agent drives the two through iterative rounds upon execution. Generated tuples then pass a final filter combining LLM majority voting and agent rollouts, ensuring quality beyond the per-task adversarial loop. To address the scarcity of training environments, we further synthesize CUA-Gym-Hub, a broad suite of high-fidelity mock web applications grounded in real-world software-use distributions, expanding the scale of CUA RLVR data by magnitude. Using this pipeline, we construct CUA-Gym, a dataset of 32,112 verified RLVR training tuples grounded in 110 environments. Trained with GSPO on CUA-Gym, our CUA-Gym-A3B and CUA-Gym-A17B achieve 62.1% and 72.6% on OSWorld-Verified, outperforming prior open-source CUAs at comparable scales, with performance scaling smoothly in both data volume and environment diversity. The same checkpoints also improve on the held-out WebArena benchmark, indicating transfer beyond the training environments. We will open-source the full synthesis pipeline, dataset, CUA-Gym-Hub environments, and models.
Received — 13 April 2026 ⏭

Establishment and characterization of an immortalized porcine gastric epithelial cell line and identification of NPC1 as a key mediator of aflatoxin B1 toxicity

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Liqi Liu · JianLin Yuan · Jinyi Bai · Yuhan Ma · Kaitao Kong · Xiaofei He · Shuangshuang Chen · Jin Chai · Keren Long · Long Jin · Mingzhou Li · Juan Deng
12 April 2026 at 18:00

Gene. 2026 Apr 9:150160. doi: 10.1016/j.gene.2026.150160. Online ahead of print.

ABSTRACT

Porcine gastric epithelial cells (PGECs) serve as a valuable model for studying the molecular and pathogenic mechanisms of the stomach. However, PGECs face limitations such as isolation challenges, short lifespan, and restricted proliferation. To address this, we established an immortalized PGECs (i-PGECs) to enable in vitro investigation of pathogen infection mechanisms. Primary PGECs were isolated from the acid-secreting glands using stepwise digestion with multiple enzymes (dispase II/collagenase I/hyaluronidase). Immortalization was achieved via lentiviral vectors expressing simian virus 40 large T antigen (SV40T) and human telomerase reverse transcriptase (hTERT), with successful expression confirmed by qRT-PCR (P < 0.05). Epithelial identity of i-PGECs was confirmed by stable expression of CK18, EpCAM, and E-cadherin, as shown by qRT-PCR and immunofluorescence. i-PGECs retained the morphological and ultrastructural features of PGECs and exhibited enhanced proliferation, as demonstrated by WST-8 assays, apoptosis and cell cycle analysis, karyotyping, and transmission electron microscopy (TEM). Telomere length analysis and scratch wound assays demonstrated stable telomere maintenance and consistent migration capacity unaffected by passaging. RNA-sequencing and differential expressed genes (DEGs) analysis revealed significantly upregulating of genes involved in cell proliferation pathways (P < 0.01). Following aflatoxin B1 (AFB1) exposure, i-PGECs significantly upregulated immune-related factors, such as NPC1 and PLAUR (P < 0.01). CRISPR/Cas9-mediated knockout of NPC1 in i-PGECs conferred increased resistance to AFB1-induced cytotoxicity, as shown by WST-8 assay. The i-PGECs remained stable after more than 50 passages, supporting their use as a reliable for in vitro model investigating the mechanisms of toxicity infection in the porcine gastric epithelium.

PMID:41966285 | DOI:10.1016/j.gene.2026.150160

Received — 2 April 2026 ⏭

Cellular Senescence in Gastric Cancer: Molecular Mechanisms, Microenvironment Remodeling and Therapeutic Implications

✇Omics in Gastric
By: Zhiyuan Shi · Zhao Sun · Yuan Liu · Yuping Ge · Ning Jia · Yuwei Hua · Lin Zhao · Xiang Wang · Yi Ba
30 March 2026 at 18:00

Aging Dis. 2026 Mar 19. doi: 10.14336/AD.2025.1571. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a leading cause of cancer-related morbidity and mortality worldwide, with poor prognosis for advanced-stage patients. Therefore, in-depth exploration of the mechanisms underlying GC initiation and progression, as well as the development of novel therapeutic strategies, is of crucial importance. Cellular senescence is a stable cell cycle arrest program that plays a dual role in GC. It exerts tumor-suppressive effects via growth arrest but also promotes tumor progression and immune evasion by remodeling the tumor microenvironment (TME) through senescence-associated secretory phenotype (SASP). This review comprehensively elucidates the molecular mechanisms of cellular senescence in GC and the core regulatory networks involving gene regulation, epigenetic modifications, metabolic reprogramming, and cell cycle arrest. Additionally, the review highlights how senescent cells foster an immunosuppressive microenvironment via SASP, forming a self-reinforcing feed-forward loop. Regarding therapeutic strategies, we summarize potential approaches targeting cellular senescence, including senescence induction, senescent cell clearance, SASP modulation, and multi-target synergistic therapy by integrating epigenetic regulation, metabolic intervention, and immune microenvironment modulation. Despite progress, numerous challenges remain. Future studies should leverage multi-omics technologies, novel models' development, and large-scale clinical trials to advance the clinical translation of GC cellular senescence research, providing new insights for improving prognosis.

PMID:41910653 | DOI:10.14336/AD.2025.1571

Received — 26 March 2026 ⏭

A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chenhan Wang · Zhengyi Bao · Huipin Lin · Jiahao Nie · Chunxiang Zhu
25 March 2026 at 12:00
arXiv:2603.22323v1 Announce Type: cross Abstract: Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and efficient operation of electric vehicles while minimizing associated risks. However, current deep learning methods are limited in their ability to selectively extract features and model time dependencies for these two parameters. Moreover, most existing methods rely on traditional recurrent neural networks, which have inherent shortcomings in long-term time-series modeling. To address these issues, this paper proposes a multi-task targeted learning framework for SOH and RUL prediction, which integrates multiple neural networks, including a multi-scale feature extraction module, an improved extended LSTM, and a dual-stream attention module. First, a feature extraction module with multi-scale CNNs is designed to capture detailed local battery decline patterns. Secondly, an improved extended LSTM network is employed to enhance the model's ability to retain long-term temporal information, thus improving temporal relationship modeling. Building on this, the dual-stream attention module-comprising polarized attention and sparse attention to selectively focus on key information relevant to SOH and RUL, respectively, by assigning higher weights to important features. Finally, a many-to-two mapping is achieved through the dual-task layer. To optimize the model's performance and reduce the need for manual hyperparameter tuning, the Hyperopt optimization algorithm is used. Extensive comparative experiments on battery aging datasets demonstrate that the proposed method reduces the average RMSE for SOH and RUL predictions by 111.3\% and 33.0\%, respectively, compared to traditional and state-of-the-art methods.
Received — 25 February 2026 ⏭

Synthesizing Multimodal Geometry Datasets from Scratch and Enabling Visual Alignment via Plotting Code

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haobo Lin · Tianyi Bai · Chen Chen · Jiajun Zhang · Bohan Zeng · Wentao Zhang · Binhang Yuan
24 February 2026 at 13:00
arXiv:2602.18745v1 Announce Type: cross Abstract: Multimodal geometry reasoning requires models to jointly understand visual diagrams and perform structured symbolic inference, yet current vision--language models struggle with complex geometric constructions due to limited training data and weak visual--symbolic alignment. We propose a pipeline for synthesizing complex multimodal geometry problems from scratch and construct a dataset named \textbf{GeoCode}, which decouples problem generation into symbolic seed construction, grounded instantiation with verification, and code-based diagram rendering, ensuring consistency across structure, text, reasoning, and images. Leveraging the plotting code provided in GeoCode, we further introduce code prediction as an explicit alignment objective, transforming visual understanding into a supervised structured prediction task. GeoCode exhibits substantially higher structural complexity and reasoning difficulty than existing benchmarks, while maintaining mathematical correctness through multi-stage validation. Extensive experiments show that models trained on GeoCode achieve consistent improvements on multiple geometry benchmarks, demonstrating both the effectiveness of the dataset and the proposed alignment strategy. The code will be available at https://github.com/would1920/GeoCode.

TAG: Thinking with Action Unit Grounding for Facial Expression Recognition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haobo Lin · Tianyi Bai · Jiajun Zhang · Xuanhao Chang · Sheng Lu · Fangming Gu · Zengjie Hu · Wentao Zhang
24 February 2026 at 13:00
arXiv:2602.18763v1 Announce Type: cross Abstract: Facial Expression Recognition (FER) is a fine-grained visual understanding task where reliable predictions require reasoning over localized and meaningful facial cues. Recent vision--language models (VLMs) enable natural language explanations for FER, but their reasoning is often ungrounded, producing fluent yet unverifiable rationales that are weakly tied to visual evidence and prone to hallucination, leading to poor robustness across different datasets. We propose TAG (Thinking with Action Unit Grounding), a vision--language framework that explicitly constrains multimodal reasoning to be supported by facial Action Units (AUs). TAG requires intermediate reasoning steps to be grounded in AU-related facial regions, yielding predictions accompanied by verifiable visual evidence. The model is trained via supervised fine-tuning on AU-grounded reasoning traces followed by reinforcement learning with an AU-aware reward that aligns predicted regions with external AU detectors. Evaluated on RAF-DB, FERPlus, and AffectNet, TAG consistently outperforms strong open-source and closed-source VLM baselines while simultaneously improving visual faithfulness. Ablation and preference studies further show that AU-grounded rewards stabilize reasoning and mitigate hallucination, demonstrating the importance of structured grounded intermediate representations for trustworthy multimodal reasoning in FER. The code will be available at https://github.com/would1920/FER_TAG .
There are no more articles
❌