JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition

By: Zixiang Chen · Yuheng Lu · Zihao Cheng · Zeming Liu · Jizeng Bai · Ziye Huang · Zhiyin Lin · Zihan Li · Yuhang Guo · Yunhong Wang · Haifeng Wang

10 September 2026 at 12:00

arXiv:2609.10451v1 Announce Type: new Abstract: Real-world GUI usage frequently involves workflows that span multiple devices and platforms, requiring the transfer of intermediate results, maintenance of shared state, and coordination across heterogeneous environments. However, existing GUI benchmarks overwhelmingly evaluate agents on single-device, statically defined tasks, thus leaving such cross-device capabilities largely unexamined, resulting in an overly optimistic assessment of agents' readiness for real-world usage. We introduce JarvisGUI, a dynamic benchmark that evaluates GUI agents on cross-device workflows requiring coordinated interaction across heterogeneous platforms, including Android, Windows, and Ubuntu. Specifically, JarvisGUI formulates GUI tasks as input-output transformations under a lightweight type system, which allows us to automatically compose multi-step, cross-device workflows and dynamically evaluate agent performance within a unified framework. By evaluating agents in virtual environments spanning multiple operating systems, JarvisGUI reveals that state-of-the-art open-source GUI agents struggle with the state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management required for real-world workflows, exposing a critical capability gap invisible to existing benchmarks.

MOSAIC: A Universal Agent-Level Interface for Cross-Paradigm Agent Mixing and Human-AI Collaboration

cs.AI, q-bio.NC updates on arXiv.org

By: Abdulhamid M. Mousa · Rakhmonberdi Khajiev · Jalaledin M. Azzabi · Abdulkarim M. Mousa · Peng Yong · Yunusa Haruna · Ming Liu

10 September 2026 at 12:00

arXiv:2603.01260v2 Announce Type: replace-cross Abstract: Existing infrastructure cannot deploy agents from different decision-making paradigms within the same environment, making fair cross-paradigm comparison under identical conditions impossible. We present MOSAIC, an open-source platform that enables heterogeneous agents (RL policies, LLMs, VLMs, and human operators) to act within shared reinforcement learning environments in ad-hoc team settings with reproducible results. MOSAIC introduces three contributions. (i) IPC-based worker protocol that wraps native and third-party frameworks as isolated subprocess workers, each executing its own training and inference logic unmodified and communicating through a versioned inter-process protocol. (ii) An operator abstraction that forms an agent-level interface by mapping workers to agent slots: each operator, regardless of whether it is backed by an RL policy, an LLM, or a human, conforms to a minimal universal interface. (iii) A deterministic cross-paradigm evaluation framework with two complementary modes: a manual mode that advances up to $N$ operators in lock-step under shared seeds for fine-grained visual inspection of behavioural differences; and a script mode that drives automated, long-running evaluation via declarative Python scripts for reproducible experiments. Our documentation is released at: https://mosaic-platform.readthedocs.io.

Multiomic characterization of malignant pulmonary nodules and development of a methylation-based diagnostic Model

Pulmonary nodule

By: Wei Zhou · Tongji Xie · Qingjun Wu · Mengmeng Song · Jianzhen Weng · Jieli Zhang · Xuan Gao · Min Zheng · Zheng Wang · Yunzhi Zhou · Yanming Li

9 June 2026 at 18:00

J Transl Med. 2026 Jun 8;24(1):776. doi: 10.1186/s12967-026-08382-w.

ABSTRACT

BACKGROUND: The molecular distinction between benign and malignant pulmonary nodules remains a significant diagnostic challenge. While genomic drivers are well studied, multiomic integration of the epigenetic-transcriptional landscape and its translation into noninvasive tools are lacking.

METHODS: We performed a multiomic characterization (genomic, epigenomic, and transcriptomic) of 158 pulmonary nodules. Unsupervised factor analysis integrated these layers to identify core regulatory axes. A 9-gene cell-free DNA (cfDNA) methylation classifier was developed and validated in blood and tissue cohorts.

RESULTS: Genomic profiling revealed EGFR mutations (exclusive to malignant nodules) and MYC amplification as fundamental initiators of malignancy. Multiomic factor analysis (Factor 1) revealed profound genetic‒epigenetic synergy, in which these alterations dictate a permissive methylome, leading to aberrant epigenetic programming of chromatin accessibility, as well as epigenetic-transcriptional effects: hypomethylation at the promoters of cell cycle genes that augments their expression, and hypermethylation at immune related pathways gene loci that silences their transcription. This effect orchestrates formation of proproliferative (E2F target/G2M checkpoint) and "immune-cold" malignant phenotype, characterized by elevated Treg/CD8+ ratios and fibroblast recruitment. Notably, we observed a gradual accumulation of methylation aberrations along the premalignant-to-invasive continuum (adenocarcinoma in situ [AIS]→minimally invasive adenocarcinoma [MIA]→adenocarcinoma [ADC]), identifying progressive epigenetic dysregulation as a hallmark of tumor aggressiveness. Global methylome remodeling drives ADC progression through hypermethylation-mediated silencing of tumor suppressors (RASA3 and PPARG) and hypomethylation-activated oncogenic axes, specifically the GDF15 axis, which independently predict poor survival in patients with lung ADC in the TCGA cohort. We translated these tissue-derived insights into a 9-gene cfDNA methylation classifier, which achieved exceptional diagnostic accuracy across independent cohorts (training AUC = 1.00; test AUC = 0.93; tissue AUC = 0.96). Rooted in the biological "ground truth" of tissue dysregulation, this classifier functions specifically as a functional readout of the core cell cycle and proliferative pathways, offering a robust, noninvasive tool for the biology-informed risk assessment of pulmonary nodules.

CONCLUSIONS: This study delineates an epigenetic-transcriptional regulatory network that drives nodule malignancy. Our findings provide a robust theoretical foundation and a high-performance liquid biopsy tool for the precise, noninvasive diagnosis of pulmonary nodules.

PMID:42260586 | PMC:PMC13274191 | DOI:10.1186/s12967-026-08382-w

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