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

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.

Advances in Radiomics for Immune Checkpoint Inhibitor-related Pneumonitis of Lung Cancer

7 September 2026 at 18:00

Zhongguo Fei Ai Za Zhi. 2026 Jul 20;29(7):540-547. doi: 10.3779/j.issn.1009-3419.2026.101.17.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have significantly improved the prognosis of patients with lung cancer. However, checkpoint inhibitor-related pneumonitis (CIP), as one of the most severe immune-related adverse events, lacks well-defined diagnostic criteria and reliable risk stratification tools. Radiomics enables high-throughput feature extraction from computed tomography images and provides a non-invasive technical approach for the early identification and risk stratification of CIP. This article systematically reviews the recent advances in the application of radiomics to risk prediction, diagnosis and differential diagnosis, and prognostic evaluation of CIP in lung cancer immunotherapy. Furthermore, it explores the value of integrating radiomics with multi-omics data in elucidating the pathogenesis of CIP, as well as the role of explainable artificial intelligence (XAI) in enhancing the clinical trustworthiness of models. .

PMID:42705857 | DOI:10.3779/j.issn.1009-3419.2026.101.17

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

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

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