Normal view
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cs.AI, q-bio.NC updates on arXiv.org
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Do Phone-Use Agents Respect Your Privacy?
arXiv:2604.00986v2 Announce Type: replace-cross Abstract: We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operationalized for phone-use agents, and ordinary apps do not reveal exactly what data agents type into which form entries during execution. To make this question measurable, we introduce MyPhoneBench, a verifiable evaluation framework for privacy behavior in mobile a
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Nature Cancer
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PRET is a few-shot system for pan-cancer recognition without example training
Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-026-01141-2Li et al. present PRET, a few-shot system for pan-cancer detection not requiring model fine-tuning, validated it in multicenter datasets and found that it outperformed existing approaches across tasks and pathologists in lymph node metastasis detection.
PRET is a few-shot system for pan-cancer recognition without example training
Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-026-01141-2
Li et al. present PRET, a few-shot system for pan-cancer detection not requiring model fine-tuning, validated it in multicenter datasets and found that it outperformed existing approaches across tasks and pathologists in lymph node metastasis detection.-
Cell
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Metabolite-gated vascular contractility switch: OXGR1 activation mechanism enables agonist therapy for rosacea erythema
Xiao et al. identify α-KG as a rosacea-associated metabolite that activates the OXGR1-Gq-MYL9 axis in the vascular smooth muscle cells to boost contractility and suppress pathological vasodilation underlying erythema. Cryo-EM reveals a bipartite-acid pocket of OXGR1 that enables structure-guided development of A-1, a selective agonist that alleviates erythema in rosacea-like models.
Metabolite-gated vascular contractility switch: OXGR1 activation mechanism enables agonist therapy for rosacea erythema
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Cell
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Editing strigolactone hormone receptor for robust antiviral silencing in rice
Precise genome editing of the rice strigolactone receptor DWARF14 confers robust, transgene-free antiviral resistance by blocking viral suppression of endogenous RNA silencing, offering a promising strategy for durable disease protection without a yield penalty.
Editing strigolactone hormone receptor for robust antiviral silencing in rice
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cs.AI, q-bio.NC updates on arXiv.org
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GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification
arXiv:2603.29112v1 Announce Type: new Abstract: We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike traditional RecSys benchmarks that focus on item prediction accuracy, our benchmark evaluates how well LLMs can extract and verify user interests from engagement data. We propose two novel metric families: Interest Groundedness (IG), decomposed into precision and recall comp
GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification
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cs.AI, q-bio.NC updates on arXiv.org
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Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model
arXiv:2603.29176v1 Announce Type: new Abstract: Parkinson's disease (PD) affects over ten million people worldwide. Although temporal interference (TI) and deep brain stimulation (DBS) are promising therapies, inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost. Previous explorations either resort to limited statistical biomarkers that are insufficient to characterize variability, or employ AI-driven methods which is prone to overf
Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model
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cs.AI, q-bio.NC updates on arXiv.org
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C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving
arXiv:2603.29908v1 Announce Type: new Abstract: Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM outputs are inherently unreliable, posing risks in safety-critical applications. We propose C-TRAIL, a framework built on a Commonsense World that couples LLM-derived commonsense with a trust mechanism to guide trajectory planning. C-TRAIL operates through a closed-loop Recall, Plan, and Update cycle: the Recall module
C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving
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cs.AI, q-bio.NC updates on arXiv.org
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Time is Not Compute: Scaling Laws for Wall-Clock Constrained Training on Consumer GPUs
arXiv:2603.28823v1 Announce Type: cross Abstract: Scaling laws relate model quality to compute budget (FLOPs), but practitioners face wall-clock time constraints, not compute budgets. We study optimal model sizing under fixed time budgets from 5 minutes to 24 hours on consumer GPUs (RTX 4090). Across 70+ runs spanning 50M--1031M parameters, we find: (1)~at each time budget a U-shaped curve emerges where too-small models overfit and too-large models undertrain; (2)~optimal model size follows $N^
Time is Not Compute: Scaling Laws for Wall-Clock Constrained Training on Consumer GPUs
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cs.AI, q-bio.NC updates on arXiv.org
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Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
arXiv:2603.29148v1 Announce Type: cross Abstract: Graph Convolutional Network (GCN) is a model that can effectively handle graph data tasks and has been successfully applied. However, for large-scale graph datasets, GCN still faces the challenge of high computational overhead, especially when the number of convolutional layers in the graph is large. Currently, there are many advanced methods that use various sampling techniques or graph coarsening techniques to alleviate the inconvenience cause
Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
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Nature - Issue - nature.com science feeds
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Electric dipole moment drives the dynamics of the TNFR1 complex I signalosome
Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10304-1Long-range interactions mediated by protein electric dipole moments have a role in driving the assembly and disassembly of super-signalling complex I for promoting NF-κB signalling.
Electric dipole moment drives the dynamics of the TNFR1 complex I signalosome
Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10304-1
Long-range interactions mediated by protein electric dipole moments have a role in driving the assembly and disassembly of super-signalling complex I for promoting NF-κB signalling.-
cs.AI, q-bio.NC updates on arXiv.org
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SynLeaF: A Dual-Stage Multimodal Fusion Framework for Synthetic Lethality Prediction Across Pan- and Single-Cancer Contexts
arXiv:2603.22369v1 Announce Type: cross Abstract: Accurate prediction of synthetic lethality (SL) is important for guiding the development of cancer drugs and therapies. SL prediction faces significant challenges in the effective fusion of heterogeneous multi-source data. Existing multimodal methods often suffer from "modality laziness" due to disparate convergence speeds, which hinders the exploitation of complementary information. This is also one reason why most existing SL prediction models
SynLeaF: A Dual-Stage Multimodal Fusion Framework for Synthetic Lethality Prediction Across Pan- and Single-Cancer Contexts
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Cell Death Discovery nature.com science feeds
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p63 in skin homeostasis and disease: molecular mechanisms and therapeutic potentials
Cell Death Discovery, Published online: 24 March 2026; doi:10.1038/s41420-026-03060-8p63 in skin homeostasis and disease: molecular mechanisms and therapeutic potentials
p63 in skin homeostasis and disease: molecular mechanisms and therapeutic potentials
Cell Death Discovery, Published online: 24 March 2026; doi:10.1038/s41420-026-03060-8
p63 in skin homeostasis and disease: molecular mechanisms and therapeutic potentials-
Omics in Hepatocellular
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Hypoxia-related and immune phenotype-related fusion model for non-invasive prognostication of hepatocellular carcinoma treated by TACE: a multicentre study
Gut. 2026 Mar 30:gutjnl-2025-337938. doi: 10.1136/gutjnl-2025-337938. Online ahead of print.ABSTRACTBACKGROUND: Survival outcomes after transarterial chemoembolisation (TACE) vary in hepatocellular carcinoma (HCC) patients, and existing prognostic scores and imaging models often lack generalisability and biological interpretability.OBJECTIVE: To develop and validate a multimodal prognostication model for HCC that allows for a precise assessment of survival outcomes of HCC patients receiving TACE
Hypoxia-related and immune phenotype-related fusion model for non-invasive prognostication of hepatocellular carcinoma treated by TACE: a multicentre study
Gut. 2026 Mar 30:gutjnl-2025-337938. doi: 10.1136/gutjnl-2025-337938. Online ahead of print.
ABSTRACT
BACKGROUND: Survival outcomes after transarterial chemoembolisation (TACE) vary in hepatocellular carcinoma (HCC) patients, and existing prognostic scores and imaging models often lack generalisability and biological interpretability.
OBJECTIVE: To develop and validate a multimodal prognostication model for HCC that allows for a precise assessment of survival outcomes of HCC patients receiving TACE therapy.
DESIGN: This study enrolled 1448 HCC patients, including a TACE cohort (n=1349), a biomarker subset from a randomised trial (n=41), a single-cell RNA sequencing cohort and The Cancer Genome Atlas (TCGA) HCC cohort (n=50). Pre-treatment contrast-enhanced CT images were used to construct deep learning and conventional radiomic models. The early-fusion and late-fusion models (LFMs) were compared, and a clinical-radiologic model (CRM) was formed by integrating the better-performing LFM with clinical variables. Using TCGA data and single-cell transcriptomic profiles, the differences between high-score and low-score groups in tumour immune microenvironment, cellular functional states and key signalling pathways were investigated.
RESULTS: The CRM effectively stratified patients' survival across multiple independent cohorts and achieved more granular risk stratification than the existing clinical models. Multi-omic analyses revealed that in the LFM high-score group, myelocytomatosis oncogene was activated, epithelial-mesenchymal transition enhanced, glycolysis upregulated and hypoxia pathway activated. Single-cell transcriptomic data confirmed that virtually all cell types in high-risk patients scored high in hypoxia, and cytotoxic T cells had a reduced cytotoxic activity.
CONCLUSION: The CRM model can non-invasively predict the prognosis of HCC patients treated by TACE therapy.
PMID:41856522 | DOI:10.1136/gutjnl-2025-337938
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cs.AI, q-bio.NC updates on arXiv.org
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SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
arXiv:2602.12670v3 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment LLM agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark of 86 tasks across 11 domains paired with curated Skills and deterministic verifiers. Each task is evaluated under three conditions: no Skills, curated Skills, and self-generated Skills. We test 7 agent-model configurat
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
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cs.AI, q-bio.NC updates on arXiv.org
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Towards AI Search Paradigm
arXiv:2506.17188v2 Announce Type: replace-cross Abstract: In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular architecture of four LLM-powered agents (Master, Planner, Executor and Writer) that dynamically adapt to the full spectrum of information needs, from simple factual queries to complex multi-stage reasoning tasks. These agents
Towards AI Search Paradigm
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cs.AI, q-bio.NC updates on arXiv.org
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VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos
arXiv:2602.07801v3 Announce Type: replace-cross Abstract: In long-video understanding, conventional uniform frame sampling often fails to capture key visual evidence, leading to degraded performance and increased hallucinations. To address this, recent agentic thinking-with-videos paradigms have emerged, adopting a localize-clip-answer pipeline in which the model actively identifies relevant video segments, performs dense sampling within those clips, and then produces answers. However, existing
VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos
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Pulmonary nodule
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Profiling of the mycobiome and metabolome: a comparative study of benign pulmonary nodules and lung adenocarcinoma
Front Cell Infect Microbiol. 2026 Feb 23;16:1732958. doi: 10.3389/fcimb.2026.1732958. eCollection 2026.ABSTRACTINTRODUCTION: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, is a form of malignant pulmonary nodule that requires clinical differentiation from benign pulmonary nodules (BPN). The mechanisms underlying the development of LUAD are complex, and effective non-invasive methods for differentiating BPN from LUAD are lacking. This study aimed not only to di
Profiling of the mycobiome and metabolome: a comparative study of benign pulmonary nodules and lung adenocarcinoma
Front Cell Infect Microbiol. 2026 Feb 23;16:1732958. doi: 10.3389/fcimb.2026.1732958. eCollection 2026.
ABSTRACT
INTRODUCTION: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, is a form of malignant pulmonary nodule that requires clinical differentiation from benign pulmonary nodules (BPN). The mechanisms underlying the development of LUAD are complex, and effective non-invasive methods for differentiating BPN from LUAD are lacking. This study aimed not only to distinguish BPN from LUAD using gut fungi and serum metabolites, but also to establish an integrated network of gut fungi-metabolite-cytokine interactions.
METHODS: Fecal and serum samples from individuals with BPN and patients with LUAD were subjected to internal transcribed spacer sequencing, ultra-performance liquid chromatography-tandem mass spectrometry, and multiplex Luminex assays to quantify gut fungi, metabolites, and cytokines, respectively.
RESULTS: A significant difference in gut fungal communities was observed between the BPN and LUAD groups. Multiple genera and species were more abundant in LUAD than in BPN. Docosapentaenoic acid n-6 (DPAn-6), indole-3-propionic acid (IPA), and interferon-γ-induced protein 10 (IP-10) were significantly elevated in the LUAD group. The integrated model established using a combination of gut fungi and metabolites demonstrated excellent performance in distinguishing BPN from LUAD. A network of interactions was established among differentially abundant gut fungi, serum metabolites, and cytokines.
CONCLUSION: Our study identifies a novel panel of fungal and metabolite biomarkers for differentiating between BPN and LUAD, and constructs a multi-omics network that provides new insights into investigating the mechanistic role of gut mycobiota dysbiosis in LUAD.
PMID:41809995 | PMC:PMC12968269 | DOI:10.3389/fcimb.2026.1732958
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cs.AI, q-bio.NC updates on arXiv.org
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AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation
arXiv:2603.07427v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costly, while LLM-based simulators are scalable but suffer from logic hallucination. We present AutoControl Arena, an automated framework for frontier AI risk evaluation built on the principle of logic-narrative decoupling. By grounding deterministic state in executable code while delegating generative dyna
AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation
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cs.AI, q-bio.NC updates on arXiv.org
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Large Language Model for Discrete Optimization Problems: Evaluation and Step-by-step Reasoning
arXiv:2603.07733v1 Announce Type: new Abstract: This work investigated the capabilities of different models, including the Llama-3 series of models and CHATGPT, with different forms of expression in solving discrete optimization problems by testing natural language datasets. In contrast to formal datasets with a limited scope of parameters, our dataset included a variety of problem types in discrete optimization problems and featured a wide range of parameter magnitudes, including instances wit
Large Language Model for Discrete Optimization Problems: Evaluation and Step-by-step Reasoning
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Journal of Medical Internet Research
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eHealth Literacy and Type 2 Diabetes Prevention Among At-Risk Populations: Mechanistic Systematic Review Using Theory-Driven Thematic Analysis
Background: Type 2 diabetes (T2D) is emerging as a growing global public health crisis. Early and effective interventions can reduce T2D incidence among at-risk populations. Compared with traditional approaches, digital health technologies offer promising opportunities for prevention, with eHealth literacy (eHL) emerging as a critical determinant of digital prevention outcomes. Objective: This systematic review aims to synthesize and explain the pathways and mechanisms through which eHL supports