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Received — 27 May 2026 ⏭

SimuWoB: Simulating Real-World Mobile Apps for Fast and Faithful GUI Agent Benchmarking

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guohong Liu · Jialei Ye · Pengzhi Gao · Wei Liu · Jian Luan · Yunxin Liu · Yuanchun Li
26 May 2026 at 12:00
arXiv:2605.25160v1 Announce Type: new Abstract: Mobile GUI agents powered by large language models have progressed rapidly, creating urgent needs for realistic and comprehensive evaluation. Existing benchmarks prioritize reproducibility but are often limited to open-source apps or file-operation tasks for the difficulty of constructing rewards on real applications, leaving a gap between benchmark settings and real-world usage. Moreover, most benchmarks focus on basic grounding and navigation, with limited coverage of complex, long-horizon interactions. To address these limitations, we introduce SimuWoB, a fully synthetic benchmark for mobile GUI agents with 120 challenging tasks spanning diverse types and difficulty levels. We build a robust virtual environment generation framework that synthesizes high-fidelity tasks and environments, and automatically provides valid rewards for each task. Each environment is deployed as a backend-free webpage accessible via URL, enabling efficient and reproducible evaluation. We conduct comprehensive experiments on several state-of-the-art mobile GUI agents. The average success rate is only 27.92%, dropping to 17.82% on long-horizon tasks, which reveals substantial weaknesses in current agents under complex scenarios. Evaluation result comparison with real-world sample tasks demonstrate that agent assessments based on our synthetic environment generalize well. We further provide diagnostic insights across key capability dimensions and discuss implications for future mobile GUI agent development.

SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiawei He · Mengyu Shi · Jiawei Liu · Zhijie Wang · Chunrong Fang · Xikai Yang · Zhenyu Chen
26 May 2026 at 12:00
arXiv:2605.23440v2 Announce Type: replace-cross Abstract: Joint Entity and Relation Extraction (JERE) is highly susceptible to weak generalization due to low-quality training data. Data augmentation is a common strategy to enhance model generalization across different domains. However, existing data augmentation methods often overlook text relevance and may disrupt semantic structures and dependencies, making it difficult to generate effective augmented data for improving model generalization. In this paper, we propose Structured Semantic Data Augmentation (SSDAU), a novel method designed to preserve the semantic structure of text during augmentation. SSDAU segments text based on entity labels and employs an encoder to capture semantic features of entities through context awareness. It then performs entity semantic restructuring to generate augmented data. To distinguish semantically similar entities, SSDAU fuses contextualized embeddings with traditional similarity scores. To mitigate potential topic ambiguity and information loss, we apply the BERTTopic model to filter out irrelevant topics, ensuring topic consistency. We evaluate SSDAU on datasets with different annotation types and compare its performance on five representative JERE models against seven popular data augmentation baselines. Experiments demonstrate that SSDAU generates semantically consistent data with superior robustness against ambiguity (8.26% F1 decrease vs. 31.91% for baselines), significantly outperforming all existing methods across all metrics.

Clinical and translational roles of circulating tumor cells in non-small cell and small cell lung cancer: a narrative review

✇MRD
By: Wei Liu · Aliss T C Chang · Joyce W Y Chan · Junko C S Chan · Clarence H W Chan · Tony S K Mok · Rainbow W H Lau · Molly S C Li · Calvin S H Ng
25 May 2026 at 18:00

J Thorac Dis. 2026 Apr 30;18(4):415. doi: 10.21037/jtd-2026-1-0025. Epub 2026 Apr 24.

ABSTRACT

BACKGROUND AND OBJECTIVE: Circulating tumor cells (CTCs) are malignant cells shed into blood that enable noninvasive, longitudinal assessment of lung cancer. Increasing evidence frames CTCs within a circulating tumor microenvironment (cTME) and broader circulating tumor-associated cell (CTAC) ecosystems that include multicellular clusters and circulating tumor endothelial cells (CTECs). We summarize definitions, detection approaches, and clinical applications of CTC-centered liquid biopsy in non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC).

METHODS: A comprehensive literature search was conducted in PubMed, Embase, Web of Science, and Google Scholar using the terms "non-small cell lung cancer", "small cell lung cancer", and "circulating tumor cells". Relevant clinical, basic, and translational studies were selected and synthesized to outline current knowledge and future directions.

KEY CONTENT AND FINDINGS: CTCs can be enriched by immunoaffinity, size, or microfluidic platforms, enabling enumeration and downstream profiling. In both NSCLC and SCLC, CTC positivity and higher burden are associated with worse survival, with the strongest effects in SCLC and with circulating tumor emboli (CTE). Serial monitoring provides early signals of response or failure; and post-treatment supports minimal residual disease (MRD) detection and relapse prediction. Molecular and phenotypic profiling enables driver and resistance tracking, including epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), while CTECs may add vascular and immune-relevant information.

CONCLUSIONS: CTC-based assays have the potential to complement imaging and tissue biopsy across screening research, prognostication, therapeutic monitoring, MRD assessment, and personalized care. Clinical translation requires standardized preanalytical workflows, harmonized thresholds, and prospective trials testing CTC-guided management.

PMID:42182710 | PMC:PMC13190041 | DOI:10.21037/jtd-2026-1-0025

Machine learning-driven multi-omics integration uncovers a senescence associated molecular axis in HCC

✇Omics in Hepatocellular
By: Talaiti Tuergan · Aimitaji Abulaiti · Yierfan Yilihaer · Bingwei Liu · Yingmei Shao · Tiemin Jiang · Tuerganaili Aji
25 May 2026 at 18:00

Front Immunol. 2026 May 8;17:1762222. doi: 10.3389/fimmu.2026.1762222. eCollection 2026.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) exhibits profound molecular heterogeneity and aberrant cellular senescence. This study systematically dissects the senescence-associated molecular landscape to identify key regulators driving HCC progression and immune evasion.

METHODS: Integrating multi-cohort transcriptomic datasets, we developed a robust prognostic signature using 101 machine-learning models, identifying prognostic signature. We employed preliminary proteomic, exploratory metabolomic, and single-cell RNA sequencing (scRNA-seq) analyses to explore multi-omics alterations. The functional senescence status and MCM7 were validated in a clinical HCC cohort by RT-qPCR, Western blotting, immunohistochemistry, and multiplex immunofluorescence (mIF). Causality was established using in vitro functional assays in HepG2 cells.

RESULTS: A 12-gene random survival forest (RSF) signature accurately predicted patient survival across independent cohorts. MCM7 emerged as a central senescence-associated driver. ScRNA-seq and mIF confirmed MCM7 characterizes a highly proliferative, clonally expanding subset of CD8+ T cells within the tumor microenvironment. In vitro, MCM7 knockdown significantly inhibited HepG2 cell proliferation and upregulated senescence enforcers p16 and p21, whereas overexpression facilitated evasion. Additionally, TIDE analysis revealed that high-risk patients exhibited elevated immune evasion potential, predicting poor immunotherapy response.

CONCLUSION: This integrative multi-omics framework uncovers an MCM7 MCM7-driven senescence-associated axis promising HCC progression and immune dysfunction, offering a robust tool for prognostic stratification and novel therapeutic insights.

PMID:42183188 | PMC:PMC13195000 | DOI:10.3389/fimmu.2026.1762222

Machine learning-driven multi-omics integration uncovers a senescence associated molecular axis in HCC

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Talaiti Tuergan · Aimitaji Abulaiti · Yierfan Yilihaer · Bingwei Liu · Yingmei Shao · Tiemin Jiang · Tuerganaili Aji
25 May 2026 at 18:00

Front Immunol. 2026 May 8;17:1762222. doi: 10.3389/fimmu.2026.1762222. eCollection 2026.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) exhibits profound molecular heterogeneity and aberrant cellular senescence. This study systematically dissects the senescence-associated molecular landscape to identify key regulators driving HCC progression and immune evasion.

METHODS: Integrating multi-cohort transcriptomic datasets, we developed a robust prognostic signature using 101 machine-learning models, identifying prognostic signature. We employed preliminary proteomic, exploratory metabolomic, and single-cell RNA sequencing (scRNA-seq) analyses to explore multi-omics alterations. The functional senescence status and MCM7 were validated in a clinical HCC cohort by RT-qPCR, Western blotting, immunohistochemistry, and multiplex immunofluorescence (mIF). Causality was established using in vitro functional assays in HepG2 cells.

RESULTS: A 12-gene random survival forest (RSF) signature accurately predicted patient survival across independent cohorts. MCM7 emerged as a central senescence-associated driver. ScRNA-seq and mIF confirmed MCM7 characterizes a highly proliferative, clonally expanding subset of CD8+ T cells within the tumor microenvironment. In vitro, MCM7 knockdown significantly inhibited HepG2 cell proliferation and upregulated senescence enforcers p16 and p21, whereas overexpression facilitated evasion. Additionally, TIDE analysis revealed that high-risk patients exhibited elevated immune evasion potential, predicting poor immunotherapy response.

CONCLUSION: This integrative multi-omics framework uncovers an MCM7 MCM7-driven senescence-associated axis promising HCC progression and immune dysfunction, offering a robust tool for prognostic stratification and novel therapeutic insights.

PMID:42183188 | PMC:PMC13195000 | DOI:10.3389/fimmu.2026.1762222

A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence

✇Nature - Issue - nature.com science feeds
By: Min He · Jia Su · Xiaogang Zhou · Tuo Qi · Jiazheng Wang · Tianxin Zhang · Jinhua Chen · Mawsheng Chern · Youpin Xu · Xiang Lu · Qingqing Hou · Hongrui Liu · Wenfang Huang · Jiawei Liu · Shiying Li · Yunfei Zhu · Xue Chen · Senyu Ran · Han Li · Renju Liu · Mingliang Lei · Guosong Shu · Haicheng Liao · Junjie Yin · Xiaobo Zhu · Yongyan Tang · Li Song · Long Wang · Qing Xiong · Jiali Liu · Yu Bi · Yihua Yang · Hai Qing · Bingtian Ma · Yan Li · Jing Fan · Peng Qin · Yuping Wang · Jianming Wu · Wenming Wang · Shigui Li · Weitao Li · Jing Wang · Xuewei Chen
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10572-x

A fungal long non-coding RNA from Magnaporthe oryzae translocates into rice cells to sequester a host microRNA that normally represses PKR1, a negative immunity regulator, thereby facilitating infection and revealing a widespread RNA-based pathogen–host interaction mechanism.

High-salt diet in macrophage-associated metabolic disorders: Mechanisms and therapeutic implications

✇(Multiomics OR Omics) AND (Pancreatic)
By: Ke Xiao · Lin Gu · Jiayi Ma · Shanjie Rong · Xiongtao Yue · Yuechen Liu · Shiwei Liu · Fei Sun · Shu Zhang · Congyi Wang
19 May 2026 at 18:00

Chin Med J (Engl). 2026 May 19. doi: 10.1097/CM9.0000000000004098. Online ahead of print.

ABSTRACT

High-salt diet (HSD) has emerged as a prevalent environmental factor that exacerbates chronic inflammation and insulin resistance in obesity-associated type 2 diabetes (T2D) by modulating macrophage polarization, metabolic reprogramming, and epigenetic imprinting. Current evidence demonstrates that HSD activates p38/mitogen-activated protein kinase (MAPK), nuclear factor kappa-B (NF-κB), and NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome signaling pathways, by which it drives macrophage polarization toward a proinflammatory M1 phenotype while inducing a glycolysis-dominant metabolic shift, thereby establishing a persistent "metabolic memory". Moreover, HSD orchestrates metabolic memory in macrophages through coordinated epigenetic machinery, including histone modifications (Trimethylation of histone H3 at lysine 4 [H3K4me3] and Acetylation of histone H3 at lysine 27 [H3K27ac]), DNA methylation, and noncoding RNAs (e.g., long non-coding RNA MALAT1 and miR-155), leading to sustained inflammatory phenotypes. In multiple metabolic organs (e.g., adipose tissue, liver, pancreas, and gut), the HSD-macrophage axis aggravates systemic insulin resistance through shared proinflammatory signaling and other tissue-specific mechanisms. Most importantly, therapeutic strategies targeting the NLRP3 inflammasome, metabolic pathways, and epigenetic alterations offer novel approaches for managing metabolic inflammation. Future investigations are encouraged to leverage lineage tracing, single-cell sequencing, and spatial multi-omics technologies to advance the development of precision medicine for macrophage-associated metabolic disorders.

PMID:42156155 | DOI:10.1097/CM9.0000000000004098

Imaging interface-controlled bulk oxygen spillover

✇Nature - Issue - nature.com science feeds
By: Weijue Wang · Hongbin Xu · Shuhui Liu · Xiaofeng Yang · Wei Liu · Yang-Gang Wang · Yanqiang Huang · Tao Zhang
15 April 2026 at 08:00

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10324-x

In situ microscopic single-particle imaging demonstrates the significance of rationally engineered metal–support interfaces for activating the oxygen in bulk catalyst, helping elucidate reaction pathways in catalytic conversions.
Received — 8 April 2026 ⏭

FileGram: Grounding Agent Personalization in File-System Behavioral Traces

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shuai Liu · Shulin Tian · Kairui Hu · Yuhao Dong · Zhe Yang · Bo Li · Jingkang Yang · Chen Change Loy · Ziwei Liu
7 April 2026 at 12:00
arXiv:2604.04901v1 Announce Type: cross Abstract: Coworking AI agents operating within local file systems are rapidly emerging as a paradigm in human-AI interaction; however, effective personalization remains limited by severe data constraints, as strict privacy barriers and the difficulty of jointly collecting multimodal real-world traces prevent scalable training and evaluation, and existing methods remain interaction-centric while overlooking dense behavioral traces in file-system operations; to address this gap, we propose FileGram, a comprehensive framework that grounds agent memory and personalization in file-system behavioral traces, comprising three core components: (1) FileGramEngine, a scalable persona-driven data engine that simulates realistic workflows and generates fine-grained multimodal action sequences at scale; (2) FileGramBench, a diagnostic benchmark grounded in file-system behavioral traces for evaluating memory systems on profile reconstruction, trace disentanglement, persona drift detection, and multimodal grounding; and (3) FileGramOS, a bottom-up memory architecture that builds user profiles directly from atomic actions and content deltas rather than dialogue summaries, encoding these traces into procedural, semantic, and episodic channels with query-time abstraction; extensive experiments show that FileGramBench remains challenging for state-of-the-art memory systems and that FileGramEngine and FileGramOS are effective, and by open-sourcing the framework, we hope to support future research on personalized memory-centric file-system agents.
Received — 26 March 2026 ⏭

A Review of the Role of Zeqi Decoction in the Treatment of Non-Small Cell Lung Cancer

✇Pulmonary nodule
By: Lan Zhang · Yanhui Jia · Lei Song · Yiman Li · Yawei Liu · Yan Qin · Tao Wang · Na Li
18 March 2026 at 18:00

J Multidiscip Healthc. 2026 Mar 11;19:584071. doi: 10.2147/JMDH.S584071. eCollection 2026.

ABSTRACT

Non-small cell lung cancer (NSCLC) is one of the malignant tumors with the highest incidence and mortality rates. Zeqi Decoction has the functions of "promoting diuresis and reducing swelling, resolving phlegm and dispersing nodules", embodying the unique approach of traditional Chinese medicine in treating lung cancer by "strengthening the body's resistance and eliminating pathogenic factors". Modern research shows that Zeqi Decoction exerts anti-NSCLC effects through multiple pathways and targets. In terms of the material basis of its efficacy, its active ingredients (such as diterpene esters and flavonoids contained in Zeqi) have the ability to directly inhibit the proliferation, invasion and migration of tumor cells and induce apoptosis. In terms of the mechanism of action, basic experiments have revealed that Zeqi Decoction can down-regulate the S100A9/STAT3 signaling pathway, inhibit the immunosuppressive activity of myelium-derived suppressor cells (MDSCs), reshape the tumor microenvironment, thereby enhancing the cytotoxic function of CD8⁺T cells, and can also regulate the EGFR/PI3K/Akt pathway to affect PD-L1 expression. Intervene in tumor immune escape; In terms of clinical transformation, the combination of Zexi Decoction with chemotherapy and targeted therapy can improve patients' symptoms such as cough and pleural effusion, prolong progression-free survival, and alleviate the toxic and side effects of Western medical treatment. In addition, Zexi Decoction also shows potential value in reversing drug resistance such as gemcitabine. At present, there are still problems such as the lack of standardized protocols and unclear molecular mechanisms in the research. In the future, it is necessary to combine new technologies such as network pharmacology and multi-omics analysis to deepen the research on the pharmacological material basis, dose-effect relationship and evidence-based medicine of Zeqi Decoction, so as to promote the clinical application and transformation of the combination of traditional Chinese and Western medicine in the treatment of NSCLC.

PMID:41847115 | PMC:PMC12991379 | DOI:10.2147/JMDH.S584071

Received — 17 March 2026 ⏭

FedBPrompt: Federated Domain Generalization Person Re-Identification via Body Distribution Aware Visual Prompts

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xin Xu · Weilong Li · Wei Liu · Wenke Huang · Zhixi Yu · Bin Yang · Xiaoying Liao · Kui Jiang
16 March 2026 at 12:00
arXiv:2603.12912v1 Announce Type: cross Abstract: Federated Domain Generalization for Person Re-Identification (FedDG-ReID) learns domain-invariant representations from decentralized data. While Vision Transformer (ViT) is widely adopted, its global attention often fails to distinguish pedestrians from high similarity backgrounds or diverse viewpoints -- a challenge amplified by cross-client distribution shifts in FedDG-ReID. To address this, we propose Federated Body Distribution Aware Visual Prompt (FedBPrompt), introducing learnable visual prompts to guide Transformer attention toward pedestrian-centric regions. FedBPrompt employs a Body Distribution Aware Visual Prompts Mechanism (BAPM) comprising: Holistic Full Body Prompts to suppress cross-client background noise, and Body Part Alignment Prompts to capture fine-grained details robust to pose and viewpoint variations. To mitigate high communication costs, we design a Prompt-based Fine-Tuning Strategy (PFTS) that freezes the ViT backbone and updates only lightweight prompts, significantly reducing communication overhead while maintaining adaptability. Extensive experiments demonstrate that BAPM effectively enhances feature discrimination and cross-domain generalization, while PFTS achieves notable performance gains within only a few aggregation rounds. Moreover, both BAPM and PFTS can be easily integrated into existing ViT-based FedDG-ReID frameworks, making FedBPrompt a flexible and effective solution for federated person re-identification. The code is available at https://github.com/leavlong/FedBPrompt.

GeoChemAD: Benchmarking Unsupervised Geochemical Anomaly Detection for Mineral Exploration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yihao Ding · Yiran Zhang · Chris Gonzalez · Eun-Jung Holden · Wei Liu
16 March 2026 at 12:00
arXiv:2603.13068v1 Announce Type: cross Abstract: Geochemical anomaly detection plays a critical role in mineral exploration as deviations from regional geochemical baselines may indicate mineralization. Existing studies suffer from two key limitations: (1) single region scenarios which limit model generalizability; (2) proprietary datasets, which makes result reproduction unattainable. In this work, we introduce \textbf{GeoChemAD}, an open-source benchmark dataset compiled from government-led geological surveys, covering multiple regions, sampling sources, and target elements. The dataset comprises eight subsets representing diverse spatial scales and sampling conditions. To establish strong baselines, we reproduce and benchmark a range of unsupervised anomaly detection methods, including statistical models, generative and transformer-based approaches. Furthermore, we propose \textbf{GeoChemFormer}, a transformer-based framework that leverages self-supervised pretraining to learn target-element-aware geochemical representations for spatial samples. Extensive experiments demonstrate that GeoChemFormer consistently achieves superior and robust performance across all eight subsets, outperforming existing unsupervised methods in both anomaly detection accuracy and generalization capability. The proposed dataset and framework provide a foundation for reproducible research and future development in this direction.
Received — 14 March 2026 ⏭
Received — 5 March 2026 ⏭

LifeBench: A Benchmark for Long-Horizon Multi-Source Memory

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zihao Cheng · Weixin Wang · Yu Zhao · Ziyang Ren · Jiaxuan Chen · Ruiyang Xu · Shuai Huang · Yang Chen · Guowei Li · Mengshi Wang · Yi Xie · Ren Zhu · Zeren Jiang · Keda Lu · Yihong Li · Xiaoliang Wang · Liwei Liu · Cam-Tu Nguyen
5 March 2026 at 13:00
arXiv:2603.03781v1 Announce Type: new Abstract: Long-term memory is fundamental for personalized agents capable of accumulating knowledge, reasoning over user experiences, and adapting across time. However, existing memory benchmarks primarily target declarative memory, specifically semantic and episodic types, where all information is explicitly presented in dialogues. In contrast, real-world actions are also governed by non-declarative memory, including habitual and procedural types, and need to be inferred from diverse digital traces. To bridge this gap, we introduce Lifebench, which features densely connected, long-horizon event simulation. It pushes AI agents beyond simple recall, requiring the integration of declarative and non-declarative memory reasoning across diverse and temporally extended contexts. Building such a benchmark presents two key challenges: ensuring data quality and scalability. We maintain data quality by employing real-world priors, including anonymized social surveys, map APIs, and holiday-integrated calendars, thus enforcing fidelity, diversity and behavioral rationality within the dataset. Towards scalability, we draw inspiration from cognitive science and structure events according to their partonomic hierarchy; enabling efficient parallel generation while maintaining global coherence. Performance results show that top-tier, state-of-the-art memory systems reach just 55.2\% accuracy, highlighting the inherent difficulty of long-horizon retrieval and multi-source integration within our proposed benchmark. The dataset and data synthesis code are available at https://github.com/1754955896/LifeBench.

Self-Play Only Evolves When Self-Synthetic Pipeline Ensures Learnable Information Gain

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wei Liu · Siya Qi · Yali Du · Yulan He
4 March 2026 at 13:00
arXiv:2603.02218v1 Announce Type: cross Abstract: Large language models (LLMs) make it plausible to build systems that improve through self-evolving loops, but many existing proposals are better understood as self-play and often plateau quickly. A central failure mode is that the loop synthesises more data without increasing learnable information for the next iteration. Through experiments on a self-play coding task, we reveal that sustainable self-evolution requires a self-synthesised data pipeline with learnable information that increases across iterations. We identify triadic roles that self-evolving LLMs play: the Proposer, which generates tasks; the Solver, which attempts solutions; and the Verifier, which provides training signals, and we identify three system designs that jointly target learnable information gain from this triadic roles perspective. Asymmetric co-evolution closes a weak-to-strong-to-weak loop across roles. Capacity growth expands parameter and inference-time budgets to match rising learnable information. Proactive information seeking introduces external context and new task sources that prevent saturation. Together, these modules provide a measurable, system-level path from brittle self-play dynamics to sustained self-evolution.

UniG2U-Bench: Do Unified Models Advance Multimodal Understanding?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zimo Wen · Boxiu Li · Wanbo Zhang · Junxiang Lei · Xiaoyu Chen · Yijia Fan · Qi Zhang · Yujiang Wang · Lili Qiu · Bo Li · Ziwei Liu · Caihua Shan · Yifan Yang · Yifei Shen
4 March 2026 at 13:00
arXiv:2603.03241v1 Announce Type: cross Abstract: Unified multimodal models have recently demonstrated strong generative capabilities, yet whether and when generation improves understanding remains unclear. Existing benchmarks lack a systematic exploration of the specific tasks where generation facilitates understanding. To this end, we introduce UniG2U-Bench, a comprehensive benchmark categorizing generation-to-understanding (G2U) evaluation into 7 regimes and 30 subtasks, requiring varying degrees of implicit or explicit visual transformations. Extensive evaluation of over 30 models reveals three core findings: 1) Unified models generally underperform their base Vision-Language Models (VLMs), and Generate-then-Answer (GtA) inference typically degrades performance relative to direct inference. 2) Consistent enhancements emerge in spatial intelligence, visual illusions, or multi-round reasoning subtasks, where enhanced spatial and shape perception, as well as multi-step intermediate image states, prove beneficial. 3) Tasks with similar reasoning structures and models sharing architectures exhibit correlated behaviors, suggesting that generation-understanding coupling induces class-consistent inductive biases over tasks, pretraining data, and model architectures. These findings highlight the necessity for more diverse training data and novel paradigms to fully unlock the potential of unified multimodal modeling.
Received — 25 February 2026 ⏭

DoAtlas-1: A Causal Compilation Paradigm for Clinical AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yulong Li · Jianxu Chen · Xiwei Liu · Chuanyue Suo · Rong Xia · Zhixiang Lu · Yichen Li · Xinlin Zhuang · Niranjana Arun Menon · Yutong Xie · Eran Segal · Imran Razzak
24 February 2026 at 13:00
arXiv:2602.19158v1 Announce Type: new Abstract: Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical auditability. We propose causal compilation, a paradigm that transforms medical evidence from narrative text into executable code. The paradigm standardizes heterogeneous research evidence into structured estimand objects, each explicitly specifying intervention contrast, effect scale, time horizon, and target population, supporting six executable causal queries: do-calculus, counterfactual reasoning, temporal trajectories, heterogeneous effects, mechanistic decomposition, and joint interventions. We instantiate this paradigm in DoAtlas-1, compiling 1,445 effect kernels from 754 studies through effect standardization, conflict-aware graph construction, and real-world validation (Human Phenotype Project, 10,000 participants). The system achieves 98.5% canonicalization accuracy and 80.5% query executability. This paradigm shifts medical AI from text generation to executable, auditable, and verifiable causal reasoning.

Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhiwei Ning · Xuanang Gao · Jiaxi Cao · Runze Yang · Huiying Xu · Xinzhong Zhu · Jie Yang · Wei Liu
24 February 2026 at 13:00
arXiv:2602.19536v1 Announce Type: cross Abstract: Linear modeling methods like Mamba have been merged as the effective backbone for the 3D object detection task. However, previous Mamba-based methods utilize the bidirectional encoding for the whole non-empty voxel sequence, which contains abundant useless background information in the scenes. Though directly encoding foreground voxels appears to be a plausible solution, it tends to degrade detection performance. We attribute this to the response attenuation and restricted context representation in the linear modeling for fore-only sequences. To address this problem, we propose a novel backbone, termed Fore-Mamba3D, to focus on the foreground enhancement by modifying Mamba-based encoder. The foreground voxels are first sampled according to the predicted scores. Considering the response attenuation existing in the interaction of foreground voxels across different instances, we design a regional-to-global slide window (RGSW) to propagate the information from regional split to the entire sequence. Furthermore, a semantic-assisted and state spatial fusion module (SASFMamba) is proposed to enrich contextual representation by enhancing semantic and geometric awareness within the Mamba model. Our method emphasizes foreground-only encoding and alleviates the distance-based and causal dependencies in the linear autoregression model. The superior performance across various benchmarks demonstrates the effectiveness of Fore-Mamba3D in the 3D object detection task.

Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yue Pan · Xingyao Wang · Hanyue Zhang · Liwei Liu · Changxin Li · Gang Yang · Rong Sheng · Yili Xia · Ming Chu
24 February 2026 at 13:00
arXiv:2602.19674v1 Announce Type: cross Abstract: Remote monitoring of heart failure (HF) via speech signals provides a non-invasive and cost-effective solution for long-term patient management. However, substantial inter-individual heterogeneity in vocal characteristics often limits the accuracy of traditional cross-sectional classification models. To address this, we propose a Longitudinal Intra-Patient Tracking (LIPT) scheme designed to capture the trajectory of relative symptomatic changes within individuals. Central to this framework is a Personalised Sequential Encoder (PSE), which transforms longitudinal speech recordings into context-aware latent representations. By incorporating historical data at each timestamp, the PSE facilitates a holistic assessment of the clinical trajectory rather than modelling discrete visits independently. Experimental results from a cohort of 225 patients demonstrate that the LIPT paradigm significantly outperforms the classic cross-sectional approaches, achieving a recognition accuracy of 99.7% for clinical status transitions. The model's high sensitivity was further corroborated by additional follow-up data, confirming its efficacy in predicting HF deterioration and its potential to secure patient safety in remote, home-based settings. Furthermore, this work addresses the gap in existing literature by providing a comprehensive analysis of different speech task designs and acoustic features. Taken together, the superior performance of the LIPT framework and PSE architecture validates their readiness for integration into long-term telemonitoring systems, offering a scalable solution for remote heart failure management.
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