aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

By: Pengsong Zhang · Xiang Hu · Guowei Huang · Yang Qi · Heng Zhang · Xiuxu Li · Jiaxing Song · Jiabin Luo · Yijiang Li · Shuo Yin · Chengxiao Dai · Eric Hanchen Jiang · Xiaoyan Zhou · Zhenfei Yin · Boqin Yuan · Jing Dong · Guinan Su · Guanren Qiao · Haiming Tang · Anghong Du · Lili Pan · Zhenzhong Lan · Xinyu Liu

18 December 2025 at 13:00

arXiv:2508.15126v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack rigorous quality-control mechanisms. Consequently, a significant amount of high-quality AI-generated research lacks appropriate venues for dissemination, hindering its potential to advance scientific progress. To address these challenges, we introduce aiXiv, a next-generation open-access platform for human and AI scientists. Its multi-agent architecture allows research proposals and papers to be submitted, reviewed, and iteratively refined by both human and AI scientists. It also provides API and MCP interfaces that enable seamless integration of heterogeneous human and AI scientists, creating a scalable and extensible ecosystem for autonomous scientific discovery. Through extensive experiments, we demonstrate that aiXiv is a reliable and robust platform that significantly enhances the quality of AI-generated research proposals and papers after iterative revising and reviewing on aiXiv. Our work lays the groundwork for a next-generation open-access ecosystem for AI scientists, accelerating the publication and dissemination of high-quality AI-generated research content. Code: https://github.com/aixiv-org aiXiv: https://aixiv.science

Why Text Prevails: Vision May Undermine Multimodal Medical Decision Making

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

By: Siyuan Dai · Lunxiao Li · Kun Zhao · Eardi Lila · Paul K. Crane · Heng Huang · Dongkuan Xu · Haoteng Tang · Liang Zhan

17 December 2025 at 13:00

arXiv:2512.13747v1 Announce Type: cross Abstract: With the rapid progress of large language models (LLMs), advanced multimodal large language models (MLLMs) have demonstrated impressive zero-shot capabilities on vision-language tasks. In the biomedical domain, however, even state-of-the-art MLLMs struggle with basic Medical Decision Making (MDM) tasks. We investigate this limitation using two challenging datasets: (1) three-stage Alzheimer's disease (AD) classification (normal, mild cognitive impairment, dementia), where category differences are visually subtle, and (2) MIMIC-CXR chest radiograph classification with 14 non-mutually exclusive conditions. Our empirical study shows that text-only reasoning consistently outperforms vision-only or vision-text settings, with multimodal inputs often performing worse than text alone. To mitigate this, we explore three strategies: (1) in-context learning with reason-annotated exemplars, (2) vision captioning followed by text-only inference, and (3) few-shot fine-tuning of the vision tower with classification supervision. These findings reveal that current MLLMs lack grounded visual understanding and point to promising directions for improving multimodal decision making in healthcare.

Automated Multitier Tagging of Chinese Online Health Education Resources Using a Large Language Model: Development and Validation Study

Journal of Medical Internet Research

By: Jialin Meng · Ruiming Dai · Xiaolan Huang · Yi Gu · Shixing Yan · Xiaoke Wang · Jingrong Gao · Tian-Tian Zhang

18 December 2025 at 06:00

Background: Precision health promotion, which aims to tailor health messages to individual needs, is hampered by the lack of structured metadata in vast digital health resource libraries. This bottleneck prevents scalable, personalized content delivery and exacerbates information overload for the public. Objective: This study aimed to develop, deploy, and validate an automated tagging system using a large language model (LLM) to create the foundational metadata infrastructure required for tailored health communication at scale. Methods: We developed a comprehensive, 3-tier health promotion taxonomy (10 primary, 34 secondary, and 90,562 tertiary tags) using a hybrid Delphi and corpus-mining methodology. We then constructed a hybrid inference pipeline by fine-tuning a Baichuan2-7B LLM with low-rank adaptation for initial tag generation. This was then refined by a domain-specific named entity recognition model and standardized against a vector database. The system’s performance was evaluated against manual annotations from nonexpert staff on a test set of 1000 resources. We used a “no gold standard” framework, comparing the artificial intelligence–human (A-H) interrater reliability (IRR) with a supplemental human-human (H-H) IRR baseline and expert adjudication for cases where artificial intelligence provided additional tags (“AI Additive”). Results: The A-H agreement was moderate (Cohen κ=0.54, 95% CI 0.53-0.56; Jaccard similarity coefficient=0.48, 95% CI 0.46-0.50). Critically, this was higher than the baseline nonexpert H-H agreement (Cohen κ=0.32, 95% CI 0.29-0.35; Jaccard similarity coefficient=0.35, 95% CI 0.27-0.43). A granular analysis of disagreements revealed that in 15.9% (159/1000) of the cases, the “AI Additive” tags were not identified by human annotators. Expert adjudication of these cases confirmed that the “AI Additive” tags were correct and relevant with a precision of 90% (45/50; 95% CI 78.2%-96.7%). Conclusions: A fine-tuned LLM, integrated into a hybrid pipeline, can function as a powerful augmentation tool for health content annotation. The system’s consistency (A-H κ=0.54) was found to be superior to the baseline human workflow (H-H κ=0.32). By moving beyond simple automation to reliably identify relevant health topics missed by manual annotators with high, expert-validated accuracy, this study provides a robust technical and methodological blueprint for implementing artificial intelligence to enhance precision health communication in public health settings.

Exploring the role of lipid metabolism genes in gastric cancer prognosis and tumor immune microenvironment

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Yanan Xiao · Qin Yu · Zewen Zhu · Shan Zhou · Zhe Wu · Xin Luo · Xingzhi Huang · Zhiqiang Zhan

11 December 2025 at 19:00

J Int Med Res. 2025 Dec;53(12):3000605251403252. doi: 10.1177/03000605251403252. Epub 2025 Dec 11.

ABSTRACT

BackgroundGastric cancer remains a major global health challenge due to its high mortality rate and complex pathophysiological mechanisms. Emerging evidence highlights that dysregulated lipid metabolism contributes to gastric cancer progression and prognosis, but the associations between lipid metabolism-associated genes, gastric cancer patient survival, and tumor immune microenvironment remodeling are not fully elucidated.MethodsWe analyzed publicly available omics and clinical data, including RNA sequencing data from 371 gastric cancer samples in The Cancer Genome Atlas database and 433 gastric cancer samples in the Gene Expression Omnibus database. We first curated the top 100 lipid metabolism-associated genes based on relevance scores. Then, univariate Cox regression was used to identify genes significantly associated with overall survival. Consensus clustering was applied to these survival-related genes to define gastric cancer molecular subtypes. Copy number variation analysis was performed to assess genomic alterations of these genes in tumor samples. A prognostic risk model was constructed using least absolute shrinkage and selection operator regression and validated via multivariate Cox regression. Immune infiltration analysis using CIBERSORT and ESTIMATE algorithms was conducted to explore associations between lipid metabolism-associated genes and tumor immune microenvironment characteristics.ResultsA total of 3911 differentially expressed genes were identified between gastric cancer and adjacent normal tissues. Among the top 100 lipid metabolism-associated genes, 43 were significantly linked to patient survival, most of which were considered as poor prognostic factors. Copy number variation analysis revealed frequent copy number gains of these genes in tumor samples. Consensus clustering stratified patients into two molecular subtypes (LMAGcluster A and LMAGcluster B), with LMAGcluster A showing significantly worse survival outcomes (median survival: 2.6 years vs. 8.3 years in LMAGcluster B, p < 0.001). LMAGcluster A was also characterized by elevated infiltration of pro-tumor immune cells, such as regulatory T cells and follicular helper T cells. The prognostic model based on 14 key lipid metabolism-associated genes exhibited robust predictive performance, with area under the receiver operating characteristic curve values of 0.702-0.761 in The Cancer Genome Atlas cohort and 0.621-0.638 in the Gene Expression Omnibus cohort for 1-, 3-, and 5-year survival.ConclusionLipid metabolism-associated genes are closely associated with gastric cancer prognosis and tumor immune microenvironment remodeling. The identified gene-based molecular subtypes and prognostic model provide novel insights into gastric cancer progression, and the 14 key genes may serve as potential biomarkers and therapeutic targets.

PMID:41381057 | DOI:10.1177/03000605251403252

The AI Productivity Index (APEX)

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

By: Bertie Vidgen · Abby Fennelly · Evan Pinnix · Julien Benchek · Daniyal Khan · Zach Richards · Austin Bridges · Calix Huang · Ben Hunsberger · Isaac Robinson · Akul Datta · Chirag Mahapatra · Dominic Barton · Cass R. Sunstein · Eric Topol · Brendan Foody · Osvald Nitski

8 December 2025 at 13:00

arXiv:2509.25721v4 Announce Type: replace-cross Abstract: We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable tasks in four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD). This technical report details the extensions to APEX-v1, including an increase in the held-out evaluation set from n = 50 to n = 100 cases per job (n = 400 total) and updates to the grading methodology. We present a new leaderboard, where GPT5 (Thinking = High) remains the top performing model with a score of 67.0%. APEX-v1-extended shows that frontier models still have substantial limitations when performing typical professional tasks. To support further research, we are open sourcing n = 25 non-benchmark example cases per role (n = 100 total) along with our evaluation harness.

Promoting Responsible DeepSeek Deployment in Health Care: Scoping Review Comparing Grey and White Literature

Journal of Medical Internet Research

By: Wang Jiang · Dan Wang · Yihang Zeng · Jiaqi Huang · Chang Xu · Chenxi Liu

6 December 2025 at 03:30

Background: The rapid deployment of DeepSeek, an open-source large language model has sparked concerns of its impact on patient outcomes and safety. However, little is known about how DeepSeek is used and regulated in these facilities. Objective: This study aimed to 1) systematically review the characteristics of deployed DeepSeek in the top 100 hospitals in China; and 2) compare performances and risks from hospital disclosure with research evidence. Methods: We performed a scoping review of gray and white literature, collecting data from the top 100 Chinese hospitals. We extracted basic characteristics of DeepSeek, its aim, evaluation approach, performance, risk and hospital regulation. A coding framework was developedcovering LLMs application scenario, evaluation dimension and source of risk. Results: We identified a total of 58 DeepSeek models in 48 out of the top 100 Chinese hospitals as well as 27 studies. We observed deployed DeepSeek mainly intended to assist clinical decision making, such as patient diagnosis and treatment recommendation. However, only 36.2% hospital-deployed models clearly indicated a pre-deployment assessment, 22.4% presented assessment results, and 8.6% identified potential risks and countermeasures. We found poor transparency in hospital reporting, with none presenting evaluation details. Hospitals were likely to report DeepSeek’s higher performance and fewer risks. Conclusions: The irresponsible deployment of DeepSeek in Chinese leading hospitals poses potential risks to patient outcomes and safety. We highlight the urgent need that existing regulations should be expanded to the downstream developers and users and hospitals need to perform a more rigorous validation and transparent reporting.

AI Deception: Risks, Dynamics, and Controls

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

By: Boyuan Chen (Jay) · Sitong Fang (Jay) · Jiaming Ji (Jay) · Yanxu Zhu (Jay) · Pengcheng Wen (Jay) · Jinzhou Wu (Jay) · Yingshui Tan (Jay) · Boren Zheng (Jay) · Mengying Yuan (Jay) · Wenqi Chen (Jay) · Donghai Hong (Jay) · Alex Qiu (Jay) · Xin Chen (Jay) · Jiayi Zhou (Jay) · Kaile Wang (Jay) · Juntao Dai (Jay) · Borong Zhang (Jay) · Tianzhuo Yang (Jay) · Saad Siddiqui (Jay) · Isabella Duan (Jay) · Yawen Duan (Jay) · Brian Tse (Jay) · Jen-Tse (Jay) · Huang · Kun Wang · Baihui Zheng · Jiaheng Liu · Jian Yang · Yiming Li · Wenting Chen · Dongrui Liu · Lukas Vierling · Zhiheng Xi · Haobo Fu · Wenxuan Wang · Jitao Sang · Zhengyan Shi · Chi-Min Chan · Eugenie Shi · Simin Li · Juncheng Li · Jian Yang · Wei Ji · Dong Li · Jinglin Yang · Jun Song · Yinpeng Dong · Jie Fu · Bo Zheng · Min Yang · Yike Guo · Philip Torr · Robert Trager · Yi Zeng · Zhongyuan Wang · Yaodong Yang · Tiejun Huang · Ya-Qin Zhang · Hongjiang Zhang · Andrew Yao

4 December 2025 at 13:00

arXiv:2511.22619v2 Announce Type: replace Abstract: As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an empirically demonstrated risk across language models, AI agents, and emerging frontier systems. This project provides a comprehensive and up-to-date overview of the AI deception field, covering its core concepts, methodologies, genesis, and potential mitigations. First, we identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception. We then review existing empirical studies and associated risks, highlighting deception as a sociotechnical safety challenge. We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment. Deception emergence reveals the mechanisms underlying AI deception: systems with sufficient capability and incentive potential inevitably engage in deceptive behaviors when triggered by external conditions. Deception treatment, in turn, focuses on detecting and addressing such behaviors. On deception emergence, we analyze incentive foundations across three hierarchical levels and identify three essential capability preconditions required for deception. We further examine contextual triggers, including supervision gaps, distributional shifts, and environmental pressures. On deception treatment, we conclude detection methods covering benchmarks and evaluation protocols in static and interactive settings. Building on the three core factors of deception emergence, we outline potential mitigation strategies and propose auditing approaches that integrate technical, community, and governance efforts to address sociotechnical challenges and future AI risks. To support ongoing work in this area, we release a living resource at www.deceptionsurvey.com.

Gut microbial metabolites in cancer immunomodulation

Omics In Lung

By: Hengshuo Liu · Xingyu Xiong · Weizhen Zhu · Sheng Wang · Weichao Huang · Guoqing Zhu · Hang Xu · Lu Yang

3 December 2025 at 19:00

Mol Cancer. 2025 Dec 3. doi: 10.1186/s12943-025-02521-5. Online ahead of print.

ABSTRACT

Gut microbiota-derived metabolites are emerging as systemic "remote immunoregulators" that shape tumor immunity across tissues. Integrating evidence across short-chain fatty acids, tryptophan derivatives, secondary bile acids, polyamines and other metabolites, we advance a metabolite-immune pathway-cancer framework that links receptor-mediated signaling, epigenetic remodeling and metabolic reprogramming to context-dependent, bidirectional immune effects. Importantly, in addition to the g protein-coupled receptor / aryl hydrocarbon receptor pathway, the selected microbial small molecule metabolites are the true T-cell receptor ligands of unconventional T cells, directly shaping the tissue resident immune and tumor microenvironment, supplementing the receptor signaling and epigenetic programs in our framework. We synthesize how these metabolites recalibrate the tumor immune microenvironment-modulating antigen presentation, T-cell effector fitness and exhaustion, regulatory T-cell activity, and myeloid polarization-and why the same metabolite can either potentiate immune surveillance or entrench immunosuppression depending on ligand-receptor pairing, dose and tissue niche. We compare tumor-type specific patterns (e.g., colorectal, liver, lung, breast and prostate cancers) to highlight common circuits and organ-restricted idiosyncrasies. Methodologically, we outline how single-cell and spatial multi-omics, imaging mass spectrometry and functional biosensors now enable co-registration of metabolite exposure with immune-cell states in human tumors, providing an actionable basis for biomarker discovery. Given ongoing debate about signals attributed to intratumoral microbiota in low-biomass tumor tissues, we foreground quantifiable, spatially mappable and pharmacologically tractable metabolite-receptor pathways, using microbe-associated molecular patterns / translocation as comparators to judge when chemical signals should be prioritized as intervention targets. Finally, we evaluate precision intervention avenues-including fecal microbiota transplantation, rational bacterial consortia, engineered microbes and nanoparticle-enabled metabolite delivery-and propose stratification rules that pair metabolite/receptor signatures with fit-for-purpose delivery. Together, mapping tissue-specific metabolite-immune circuits and embedding them in robust biomarker frameworks may convert microbial metabolites from correlative markers into therapeutic targets and tools, improving the efficacy and durability of cancer immunotherapy.

PMID:41339918 | DOI:10.1186/s12943-025-02521-5

Radiologist Copilot: An Agentic Assistant with Orchestrated Tools for Radiology Reporting with Quality Control

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

By: Yongrui Yu · Zhongzhen Huang · Linjie Mu · Shaoting Zhang · Xiaofan Zhang

3 December 2025 at 13:00

arXiv:2512.02814v1 Announce Type: new Abstract: Radiology reporting is an essential yet time-consuming and error-prone task for radiologists in clinical examinations, especially for volumetric medical images. Rigorous quality control is also critical but tedious, ensuring that the final report meets clinical standards. Existing automated approaches, including radiology report generation methods and medical vision-language models, focus mainly on the report generation phase and neglect the crucial quality control procedure, limiting their capability to provide comprehensive support to radiologists. We propose Radiologist Copilot, an agentic AI assistant equipped with orchestrated tools designed for automated radiology reporting with quality control. Leveraging large language models as the reasoning backbone, the agentic system autonomously selects tools, plans, and executes actions, emulating the behavior of radiologists throughout the holistic radiology reporting process. The orchestrated tools include region localization, think with image paradigm directed region analysis planning, strategic template selection for report generation, quality assessment and feedback-driven adaptive refinement for quality control. Therefore, Radiologist Copilot facilitates accurate, complete, and efficient radiology reporting, assisting radiologists and improving clinical efficiency. Experimental results demonstrate that Radiologist Copilot significantly surpasses other state-of-the-art methods in radiology reporting. The source code will be released upon acceptance.

MedCondDiff: Lightweight, Robust, Semantically Guided Diffusion for Medical Image Segmentation

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

By: Ruirui Huang · Jiacheng Li

2 December 2025 at 13:00

arXiv:2512.00350v1 Announce Type: cross Abstract: We introduce MedCondDiff, a diffusion-based framework for multi-organ medical image segmentation that is efficient and anatomically grounded. The model conditions the denoising process on semantic priors extracted by a Pyramid Vision Transformer (PVT) backbone, yielding a semantically guided and lightweight diffusion architecture. This design improves robustness while reducing both inference time and VRAM usage compared to conventional diffusion models. Experiments on multi-organ, multi-modality datasets demonstrate that MedCondDiff delivers competitive performance across anatomical regions and imaging modalities, underscoring the potential of semantically guided diffusion models as an effective class of architectures for medical imaging tasks.

SelfAI: Building a Self-Training AI System with LLM Agents

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

By: Xiao Wu · Ting-Zhu Huang · Liang-Jian Deng · Xiaobing Yu · Yu Zhong · Shangqi Deng · Ufaq Khan · Jianghao Wu · Xiaofeng Liu · Imran Razzak · Xiaojun Chang · Yutong Xie

2 December 2025 at 13:00

arXiv:2512.00403v1 Announce Type: cross Abstract: Recent work on autonomous scientific discovery has leveraged LLM-based agents to integrate problem specification, experiment planning, and execution into end-to-end systems. However, these frameworks are often confined to narrow application domains, offer limited real-time interaction with researchers, and lack principled mechanisms for determining when to halt exploration, resulting in inefficiencies, reproducibility challenges, and under-utilized human expertise. To address these gaps, we propose \textit{SelfAI}, a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations, a Cognitive Agent powered by LLMs with optimal stopping criteria to iteratively refine hyperparameter searches, and an Experiment Manager responsible for orchestrating parallel, fault-tolerant training workflows across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback. We further introduce two novel evaluation metrics, Score and $\text{AUP}_D$, to quantify discovery efficiency and search diversity. Across regression, NLP, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials compared to classical Bayesian optimization and LLM-based baselines, while enabling seamless interaction with human researchers.

Human Decision-making is Susceptible to AI-driven Manipulation

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

By: Sahand Sabour · June M. Liu · Siyang Liu · Chris Z. Yao · Shiyao Cui · Xuanming Zhang · Wen Zhang · Yaru Cao · Advait Bhat · Jian Guan · Wei Wu · Rada Mihalcea · Hongning Wang · Tim Althoff · Tatia M. C. Lee · Minlie Huang

2 December 2025 at 13:00

arXiv:2502.07663v3 Announce Type: replace Abstract: AI systems are increasingly intertwined with daily life, assisting users with various tasks and guiding decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. Through a randomized between-subjects experiment with 233 participants, we examined human susceptibility to such manipulation in financial (e.g., purchases) and emotional (e.g., conflict resolution) decision-making contexts. Participants interacted with one of three AI agents: a neutral agent (NA) optimizing for user benefit without explicit influence, a manipulative agent (MA) designed to covertly influence beliefs and behaviors, or a strategy-enhanced manipulative agent (SEMA) equipped with established psychological tactics, allowing it to select and apply them adaptively during interactions to reach its hidden objectives. By analyzing participants' preference ratings, we found significant susceptibility to AI-driven manipulation. Particularly across both decision-making domains, interacting with the manipulative agents significantly increased the odds of rating hidden incentives higher than optimal options (Financial, MA: OR=5.24, SEMA: OR=7.96; Emotional, MA: OR=5.52, SEMA: OR=5.71) compared to the NA group. Notably, we found no clear evidence that employing psychological strategies (SEMA) was overall more effective than simple manipulative objectives (MA) on our primary outcomes. Hence, AI-driven manipulation could become widespread even without requiring sophisticated tactics and expertise. While our findings are preliminary and derived from hypothetical, low-stakes scenarios, we highlight a critical vulnerability in human-AI interactions, emphasizing the need for ethical safeguards and regulatory frameworks to protect human autonomy.

Uni-X: Mitigating Modality Conflict with a Two-End-Separated Architecture for Unified Multimodal Models

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

By: Jitai Hao · Hao Liu · Xinyan Xiao · Qiang Huang · Jun Yu

2 December 2025 at 13:00

arXiv:2509.24365v2 Announce Type: replace-cross Abstract: Unified Multimodal Models (UMMs) built on shared autoregressive (AR) transformers are attractive for their architectural simplicity. However, we identify a critical limitation: when trained on multimodal inputs, modality-shared transformers suffer from severe gradient conflicts between vision and text, particularly in shallow and deep layers. We trace this issue to the fundamentally different low-level statistical properties of images and text, while noting that conflicts diminish in middle layers where representations become more abstract and semantically aligned. To overcome this challenge, we propose Uni-X, a two-end-separated, middle-shared architecture. Uni-X dedicates its initial and final layers to modality-specific processing, while maintaining shared parameters in the middle layers for high-level semantic fusion. This X-shaped design not only eliminates gradient conflicts at both ends but also further alleviates residual conflicts in the shared layers. Extensive experiments validate the effectiveness of Uni-X. Under identical training conditions, Uni-X achieves superior training efficiency compared to strong baselines. When scaled to 3B parameters with larger training data, Uni-X matches or surpasses 7B AR-based UMMs, achieving a GenEval score of 82 for image generation alongside strong performance in text and vision understanding tasks. These results establish Uni-X as a parameter-efficient and scalable foundation for future unified multimodal modeling. Our code is available at https://github.com/CURRENTF/Uni-X

The AI Productivity Index (APEX)

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

By: Bertie Vidgen · Abby Fennelly · Evan Pinnix · Julien Bencheck · Daniyal Khan · Zach Richards · Austin Bridges · Calix Huang · Ben Hunsberger · Isaac Robinson · Akul Datta · Chirag Mahapatra · Dominic Barton · Cass R. Sunstein · Eric Topol · Brendan Foody · Osvald Nitski

2 December 2025 at 13:00

arXiv:2509.25721v3 Announce Type: replace-cross Abstract: We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable tasks in four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD). This technical report details the extensions to APEX-v1, including an increase in the held-out evaluation set from n = 50 to n = 100 cases per job (n = 400 total) and updates to the grading methodology. We present a new leaderboard, where GPT5 (Thinking = High) remains the top performing model with a score of 67.0%. APEX-v1-extended shows that frontier models still have substantial limitations when performing typical professional tasks. To support further research, we are open sourcing n = 25 non-benchmark example cases per role (n = 100 total) along with our evaluation harness.

Decoding the cholesterol-apoptosis axis in HCC: a machine learning-based multi-omics integration and single-cell transcriptomic analysis

Omics in Hepatocellular

By: Yang Li · Jun Shi · Aiqing Zhao · Jing Huang · Lei Dai

25 November 2025 at 19:00

Discov Oncol. 2025 Nov 25;16(1):2162. doi: 10.1007/s12672-025-04010-z.

ABSTRACT

Liver hepatocellular carcinoma (LIHC), a predominant form of primary hepatic malignancy, demonstrates a progressively escalating global incidence, imposing substantial health and economic burdens on patients and society. Early diagnosis remains challenging, often resulting in late-stage detection, which limits the efficacy of current therapeutic strategies. This study systematically examines the transcriptional signatures of apoptosis-associated and cholesterol metabolic pathways in LIHC, providing insights into its underlying mechanisms and identifying potential prognostic markers. We employed multi-omics and machine learning to evaluate gene expression variations and construct a prognostic risk scoring model. This study identified apoptosis- and cholesterol metabolism-related differentially expressed genes (ACMRDEGs). Importantly, LASSO regression analysis identified six hub genes (EPHX2, FABP5, SQLE, ADH4, HMGCS2, and CYP7A1) as critical prognostic biomarkers, demonstrating significant correlation with overall survival (OS). Furthermore, immune cell infiltration analysis indicated significant differences in 12 immune cell types within LIHC microenvironment, underscoring the immune system's involvement in disease progression. cholesterol and alcohol metabolism pathways were significantly enriched among hub gene modules, as quantified by multiple gene enrichment analyses. Single-cell analysis identified six major cell types, providing a deeper understanding of the cellular heterogeneity within LIHC. In summarize, this study presents the first integrated apoptosis-cholesterol metabolic pathway-based six-gene prognostic model for LIHC, validated for robustness across multiple cohorts, which may facilitate personalized therapeutic strategies and refined risk assessment in clinical practice.

PMID:41288805 | PMC:PMC12647489 | DOI:10.1007/s12672-025-04010-z

Decoding the cholesterol-apoptosis axis in HCC: a machine learning-based multi-omics integration and single-cell transcriptomic analysis

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Yang Li · Jun Shi · Aiqing Zhao · Jing Huang · Lei Dai

25 November 2025 at 19:00

Discov Oncol. 2025 Nov 25;16(1):2162. doi: 10.1007/s12672-025-04010-z.

ABSTRACT

Liver hepatocellular carcinoma (LIHC), a predominant form of primary hepatic malignancy, demonstrates a progressively escalating global incidence, imposing substantial health and economic burdens on patients and society. Early diagnosis remains challenging, often resulting in late-stage detection, which limits the efficacy of current therapeutic strategies. This study systematically examines the transcriptional signatures of apoptosis-associated and cholesterol metabolic pathways in LIHC, providing insights into its underlying mechanisms and identifying potential prognostic markers. We employed multi-omics and machine learning to evaluate gene expression variations and construct a prognostic risk scoring model. This study identified apoptosis- and cholesterol metabolism-related differentially expressed genes (ACMRDEGs). Importantly, LASSO regression analysis identified six hub genes (EPHX2, FABP5, SQLE, ADH4, HMGCS2, and CYP7A1) as critical prognostic biomarkers, demonstrating significant correlation with overall survival (OS). Furthermore, immune cell infiltration analysis indicated significant differences in 12 immune cell types within LIHC microenvironment, underscoring the immune system's involvement in disease progression. cholesterol and alcohol metabolism pathways were significantly enriched among hub gene modules, as quantified by multiple gene enrichment analyses. Single-cell analysis identified six major cell types, providing a deeper understanding of the cellular heterogeneity within LIHC. In summarize, this study presents the first integrated apoptosis-cholesterol metabolic pathway-based six-gene prognostic model for LIHC, validated for robustness across multiple cohorts, which may facilitate personalized therapeutic strategies and refined risk assessment in clinical practice.

PMID:41288805 | DOI:10.1007/s12672-025-04010-z

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems

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

By: Zekun Zhou · Xiaocheng Feng · Lei Huang · Xiachong Feng · Ziyun Song · Ruihan Chen · Liang Zhao · Weitao Ma · Yuxuan Gu · Baoxin Wang · Dayong Wu · Guoping Hu · Ting Liu · Bing Qin

24 November 2025 at 13:00

arXiv:2503.01424v4 Announce Type: replace Abstract: Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents a systematic review of the progress in this domain. Specifically, we organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. Hypothesis formulation involves knowledge synthesis and hypothesis generation. Hypothesis validation includes the verification of scientific claims, theorem proving, and experiment validation. Manuscript publication encompasses manuscript writing and the peer review process. Furthermore, we identify and discuss the current challenges faced in these areas, as well as potential future directions for research. Finally, we also offer a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process. We hope this paper serves as an introduction for beginners and fosters future research. Resources have been made publicly available at https://github.com/zkzhou126/AI-for-Research.

Multimodal analysis of whole slide images in colorectal cancer

npj Digital Medicine

By: Jitendra Jonnagaddala · Miljana Shulajkovska · Anton Gradišek · Toni Rose Jue · Qifeng Zhou · Yuzhi Guo · Jamil Mahmoud El Chayeb · Ruijiang Li · Jana Lipkova · Jakob Nikolas Kather · Junzhou Huang

24 November 2025 at 08:00

npj Digital Medicine, Published online: 24 November 2025; doi:10.1038/s41746-025-02095-y

Multimodal analysis of whole slide images in colorectal cancer

Grounded by Experience: Generative Healthcare Prediction Augmented with Hierarchical Agentic Retrieval

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

By: Chuang Zhao · Hui Tang · Hongke Zhao · Xiaofang Zhou · Xiaomeng Li

18 November 2025 at 13:00

arXiv:2511.13293v1 Announce Type: new Abstract: Accurate healthcare prediction is critical for improving patient outcomes and reducing operational costs. Bolstered by growing reasoning capabilities, large language models (LLMs) offer a promising path to enhance healthcare predictions by drawing on their rich parametric knowledge. However, LLMs are prone to factual inaccuracies due to limitations in the reliability and coverage of their embedded knowledge. While retrieval-augmented generation (RAG) frameworks, such as GraphRAG and its variants, have been proposed to mitigate these issues by incorporating external knowledge, they face two key challenges in the healthcare scenario: (1) identifying the clinical necessity to activate the retrieval mechanism, and (2) achieving synergy between the retriever and the generator to craft contextually appropriate retrievals. To address these challenges, we propose GHAR, a \underline{g}enerative \underline{h}ierarchical \underline{a}gentic \underline{R}AG framework that simultaneously resolves when to retrieve and how to optimize the collaboration between submodules in healthcare. Specifically, for the first challenge, we design a dual-agent architecture comprising Agent-Top and Agent-Low. Agent-Top acts as the primary physician, iteratively deciding whether to rely on parametric knowledge or to initiate retrieval, while Agent-Low acts as the consulting service, summarising all task-relevant knowledge once retrieval was triggered. To tackle the second challenge, we innovatively unify the optimization of both agents within a formal Markov Decision Process, designing diverse rewards to align their shared goal of accurate prediction while preserving their distinct roles. Extensive experiments on three benchmark datasets across three popular tasks demonstrate our superiority over state-of-the-art baselines, highlighting the potential of hierarchical agentic RAG in advancing healthcare systems.

SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning

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

By: Xuchen Li · Ruitao Wu · Xuanbo Liu · Xukai Wang · Jinbo Hu · Zhixin Bai · Bohan Zeng · Hao Liang · Leheng Chen · Mingrui Chen · Haitian Zhong · Xuanlin Yang · Xu-Yao Zhang · Liu Liu · Jia Li · Kaiqi Huang · Jiahao Xu · Haitao Mi · Wentao Zhang · Bin Dong

18 November 2025 at 13:00

arXiv:2511.08151v2 Announce Type: replace Abstract: Recent advances in large language models have enabled AI systems to achieve expert-level performance on domain-specific scientific tasks, yet these systems remain narrow and handcrafted. We introduce SciAgent, a unified multi-agent system designed for generalistic scientific reasoning-the ability to adapt reasoning strategies across disciplines and difficulty levels. SciAgent organizes problem solving as a hierarchical process: a Coordinator Agent interprets each problem's domain and complexity, dynamically orchestrating specialized Worker Systems, each composed of interacting reasoning Sub-agents for symbolic deduction, conceptual modeling, numerical computation, and verification. These agents collaboratively assemble and refine reasoning pipelines tailored to each task. Across mathematics and physics Olympiads (IMO, IMC, IPhO, CPhO), SciAgent consistently attains or surpasses human gold-medalist performance, demonstrating both domain generality and reasoning adaptability. Additionally, SciAgent has been tested on the International Chemistry Olympiad (IChO) and selected problems from the Humanity's Last Exam (HLE) benchmark, further confirming the system's ability to generalize across diverse scientific domains. This work establishes SciAgent as a concrete step toward generalistic scientific intelligence-AI systems capable of coherent, cross-disciplinary reasoning at expert levels.

Reading view