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Deep Ideation: Designing LLM Agents to Generate Novel Research Ideas on Scientific Concept Network

✇cs.AI, q-bio.NC updates on arXiv.org
By: Keyu Zhao · Weiquan Lin · Qirui Zheng · Fengli Xu · Yong Li
arXiv:2511.02238v1 Announce Type: new Abstract: Novel research ideas play a critical role in advancing scientific inquiries. Recent advancements in Large Language Models (LLMs) have demonstrated their potential to generate novel research ideas by leveraging large-scale scientific literature. However, previous work in research ideation has primarily relied on simplistic methods, such as keyword co-occurrence or semantic similarity. These approaches focus on identifying statistical associations in the literature but overlook the complex, contextual relationships between scientific concepts, which are essential to effectively leverage knowledge embedded in human literature. For instance, papers that simultaneously mention "keyword A" and "keyword B" often present research ideas that integrate both concepts. Additionally, some LLM-driven methods propose and refine research ideas using the model's internal knowledge, but they fail to effectively utilize the scientific concept network, limiting the grounding of ideas in established research. To address these challenges, we propose the Deep Ideation framework to address these challenges, integrating a scientific network that captures keyword co-occurrence and contextual relationships, enriching LLM-driven ideation. The framework introduces an explore-expand-evolve workflow to iteratively refine research ideas, using an Idea Stack to track progress. A critic engine, trained on real-world reviewer feedback, guides the process by providing continuous feedback on the novelty and feasibility of ideas. Our experiments show that our approach improves the quality of generated ideas by 10.67% compared to other methods, with ideas surpassing top conference acceptance levels. Human evaluation highlights their practical value in scientific research, and ablation studies confirm the effectiveness of each component in the workflow. Code repo is available at https://github.com/kyZhao-1/Deep-Ideation.
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Causal Graph Neural Networks for Healthcare

✇cs.AI, q-bio.NC updates on arXiv.org
By: Munib Mesinovic · Max Buhlan · Tingting Zhu
arXiv:2511.02531v1 Announce Type: cross Abstract: Healthcare artificial intelligence systems routinely fail when deployed across institutions, with documented performance drops and perpetuation of discriminatory patterns embedded in historical data. This brittleness stems, in part, from learning statistical associations rather than causal mechanisms. Causal graph neural networks address this triple crisis of distribution shift, discrimination, and inscrutability by combining graph-based representations of biomedical data with causal inference principles to learn invariant mechanisms rather than spurious correlations. This Review examines methodological foundations spanning structural causal models, disentangled causal representation learning, and techniques for interventional prediction and counterfactual reasoning on graphs. We analyse applications demonstrating clinical value across psychiatric diagnosis through brain network analysis, cancer subtyping via multi-omics causal integration, continuous physiological monitoring with mechanistic interpretation, and drug recommendation correcting prescription bias. These advances establish foundations for patient-specific Causal Digital Twins, enabling in silico clinical experimentation, with integration of large language models for hypothesis generation and causal graph neural networks for mechanistic validation. Substantial barriers remain, including computational requirements precluding real-time deployment, validation challenges demanding multi-modal evidence triangulation beyond cross-validation, and risks of causal-washing where methods employ causal terminology without rigorous evidentiary support. We propose tiered frameworks distinguishing causally-inspired architectures from causally-validated discoveries and identify critical research priorities making causal rather than purely associational claims.
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AI Diffusion in Low Resource Language Countries

✇cs.AI, q-bio.NC updates on arXiv.org
By: Amit Misra · Syed Waqas Zamir · Wassim Hamidouche · Inbal Becker-Reshef · Juan Lavista Ferres
arXiv:2511.02752v1 Announce Type: cross Abstract: Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-resource languages due to data scarcity. We hypothesize that this performance deficit reduces the utility of AI, thereby slowing adoption in Low-Resource Language Countries (LRLCs). To test this, we use a weighted regression model to isolate the language effect from socioeconomic and demographic factors, finding that LRLCs have a share of AI users that is approximately 20% lower relative to their baseline. These results indicate that linguistic accessibility is a significant, independent barrier to equitable AI diffusion.
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MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qianhao Yuan · Jie Lou · Zichao Li · Jiawei Chen · Yaojie Lu · Hongyu Lin · Le Sun · Debing Zhang · Xianpei Han
arXiv:2511.02805v1 Announce Type: cross Abstract: Typical search agents concatenate the entire interaction history into the LLM context, preserving information integrity but producing long, noisy contexts, resulting in high computation and memory costs. In contrast, using only the current turn avoids this overhead but discards essential information. This trade-off limits the scalability of search agents. To address this challenge, we propose MemSearcher, an agent workflow that iteratively maintains a compact memory and combines the current turn with it. At each turn, MemSearcher fuses the user's question with the memory to generate reasoning traces, perform search actions, and update memory to retain only information essential for solving the task. This design stabilizes context length across multi-turn interactions, improving efficiency without sacrificing accuracy. To optimize this workflow, we introduce multi-context GRPO, an end-to-end RL framework that jointly optimize reasoning, search strategies, and memory management of MemSearcher Agents. Specifically, multi-context GRPO samples groups of trajectories under different contexts and propagates trajectory-level advantages across all conversations within them. Trained on the same dataset as Search-R1, MemSearcher achieves significant improvements over strong baselines on seven public benchmarks: +11% on Qwen2.5-3B-Instruct and +12% on Qwen2.5-7B-Instruct relative average gains. Notably, the 3B-based MemSearcher even outperforms 7B-based baselines, demonstrating that striking a balance between information integrity and efficiency yields both higher accuracy and lower computational overhead. The code and models will be publicly available at https://github.com/icip-cas/MemSearcher
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How can we assess human-agent interactions? Case studies in software agent design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Valerie Chen · Rohit Malhotra · Xingyao Wang · Juan Michelini · Xuhui Zhou · Aditya Bharat Soni · Hoang H. Tran · Calvin Smith · Ameet Talwalkar · Graham Neubig
arXiv:2510.09801v2 Announce Type: replace Abstract: LLM-powered agents are both a promising new technology and a source of complexity, where choices about models, tools, and prompting can affect their usefulness. While numerous benchmarks measure agent accuracy across domains, they mostly assume full automation, failing to represent the collaborative nature of real-world use cases. In this paper, we make two major steps towards the rigorous assessment of human-agent interactions. First, we propose PULSE, a framework for more efficient human-centric evaluation of agent designs, which comprises collecting user feedback, training an ML model to predict user satisfaction, and computing results by combining human satisfaction ratings with model-generated pseudo-labels. Second, we deploy the framework on a large-scale web platform built around the open-source software agent OpenHands, collecting in-the-wild usage data across over 15k users. We conduct case studies around how three agent design decisions -- choice of LLM backbone, planning strategy, and memory mechanisms -- impact developer satisfaction rates, yielding practical insights for software agent design. We also show how our framework can lead to more robust conclusions about agent design, reducing confidence intervals by 40% compared to a standard A/B test. Finally, we find substantial discrepancies between in-the-wild results and benchmark performance (e.g., the anti-correlation between results comparing claude-sonnet-4 and gpt-5), underscoring the limitations of benchmark-driven evaluation. Our findings provide guidance for evaluations of LLM agents with humans and identify opportunities for better agent designs.
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AutoPDL: Automatic Prompt Optimization for LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Claudio Spiess · Mandana Vaziri · Louis Mandel · Martin Hirzel
arXiv:2504.04365v5 Announce Type: replace-cross Abstract: The performance of large language models (LLMs) depends on how they are prompted, with choices spanning both the high-level prompting pattern (e.g., Zero-Shot, CoT, ReAct, ReWOO) and the specific prompt content (instructions and few-shot demonstrations). Manually tuning this combination is tedious, error-prone, and specific to a given LLM and task. Therefore, this paper proposes AutoPDL, an automated approach to discovering good LLM agent configurations. Our approach frames this as a structured AutoML problem over a combinatorial space of agentic and non-agentic prompting patterns and demonstrations, using successive halving to efficiently navigate this space. We introduce a library implementing common prompting patterns using the PDL prompt programming language. AutoPDL solutions are human-readable, editable, and executable PDL programs that use this library. This approach also enables source-to-source optimization, allowing human-in-the-loop refinement and reuse. Evaluations across three tasks and seven LLMs (ranging from 3B to 70B parameters) show consistent accuracy gains ($9.21\pm15.46$ percentage points), up to 67.5pp, and reveal that selected prompting strategies vary across models and tasks.
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From Passive to Proactive: A Multi-Agent System with Dynamic Task Orchestration for Intelligent Medical Pre-Consultation

✇cs.AI, q-bio.NC updates on arXiv.org
By: ChengZhang Yu · YingRu He · Hongyan Cheng · nuo Cheng · Zhixing Liu · Dongxu Mu · Zhangrui Shen · Zhanpeng Jin
arXiv:2511.01445v1 Announce Type: new Abstract: Global healthcare systems face critical challenges from increasing patient volumes and limited consultation times, with primary care visits averaging under 5 minutes in many countries. While pre-consultation processes encompassing triage and structured history-taking offer potential solutions, they remain limited by passive interaction paradigms and context management challenges in existing AI systems. This study introduces a hierarchical multi-agent framework that transforms passive medical AI systems into proactive inquiry agents through autonomous task orchestration. We developed an eight-agent architecture with centralized control mechanisms that decomposes pre-consultation into four primary tasks: Triage ($T_1$), History of Present Illness collection ($T_2$), Past History collection ($T_3$), and Chief Complaint generation ($T_4$), with $T_1$--$T_3$ further divided into 13 domain-specific subtasks. Evaluated on 1,372 validated electronic health records from a Chinese medical platform across multiple foundation models (GPT-OSS 20B, Qwen3-8B, Phi4-14B), the framework achieved 87.0% accuracy for primary department triage and 80.5% for secondary department classification, with task completion rates reaching 98.2% using agent-driven scheduling versus 93.1% with sequential processing. Clinical quality scores from 18 physicians averaged 4.56 for Chief Complaints, 4.48 for History of Present Illness, and 4.69 for Past History on a 5-point scale, with consultations completed within 12.7 rounds for $T_2$ and 16.9 rounds for $T_3$. The model-agnostic architecture maintained high performance across different foundation models while preserving data privacy through local deployment, demonstrating the potential for autonomous AI systems to enhance pre-consultation efficiency and quality in clinical settings.
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Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Angelos Alexopoulos · Agorakis Bompotas · Nikitas Rigas Kalogeropoulos · Panagiotis Kechagias · Athanasios P. Kalogeras · Christos Alexakos
arXiv:2511.00094v1 Announce Type: cross Abstract: Robotic systems have become integral to smart environments, enabling applications ranging from urban surveillance and automated agriculture to industrial automation. However, their effective operation in dynamic settings - such as smart cities and precision farming - is challenged by continuously evolving topographies and environmental conditions. Traditional control systems often struggle to adapt quickly, leading to inefficiencies or operational failures. To address this limitation, we propose a novel framework for autonomous and dynamic reconfiguration of robotic controllers using Digital Twin technology. Our approach leverages a virtual replica of the robot's operational environment to simulate and optimize movement trajectories in response to real-world changes. By recalculating paths and control parameters in the Digital Twin and deploying the updated code to the physical robot, our method ensures rapid and reliable adaptation without manual intervention. This work advances the integration of Digital Twins in robotics, offering a scalable solution for enhancing autonomy in smart, dynamic environments.
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Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiquan Wang · Yahui Ma · Yuhan Chang · Jiayao Yan · Jialin Zhang · Minnuo Cai · Kai Wei
arXiv:2511.00209v1 Announce Type: cross Abstract: Diffusion models have emerged as a leading framework in generative modeling, showing significant potential to accelerate and transform the traditionally slow and costly process of drug discovery. This review provides a systematic comparison of their application in designing two principal therapeutic modalities: small molecules and therapeutic peptides. We analyze how a unified framework of iterative denoising is adapted to the distinct molecular representations, chemical spaces, and design objectives of each modality. For small molecules, these models excel at structure-based design, generating novel, pocket-fitting ligands with desired physicochemical properties, yet face the critical hurdle of ensuring chemical synthesizability. Conversely, for therapeutic peptides, the focus shifts to generating functional sequences and designing de novo structures, where the primary challenges are achieving biological stability against proteolysis, ensuring proper folding, and minimizing immunogenicity. Despite these distinct challenges, both domains face shared hurdles: the need for more accurate scoring functions, the scarcity of high-quality experimental data, and the crucial requirement for experimental validation. We conclude that the full potential of diffusion models will be unlocked by bridging these modality-specific gaps and integrating them into automated, closed-loop Design-Build-Test-Learn (DBTL) platforms, thereby shifting the paradigm from chemical exploration to the targeted creation of novel therapeutics.
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Diagnosing Hallucination Risk in AI Surgical Decision-Support: A Sequential Framework for Sequential Validation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dong Chen · Yanzhe Wei · Zonglin He · Guan-Ming Kuang · Canhua Ye · Meiru An · Huili Peng · Yong Hu · Huiren Tao · Kenneth MC Cheung
arXiv:2511.00588v1 Announce Type: cross Abstract: Large language models (LLMs) offer transformative potential for clinical decision support in spine surgery but pose significant risks through hallucinations, which are factually inconsistent or contextually misaligned outputs that may compromise patient safety. This study introduces a clinician-centered framework to quantify hallucination risks by evaluating diagnostic precision, recommendation quality, reasoning robustness, output coherence, and knowledge alignment. We assessed six leading LLMs across 30 expert-validated spinal cases. DeepSeek-R1 demonstrated superior overall performance (total score: 86.03 $\pm$ 2.08), particularly in high-stakes domains such as trauma and infection. A critical finding reveals that reasoning-enhanced model variants did not uniformly outperform standard counterparts: Claude-3.7-Sonnet's extended thinking mode underperformed relative to its standard version (80.79 $\pm$ 1.83 vs. 81.56 $\pm$ 1.92), indicating extended chain-of-thought reasoning alone is insufficient for clinical reliability. Multidimensional stress-testing exposed model-specific vulnerabilities, with recommendation quality degrading by 7.4% under amplified complexity. This decline contrasted with marginal improvements in rationality (+2.0%), readability (+1.7%) and diagnosis (+4.7%), highlighting a concerning divergence between perceived coherence and actionable guidance. Our findings advocate integrating interpretability mechanisms (e.g., reasoning chain visualization) into clinical workflows and establish a safety-aware validation framework for surgical LLM deployment.
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How Far Are Surgeons from Surgical World Models? A Pilot Study on Zero-shot Surgical Video Generation with Expert Assessment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhen Chen · Qing Xu · Jinlin Wu · Biao Yang · Yuhao Zhai · Geng Guo · Jing Zhang · Yinlu Ding · Nassir Navab · Jiebo Luo
arXiv:2511.01775v1 Announce Type: cross Abstract: Foundation models in video generation are demonstrating remarkable capabilities as potential world models for simulating the physical world. However, their application in high-stakes domains like surgery, which demand deep, specialized causal knowledge rather than general physical rules, remains a critical unexplored gap. To systematically address this challenge, we present SurgVeo, the first expert-curated benchmark for video generation model evaluation in surgery, and the Surgical Plausibility Pyramid (SPP), a novel, four-tiered framework tailored to assess model outputs from basic appearance to complex surgical strategy. On the basis of the SurgVeo benchmark, we task the advanced Veo-3 model with a zero-shot prediction task on surgical clips from laparoscopic and neurosurgical procedures. A panel of four board-certified surgeons evaluates the generated videos according to the SPP. Our results reveal a distinct "plausibility gap": while Veo-3 achieves exceptional Visual Perceptual Plausibility, it fails critically at higher levels of the SPP, including Instrument Operation Plausibility, Environment Feedback Plausibility, and Surgical Intent Plausibility. This work provides the first quantitative evidence of the chasm between visually convincing mimicry and causal understanding in surgical AI. Our findings from SurgVeo and the SPP establish a crucial foundation and roadmap for developing future models capable of navigating the complexities of specialized, real-world healthcare domains.
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Will Humanity Be Rendered Obsolete by AI?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mohamed El Louadi · Emna Ben Romdhane
arXiv:2510.22814v2 Announce Type: replace Abstract: This article analyzes the existential risks artificial intelligence (AI) poses to humanity, tracing the trajectory from current AI to ultraintelligence. Drawing on Irving J. Good and Nick Bostrom's theoretical work, plus recent publications (AI 2027; If Anyone Builds It, Everyone Dies), it explores AGI and superintelligence. Considering machines' exponentially growing cognitive power and hypothetical IQs, it addresses the ethical and existential implications of an intelligence vastly exceeding humanity's, fundamentally alien. Human extinction may result not from malice, but from uncontrollable, indifferent cognitive superiority.
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A Survey on Cache Methods in Diffusion Models: Toward Efficient Multi-Modal Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiacheng Liu · Xinyu Wang · Yuqi Lin · Zhikai Wang · Peiru Wang · Peiliang Cai · Qinming Zhou · Zhengan Yan · Zexuan Yan · Zhengyi Shi · Chang Zou · Yue Ma · Linfeng Zhang
arXiv:2510.19755v3 Announce Type: replace-cross Abstract: Diffusion Models have become a cornerstone of modern generative AI for their exceptional generation quality and controllability. However, their inherent \textit{multi-step iterations} and \textit{complex backbone networks} lead to prohibitive computational overhead and generation latency, forming a major bottleneck for real-time applications. Although existing acceleration techniques have made progress, they still face challenges such as limited applicability, high training costs, or quality degradation. Against this backdrop, \textbf{Diffusion Caching} offers a promising training-free, architecture-agnostic, and efficient inference paradigm. Its core mechanism identifies and reuses intrinsic computational redundancies in the diffusion process. By enabling feature-level cross-step reuse and inter-layer scheduling, it reduces computation without modifying model parameters. This paper systematically reviews the theoretical foundations and evolution of Diffusion Caching and proposes a unified framework for its classification and analysis. Through comparative analysis of representative methods, we show that Diffusion Caching evolves from \textit{static reuse} to \textit{dynamic prediction}. This trend enhances caching flexibility across diverse tasks and enables integration with other acceleration techniques such as sampling optimization and model distillation, paving the way for a unified, efficient inference framework for future multimodal and interactive applications. We argue that this paradigm will become a key enabler of real-time and efficient generative AI, injecting new vitality into both theory and practice of \textit{Efficient Generative Intelligence}.
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Generative Artificial Intelligence in Medical Education: Enhancing Critical Thinking or Undermining Cognitive Autonomy?

✇Journal of Medical Internet Research
By: Juan S Izquierdo-Condoy · Marlon Arias-Intriago · Andrea Tello-De-la-Torre · Felix Busch · Esteban Ortiz-Prado
Generative artificial intelligence (GenAI) enables the production of coherent and contextually relevant text by processing large-scale linguistic datasets. Tools such as ChatGPT, Gemini, Claude, and LLaMA are increasingly integrated into medical education, assisting students with a range of tasks, including clinical reasoning, literature review, scientific writing, and formative assessment. Although these tools offer significant advantages in terms of productivity, personalization, and cognitive support, their impact on critical thinking—a cornerstone of medical education—remains uncertain. The aim of this viewpoint paper is to critically assess the influence of GenAI on critical thinking within medical training, examining both its potential to enhance cognitive skills and the risks it poses to cognitive autonomy. Users have reported increased efficiency and improved linguistic output; however, concerns have also been raised regarding the risk of cognitive overreliance. Current evidence presents a mixed picture, indicating both improvements in learner engagement and potential drawbacks such as passivity or susceptibility to misinformation. Without curricular integration that prioritizes ethical use, prompt engineering, and critical evaluation, GenAI may compromise the cognitive autonomy of medical students. Conversely, when thoughtfully embedded into pedagogical frameworks, these tools can act as cognitive enhancers—supporting, rather than replacing, clinical reasoning. Medical education must adapt to ensure that future physicians engage with GenAI in a critical, ethical, and context-aware manner, especially in complex decision-making scenarios. This transformation demands not only technological fluency but also reflective practice and sustained oversight by faculty and academic institutions.
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Can human connection amplify digital health outcomes? Familial involvement in a mobile health app

✇npj Digital Medicine
By: Elizabeth J. Enichen · Kimia Heydari · Ben Li · Joseph C. Kvedar

npj Digital Medicine, Published online: 03 November 2025; doi:10.1038/s41746-025-02037-8

In “A Randomized Controlled Trial of Mobile Intervention Using Health Support Bubbles to Prevent Social Frailty”, Hayashi et al. investigated the effects of using a mobile health app with family or individually. Greater improvements in social behavior and frailty were noted in participants who used the app with family. In an era of remote healthcare and app-based health interventions, Hayashi et al.’s study reminds of the importance of human connection.
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Leveraging artificial intelligence to validate traditional biomarkers and drug targets in liver cancer recovery: a mini review

✇Omics in Hepatocellular
By: Shengjian Wu · Xiaoqiao Chen · Yuxiu Ji · Chi Zhang · Yujie Xie · Bin Liang

Front Pharmacol. 2025 Oct 17;16:1697608. doi: 10.3389/fphar.2025.1697608. eCollection 2025.

ABSTRACT

Hepatocellular carcinoma (HCC) remains a leading cause of cancer death, and recovery after therapy is shaped by heterogeneous etiologies, genomes and microenvironments. Targeted and immunotherapy combinations have broadened first-line options; yet durable benefit is uneven, and serum/imaging anchors (AFP, AFP-L3%, PIVKA-II, LI-RADS/mRECIST) incompletely resolve residual disease or functional restoration. In this review we summarise AI-enabled radiology, digital pathology and multi-omic/liquid-biopsy analytics that test and refine traditional biomarkers and drug-target readouts, and appraise translational opportunities in composite surveillance and recovery forecasting. We also discuss enduring challenges-including assay standardisation, spectrum bias, data leakage, domain shift and limited prospective external validation-that temper implementation. By integrating established anchors (AFP/AFP-L3%, PIVKA-II, ALBI, contrast-enhanced hallmarks) with AI-derived signals (radiomics/pathomics, cfDNA methylation) and pathway contexts (VEGF-VEGFR, WNT/β-catenin), emerging strategies align predictions with clinical endpoints, individualise therapy and chart hepatic function. Our synthesis provides an appraisal of AI-traditional integration in liver cancer recovery and outlines pragmatic standards-analytical robustness, transparent reporting and prospective, guideline-conformant evaluation-required for clinical adoption. We hope these insights will aid researchers and clinicians as they implement more effective, individualised monitoring and treatment pathways.

PMID:41181592 | PMC:PMC12575369 | DOI:10.3389/fphar.2025.1697608

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Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies

✇(Multiomics OR Omics) AND (Pancreatic)
By: Xi-Yue Zhang · Meng-Di Hu · Diliare Maimaitijiang · Tao Wang · Lin Wang

World J Gastroenterol. 2025 Oct 21;31(39):110971. doi: 10.3748/wjg.v31.i39.110971.

ABSTRACT

Pancreatitis poses persistent diagnostic and therapeutic challenges due to its heterogeneous clinical presentation, variable disease course, and lack of targeted interventions. Conventional tools, such as serum enzymes, cross-sectional imaging and clinical scoring systems, often exhibit limited sensitivity and prognostic value, especially during early or atypical stages. Moreover, therapeutic development remains slow, with limited progress toward personalized or mechanism-based strategies. These limitations highlight a critical need for integrative data-driven approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance clinical decision-making in pancreatitis. This narrative review synthesizes recent progress in AI applications across three domains. First, AI-enabled diagnostic platforms incorporating radiomics, deep learning-based imaging analysis, and biomarker optimization have improved early detection and differentiation of pancreatic diseases. Second, AI-driven prognostic models now allow real-time severity prediction, complication forecasting, and recurrence risk assessment, some of which have been deployed in hospital information systems for intensive care units and mortality risk triage. Third, AI-assisted drug discovery and network pharmacology, particularly in combination with traditional Chinese medicine, have revealed novel therapeutic opportunities. Despite encouraging developments, challenges remain in data standardization, model transparency and clinical validation. A multidisciplinary strategy integrating omics data, longitudinal monitoring and pharmacological modeling may help bridge current gaps and advance precision medicine in pancreatitis care.

PMID:41180795 | PMC:PMC12576603 | DOI:10.3748/wjg.v31.i39.110971

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The Denario project: Deep knowledge AI agents for scientific discovery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Francisco Villaescusa-Navarro · Boris Bolliet · Pablo Villanueva-Domingo · Adrian E. Bayer · Aidan Acquah · Chetana Amancharla · Almog Barzilay-Siegal · Pablo Bermejo · Camille Bilodeau · Pablo C\'ardenas Ram\'irez · Miles Cranmer · Urbano L. Fran\c{c}a · ChangHoon Hahn · Yan-Fei Jiang · Raul Jimenez · Jun-Young Lee · Antonio Lerario · Osman Mamun · Thomas Meier · Anupam A. Ojha · Pavlos Protopapas · Shimanto Roy · David N. Spergel · Pedro Taranc\'on-\'Alvarez · Ujjwal Tiwari · Matteo Viel · Digvijay Wadekar · Chi Wang · Bonny Y. Wang · Licong Xu · Yossi Yovel · Shuwen Yue · Wen-Han Zhou · Qiyao Zhu · Jiajun Zou · \'I\~nigo Zubeldia
arXiv:2510.26887v1 Announce Type: new Abstract: We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.
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