❌

Normal view

Received β€” 17 December 2025 ⏭ Favourites

New Perspectives on Gastric Inflammaging: Integrating Multi-Omics Mechanisms and Gerotherapeutic Strategies in Chronic Gastritis

βœ‡Omics in Gastric
By: Li Wang Β· Qiuye Chen Β· Houyan Zhang Β· Xin Zhao Β· Jinsheng Dong Β· Dongjie Wu Β· Yanjie Lian Β· Mingkun Liu Β· Hongliang Liu Β· Yaguang Chen Β· Qiu Chen Β· Wenliang Lyu Β· Yuning Bai
16 December 2025 at 19:00

Aging Dis. 2025 Dec 15. doi: 10.14336/AD.2025.1444. Online ahead of print.

ABSTRACT

Chronic gastritis (CG) is a highly prevalent, age-associated inflammatory disorder of gastric mucosa and a key precursor of gastric cancer in older adults. Beyond Helicobacter pylori infection and environmental insults, accumulating evidence indicates that chronic, low-grade inflammation coupled with aging biology, "gastric inflammaging", plays a central role in driving mucosal degeneration, atrophy, and malignant transformation. Here, we synthesize current mechanistic and multi-omics evidence to conceptualize CG as a tractable model of organ-specific inflammaging. We first summarize how hallmarks of aging-including cellular senescence and the senescence-associated secretory phenotype (SASP), mitochondrial dysfunction, impaired autophagy, immune exhaustion, and microbiome dysbiosis-converge to create a self-perpetuating inflammatory microenvironment in the stomach. We then review emerging single-cell and spatial multi-omics studies that delineate senescence-inflammation niches and reveal how these molecular neighborhoods relate to disease stage and cancer risk. Finally, we discuss therapeutic implications, highlighting geroscience-guided interventions such as senolytics/senomorphics, inflammasome and cGAS-STING pathway modulators, microbiota- and metabolite-targeted strategies, lifestyle interventions, and natural products, and propose a precision framework linking inflammaging biomarkers to patient stratification and clinical endpoints. Reframing CG as a gastric inflammaging model may provide a prototype for organ-specific healthy aging strategies and near-term gerotherapeutic trials aimed at extending healthspan.

PMID:41400573 | DOI:10.14336/AD.2025.1444

Development, advancement, and clinical integration of artificial intelligence technology in gastric cancer

βœ‡Omics in Gastric
By: Jia Fu Β· Mengjie Fang Β· Ling Wu Β· Xiaodong Li Β· Francesco De Cobelli Β· Diego Palumbo Β· Xuebin Xie Β· Xin Feng Β· Xu-Yao Zhang Β· Jingliang Zhang Β· Zhuozhao Zheng Β· Di Dong
16 December 2025 at 19:00

Chin Med J (Engl). 2025 Nov 28;138(24):3332-50. doi: 10.1097/CM9.0000000000003922. Online ahead of print.

ABSTRACT

Personalized medicine for gastric cancer continues to face numerous challenges, primarily due to the complexity of clinical decision making and the difficulty of integrating multimodal data. Artificial intelligence (AI), with its powerful capabilities in feature learning and pattern recognition, is emerging as a key technology to overcome these barriers. It provides critical support in areas such as early screening, histological subtyping, prediction of treatment response, and prognostic risk stratification. This review examines the application of AI in diagnosing and treating gastric cancer, with particular attention to the current mainstream AI methodologies, including feature engineering and deep learning and the rapidly evolving pretrained foundation models and multimodal large models. With the integration of medical images, digital pathology, multiomics data, and structured clinical information, AI systems are increasingly effective at capturing tumor heterogeneity and supporting complex clinical decisions in real time. On the one hand, task-specific models have demonstrated excellent performance in subtyping, staging, and prognosis assessment. On the other hand, the rise of foundation models and general-purpose large models is redefining the limits of AI in cross-task transfer, complex reasoning, and human-machine interaction. These technologies hold promise in addressing key obstacles such as data scarcity, modality heterogeneity, and fragmented clinical workflows, offering a feasible path toward a unified and efficient AI-driven diagnostic and therapeutic system for gastric cancer. As technological maturity progresses alongside the development of robust safety and ethical frameworks, AI is expected to evolve from a static auxiliary interpretation tool into an intelligent decision-making platform capable of semantic understanding, dynamic feedback, and multidisciplinary collaboration-therefore playing a pivotal role across the full spectrum of precision medicine in gastric cancer.

PMID:41400327 | PMC:PMC12721780 | DOI:10.1097/CM9.0000000000003922

Opinion: We crunched the numbers on drug discovery in the U.S. vs. China. The results were alarming

βœ‡STAT
By: Michael Kinch and Kevin Gardner
16 December 2025 at 17:30

This essay is part of a First Opinion series on the future of the National Institutes of Health and American science.

Around the world, nations with robust research and development infrastructure race to create therapeutics that meet the needs of their residents. Simply put, they dictate research priorities based on need. During the Covid pandemic, the United States was one of the first countries to gain access to vaccines to protect its citizens.

Read the rest…

Β© Adobe

Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues

βœ‡Journal of Medical Internet Research
By: Qiyang Zhang Β· Renwen Zhang Β· Yiying Xiong Β· Yuan Sui Β· Chang Tong Β· Fu-Hung Lin
17 December 2025 at 02:30
Background: To date, there is no comprehensive paper that systematically synthesizes the effect of generative AI chatbot’s impact on mental health. Can generative AI chatbots help reduce our psychological distress? Objective: To comprehensively assess existing evidence, a systematic review and meta-analysis is essential to evaluate the overall effectiveness, identify gaps, and guide future research in this evolving field. This paper aims to: 1) synthesize current evidence on generative AI chatbot interventions targeting mental health issues, 2) quantify the effectiveness of these interventions via a meta-analysis of randomized controlled trials (RCTs), and examine key moderators of intervention effectiveness. Methods: This systematic review included 26 studies for narrative synthesis, out of which 12 randomized controlled trials were included in the meta-analysis. Results: The systematic synthesis revealed that 1) generative AI-chatbot interventions mostly took place in non-WEIRD countries (Western, Educated, Industrialized, Rich, and Democratic) and 2) there is a lack of studies focusing on young children and older adults. The meta-analysis showed a statistically significant effect (ES = 0.36, p = .039), which means that generative AI chatbots are, on average, effective in reducing negative mental health issues. Among moderators, we found statistically significant and higher effect sizes among interventions that have an active control group, conducted in WEIRD countries, recruited non-clinical populations, older age, majority female, non-personalized, with human assistance, and social-oriented. Conclusions: In conclusion, this comprehensive review has highlighted the potential of generative AI chatbots in addressing anxiety, depression, negative mood, and stress. The findings indicate that generative AI interventions are particularly beneficial in WEIRD countries, among non-clinical populations, older adults, and females. Human-assisted and social-oriented programs, as opposed to fully autonomous or task-oriented ones, demonstrate greater effectiveness. Meanwhile, non-personalized chatbots appear to yield more effective outcomes than personalized systems.
Received β€” 16 December 2025 ⏭ Favourites

Robustness of Probabilistic Models to Low-Quality Data: A Multi-Perspective Analysis

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Liu Peng Β· Yaochu Jin
16 December 2025 at 13:00
arXiv:2512.11912v1 Announce Type: new Abstract: A systematic, comparative investigation into the effects of low-quality data reveals a stark spectrum of robustness across modern probabilistic models. We find that autoregressive language models, from token prediction to sequence-to-sequence tasks, are remarkably resilient (for GPT-2, test NLL increases modestly from 2.87 to 3.59 despite 50% token corruption). By contrast, under the same levels of data corruption, class-conditional diffusion models degrade catastrophically (image-label consistency plummets by 56.81% relative to baseline), while classifiers show a moderate impact that diminishes with dataset scale. To explain these discrepancies, we analyze the results through a multi-perspective lens, integrating information theory, PAC learning, and gradient dynamics. These analyses suggest that robustness is heavily influenced by two key principles: the richness of conditioning information, which constrains the learning problem, and the absolute information content of the training data, which allows the signal from correct information to dominate statistical noise.

Understanding Critical Thinking in Generative Artificial Intelligence Use: Development, Validation, and Correlates of the Critical Thinking in AI Use Scale

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Gabriel R. Lau Β· Wei Yan Low Β· Louis Tay Β· Ysabel Guevarra Β· Dragan Ga\v{s}evi\'c Β· Andree Hartanto
16 December 2025 at 13:00
arXiv:2512.12413v1 Announce Type: new Abstract: Generative AI tools are increasingly embedded in everyday work and learning, yet their fluency, opacity, and propensity to hallucinate mean that users must critically evaluate AI outputs rather than accept them at face value. The present research conceptualises critical thinking in AI use as a dispositional tendency to verify the source and content of AI-generated information, to understand how models work and where they fail, and to reflect on the broader implications of relying on AI. Across six studies (N = 1365), we developed and validated the 13-item critical thinking in AI use scale and mapped its nomological network. Study 1 generated and content-validated scale items. Study 2 supported a three-factor structure (Verification, Motivation, and Reflection). Studies 3, 4, and 5 confirmed this higher-order model, demonstrated internal consistency and test-retest reliability, strong factor loadings, sex invariance, and convergent and discriminant validity. Studies 3 and 4 further revealed that critical thinking in AI use was positively associated with openness, extraversion, positive trait affect, and frequency of AI use. Lastly, Study 6 demonstrated criterion validity of the scale, with higher critical thinking in AI use scores predicting more frequent and diverse verification strategies, greater veracity-judgement accuracy in a novel and naturalistic ChatGPT-powered fact-checking task, and deeper reflection about responsible AI. Taken together, the current work clarifies why and how people exercise oversight over generative AI outputs and provides a validated scale and ecologically grounded task paradigm to support theory testing, cross-group, and longitudinal research on critical engagement with generative AI outputs.

World Models Unlock Optimal Foraging Strategies in Reinforcement Learning Agents

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yesid Fonseca Β· Manuel S. R\'ios Β· Nicanor Quijano Β· Luis F. Giraldo
16 December 2025 at 13:00
arXiv:2512.12548v1 Announce Type: new Abstract: Patch foraging involves the deliberate and planned process of determining the optimal time to depart from a resource-rich region and investigate potentially more beneficial alternatives. The Marginal Value Theorem (MVT) is frequently used to characterize this process, offering an optimality model for such foraging behaviors. Although this model has been widely used to make predictions in behavioral ecology, discovering the computational mechanisms that facilitate the emergence of optimal patch-foraging decisions in biological foragers remains under investigation. Here, we show that artificial foragers equipped with learned world models naturally converge to MVT-aligned strategies. Using a model-based reinforcement learning agent that acquires a parsimonious predictive representation of its environment, we demonstrate that anticipatory capabilities, rather than reward maximization alone, drive efficient patch-leaving behavior. Compared with standard model-free RL agents, these model-based agents exhibit decision patterns similar to many of their biological counterparts, suggesting that predictive world models can serve as a foundation for more explainable and biologically grounded decision-making in AI systems. Overall, our findings highlight the value of ecological optimality principles for advancing interpretable and adaptive AI.
  • βœ‡cs.AI, q-bio.NC updates on arXiv.org
  • Spiking Manifesto Eugene Izhikevich
    arXiv:2512.11843v1 Announce Type: cross Abstract: Practically everything computers do is better, faster, and more power-efficient than the brain. For example, a calculator crunches numbers more energy-efficiently than any human. Yet AI models are a thousand times less efficient than the brain. These models use artificial neural networks (ANNs) and require GPUs for the multiplication of huge matrices. In contrast, spiking neural networks (SNNs) of the brain have no matrix multiplication and much
     
  • 🏷️
  • ↗️

Spiking Manifesto

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Eugene Izhikevich
16 December 2025 at 13:00
arXiv:2512.11843v1 Announce Type: cross Abstract: Practically everything computers do is better, faster, and more power-efficient than the brain. For example, a calculator crunches numbers more energy-efficiently than any human. Yet AI models are a thousand times less efficient than the brain. These models use artificial neural networks (ANNs) and require GPUs for the multiplication of huge matrices. In contrast, spiking neural networks (SNNs) of the brain have no matrix multiplication and much smaller energy requirements. This manifesto proposes a framework for thinking about popular AI models in terms of spiking networks and polychronization, and for interpreting spiking activity as nature's way of implementing look-up tables. This offers a way to convert AI models into a novel type of architecture with the promise of a thousandfold improvement in efficiency. Code is available at https://github.com/izhikevich/SNN

UniMark: Artificial Intelligence Generated Content Identification Toolkit

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Meilin Li Β· Ji He Β· Jia Xu Β· Shanzhe Lei Β· Yan Teng Β· Yingchun Wang Β· Xuhong Wang
16 December 2025 at 13:00
arXiv:2512.12324v1 Announce Type: cross Abstract: The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer from fragmentation and a lack of support for visible compliance marking. To address these gaps, we introduce the \textbf{UniMark}, an open-source, unified framework for multimodal content governance. Our system features a modular unified engine that abstracts complexities across text, image, audio, and video modalities. Crucially, we propose a novel dual-operation strategy, natively supporting both \emph{Hidden Watermarking} for copyright protection and \emph{Visible Marking} for regulatory compliance. Furthermore, we establish a standardized evaluation framework with three specialized benchmarks (Image/Video/Audio-Bench) to ensure rigorous performance assessment. This toolkit bridges the gap between advanced algorithms and engineering implementation, fostering a more transparent and secure digital ecosystem.

Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Xuhai Xu Β· Haoyu Hu Β· Haoran Zhang Β· Will Ke Wang Β· Reina Wang Β· Luis R. Soenksen Β· Omar Badri Β· Sheharbano Jafry Β· Elise Burger Β· Lotanna Nwandu Β· Apoorva Mehta Β· Erik P. Duhaime Β· Asif Qasim Β· Hause Lin Β· Janis Pereira Β· Jonathan Hershon Β· Paulius Mui Β· Alejandro A. Gru Β· No\'emie Elhadad Β· Lena Mamykina Β· Matthew Groh Β· Philipp Tschandl Β· Roxana Daneshjou Β· Marzyeh Ghassemi
16 December 2025 at 13:00
arXiv:2512.12500v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm's opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two large-scale experiments (623 lay people; 153 primary care physicians, PCPs) combining a fairness-based diagnosis AI model and different XAI explanations to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. AI assistance balanced across skin tones improved accuracy and reduced diagnostic disparities. However, LLM explanations yielded divergent effects: lay users showed higher automation bias - accuracy boosted when AI was correct, reduced when AI erred - while experienced PCPs remained resilient, benefiting irrespective of AI accuracy. Presenting AI suggestions first also led to worse outcomes when the AI was incorrect for both groups. These findings highlight XAI's varying impact based on expertise and timing, underscoring LLMs as a "double-edged sword" in medical AI and informing future human-AI collaborative system design.

Understanding Syllogistic Reasoning in LLMs from Formal and Natural Language Perspectives

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Aheli Poddar (Institute of Engineering & Management, Kolkata), Saptarshi Sahoo (Indian Statistical Institute, Chennai), Sujata Ghosh (Indian Statistical Institute, Chennai)
16 December 2025 at 13:00
arXiv:2512.12620v1 Announce Type: cross Abstract: We study syllogistic reasoning in LLMs from the logical and natural language perspectives. In process, we explore fundamental reasoning capabilities of the LLMs and the direction this research is moving forward. To aid in our studies, we use 14 large language models and investigate their syllogistic reasoning capabilities in terms of symbolic inferences as well as natural language understanding. Even though this reasoning mechanism is not a uniform emergent property across LLMs, the perfect symbolic performances in certain models make us wonder whether LLMs are becoming more and more formal reasoning mechanisms, rather than making explicit the nuances of human reasoning.

Cisco Integrated AI Security and Safety Framework Report

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Amy Chang Β· Tiffany Saade Β· Sanket Mendapara Β· Adam Swanda Β· Ankit Garg
16 December 2025 at 13:00
arXiv:2512.12921v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are being readily and rapidly adopted, increasingly permeating critical domains: from consumer platforms and enterprise software to networked systems with embedded agents. While this has unlocked potential for human productivity gains, the attack surface has expanded accordingly: threats now span content safety failures (e.g., harmful or deceptive outputs), model and data integrity compromise (e.g., poisoning, supply-chain tampering), runtime manipulations (e.g., prompt injection, tool and agent misuse), and ecosystem risks (e.g., orchestration abuse, multi-agent collusion). Existing frameworks such as MITRE ATLAS, National Institute of Standards and Technology (NIST) AI 100-2 Adversarial Machine Learning (AML) taxonomy, and OWASP Top 10s for Large Language Models (LLMs) and Agentic AI Applications provide valuable viewpoints, but each covers only slices of this multi-dimensional space. This paper presents Cisco's Integrated AI Security and Safety Framework ("AI Security Framework"), a unified, lifecycle-aware taxonomy and operationalization framework that can be used to classify, integrate, and operationalize the full range of AI risks. It integrates AI security and AI safety across modalities, agents, pipelines, and the broader ecosystem. The AI Security Framework is designed to be practical for threat identification, red-teaming, risk prioritization, and it is comprehensive in scope and can be extensible to emerging deployments in multimodal contexts, humanoids, wearables, and sensory infrastructures. We analyze gaps in prevailing frameworks, discuss design principles for our framework, and demonstrate how the taxonomy provides structure for understanding how modern AI systems fail, how adversaries exploit these failures, and how organizations can build defenses across the AI lifecycle that evolve alongside capability advancements.

Building from Scratch: A Multi-Agent Framework with Human-in-the-Loop for Multilingual Legal Terminology Mapping

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Lingyi Meng Β· Maolin Liu Β· Hao Wang Β· Yilan Cheng Β· Qi Yang Β· Idlkaid Mohanmmed
16 December 2025 at 13:00
arXiv:2512.12950v1 Announce Type: cross Abstract: Accurately mapping legal terminology across languages remains a significant challenge, especially for language pairs like Chinese and Japanese, which share a large number of homographs with different meanings. Existing resources and standardized tools for these languages are limited. To address this, we propose a human-AI collaborative approach for building a multilingual legal terminology database, based on a multi-agent framework. This approach integrates advanced large language models and legal domain experts throughout the entire process-from raw document preprocessing, article-level alignment, to terminology extraction, mapping, and quality assurance. Unlike a single automated pipeline, our approach places greater emphasis on how human experts participate in this multi-agent system. Humans and AI agents take on different roles: AI agents handle specific, repetitive tasks, such as OCR, text segmentation, semantic alignment, and initial terminology extraction, while human experts provide crucial oversight, review, and supervise the outputs with contextual knowledge and legal judgment. We tested the effectiveness of this framework using a trilingual parallel corpus comprising 35 key Chinese statutes, along with their English and Japanese translations. The experimental results show that this human-in-the-loop, multi-agent workflow not only improves the precision and consistency of multilingual legal terminology mapping but also offers greater scalability compared to traditional manual methods.

From User Interface to Agent Interface: Efficiency Optimization of UI Representations for LLM Agents

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Dezhi Ran Β· Zhi Gong Β· Yuzhe Guo Β· Mengzhou Wu Β· Yuan Cao Β· Haochuan Lu Β· Hengyu Zhang Β· Xia Zeng Β· Gang Cao Β· Liangchao Yao Β· Yuetang Deng Β· Wei Yang Β· Tao Xie
16 December 2025 at 13:00
arXiv:2512.13438v1 Announce Type: cross Abstract: While Large Language Model (LLM) agents show great potential for automated UI navigation such as automated UI testing and AI assistants, their efficiency has been largely overlooked. Our motivating study reveals that inefficient UI representation creates a critical performance bottleneck. However, UI representation optimization, formulated as the task of automatically generating programs that transform UI representations, faces two unique challenges. First, the lack of Boolean oracles, which traditional program synthesis uses to decisively validate semantic correctness, poses a fundamental challenge to co-optimization of token efficiency and completeness. Second, the need to process large, complex UI trees as input while generating long, compositional transformation programs, making the search space vast and error-prone. Toward addressing the preceding limitations, we present UIFormer, the first automated optimization framework that synthesizes UI transformation programs by conducting constraint-based optimization with structured decomposition of the complex synthesis task. First, UIFormer restricts the program space using a domain-specific language (DSL) that captures UI-specific operations. Second, UIFormer conducts LLM-based iterative refinement with correctness and efficiency rewards, providing guidance for achieving the efficiency-completeness co-optimization. UIFormer operates as a lightweight plugin that applies transformation programs for seamless integration with existing LLM agents, requiring minimal modifications to their core logic. Evaluations across three UI navigation benchmarks spanning Android and Web platforms with five LLMs demonstrate that UIFormer achieves 48.7% to 55.8% token reduction with minimal runtime overhead while maintaining or improving agent performance. Real-world industry deployment at WeChat further validates the practical impact of UIFormer.
  • βœ‡cs.AI, q-bio.NC updates on arXiv.org
  • The Traitors: Deception and Trust in Multi-Agent Language Model Simulations Pedro M. P. Curvo
    arXiv:2505.12923v2 Announce Type: replace Abstract: As AI systems increasingly assume roles where trust and alignment with human values are essential, understanding when and why they engage in deception has become a critical research priority. We introduce The Traitors, a multi-agent simulation framework inspired by social deduction games, designed to probe deception, trust formation, and strategic communication among large language model (LLM) agents under asymmetric information. A minority of
     
  • 🏷️
  • ↗️

The Traitors: Deception and Trust in Multi-Agent Language Model Simulations

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Pedro M. P. Curvo
16 December 2025 at 13:00
arXiv:2505.12923v2 Announce Type: replace Abstract: As AI systems increasingly assume roles where trust and alignment with human values are essential, understanding when and why they engage in deception has become a critical research priority. We introduce The Traitors, a multi-agent simulation framework inspired by social deduction games, designed to probe deception, trust formation, and strategic communication among large language model (LLM) agents under asymmetric information. A minority of agents the traitors seek to mislead the majority, while the faithful must infer hidden identities through dialogue and reasoning. Our contributions are: (1) we ground the environment in formal frameworks from game theory, behavioral economics, and social cognition; (2) we develop a suite of evaluation metrics capturing deception success, trust dynamics, and collective inference quality; (3) we implement a fully autonomous simulation platform where LLMs reason over persistent memory and evolving social dynamics, with support for heterogeneous agent populations, specialized traits, and adaptive behaviors. Our initial experiments across DeepSeek-V3, GPT-4o-mini, and GPT-4o (10 runs per model) reveal a notable asymmetry: advanced models like GPT-4o demonstrate superior deceptive capabilities yet exhibit disproportionate vulnerability to others' falsehoods. This suggests deception skills may scale faster than detection abilities. Overall, The Traitors provides a focused, configurable testbed for investigating LLM behavior in socially nuanced interactions. We position this work as a contribution toward more rigorous research on deception mechanisms, alignment challenges, and the broader social reliability of AI systems.

M^3-Bench: Multi-Modal, Multi-Hop, Multi-Threaded Tool-Using MLLM Agent Benchmark

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yang Zhou Β· Mingyu Zhao Β· Zhenting Wang Β· Difei Gu Β· Bangwei Guo Β· Ruosong Ye Β· Ligong Han Β· Can Jin Β· Dimitris N. Metaxas
16 December 2025 at 13:00
arXiv:2511.17729v3 Announce Type: replace Abstract: We present M^3-Bench, the first benchmark for evaluating multimodal tool use under the Model Context Protocol. The benchmark targets realistic, multi-hop and multi-threaded workflows that require visual grounding and textual reasoning, cross-tool dependencies, and persistence of intermediate resources across steps. We introduce a similarity-driven alignment that serializes each tool call, embeds signatures with a sentence encoder, and performs similarity-bucketed Hungarian matching to obtain auditable one-to-one correspondences. On top of this alignment, we report interpretable metrics that decouple semantic fidelity from workflow consistency. The benchmark spans 28 servers with 231 tools, and provides standardized trajectories curated through an Executor & Judge pipeline with human verification; an auxiliary four large language models (LLMs) judge ensemble reports end-task Task Completion and information grounding. Evaluations of representative state-of-the-art Multimodal LLMs (MLLMs) reveal persistent gaps in multimodal MCP tool use, particularly in argument fidelity and structure consistency, underscoring the need for methods that jointly reason over images, text, and tool graphs. Our Benchmark's anonymous repository is at https://github.com/EtaYang10th/Open-M3-Bench

Privacy Ethics Alignment in AI: A Stakeholder-Centric Framework for Ethical AI

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Ankur Barthwal Β· Molly Campbell Β· Ajay Kumar Shrestha
16 December 2025 at 13:00
arXiv:2503.11950v4 Announce Type: replace-cross Abstract: The increasing integration of artificial intelligence (AI) in digital ecosystems has reshaped privacy dynamics, particularly for young digital citizens navigating data-driven environments. This study explores evolving privacy concerns across three key stakeholder groups-young digital citizens, parents/educators, and AI professionals-and assesses differences in data ownership, trust, transparency, parental mediation, education, and risk-benefit perceptions. Employing a grounded theory methodology, this research synthesizes insights from key participants through structured surveys, qualitative interviews, and focus groups to identify distinct privacy expectations. Young digital citizens emphasized autonomy and digital agency, while parents and educators prioritized oversight and AI literacy. AI professionals focused on balancing ethical design with system performance. The analysis revealed significant gaps in transparency and digital literacy, underscoring the need for inclusive, stakeholder-driven privacy frameworks. Drawing on comparative thematic analysis, this study introduces the Privacy-Ethics Alignment in AI (PEA-AI) model, which conceptualizes privacy decision-making as a dynamic negotiation among stakeholders. By aligning empirical findings with governance implications, this research provides a scalable foundation for adaptive, youth-centered AI privacy governance.

Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Waldemar Hahn, Jan-Niklas Eckardt, Christoph R\"ollig, Martin Sedlmayr, Jan Moritz Middeke, Markus Wolfien
16 December 2025 at 13:00
arXiv:2505.05019v2 Announce Type: replace-cross Abstract: The generation of synthetic clinical trial data offers a promising approach to mitigating privacy concerns and data accessibility limitations in medical research. However, ensuring that synthetic datasets maintain high fidelity, utility, and adherence to domain-specific constraints remains a key challenge. While hyperparameter optimization (HPO) improves generative model performance, the effectiveness of different optimization strategies for synthetic clinical data remains unclear. This study systematically evaluates four HPO objectives across nine generative models, comparing single-metric to compound metric optimization. Our results demonstrate that HPO consistently improves synthetic data quality, with Tab DDPM achieving the largest relative gains, followed by TVAE (60%), CTGAN (39%), and CTAB-GAN+ (38%). Compound metric optimization outperformed single-metric objectives, producing more generalizable synthetic datasets. Despite improving overall quality, HPO alone fails to prevent violations of essential clinical survival constraints. Preprocessing and postprocessing played a crucial role in reducing these violations, as models lacking robust processing steps produced invalid data in up to 61% of cases. These findings underscore the necessity of integrating explicit domain knowledge alongside HPO to generate high-quality synthetic datasets. Our study provides actionable recommendations for improving synthetic data generation, with future work needed to refine metric selection and validate findings on larger datasets.
  • βœ‡cs.AI, q-bio.NC updates on arXiv.org
  • A Collectivist, Economic Perspective on AI Michael I. Jordan
    arXiv:2507.06268v3 Announce Type: replace-cross Abstract: Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word ``intelligence'' is being used as a North Star for the development of this technology, with human cognition viewed as a baseline. This view neglects the fact that humans are social animals and that much of our intelligence is social and cultural in origin. Moreover, faili
     
  • 🏷️
  • ↗️

A Collectivist, Economic Perspective on AI

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Michael I. Jordan
16 December 2025 at 13:00
arXiv:2507.06268v3 Announce Type: replace-cross Abstract: Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word ``intelligence'' is being used as a North Star for the development of this technology, with human cognition viewed as a baseline. This view neglects the fact that humans are social animals and that much of our intelligence is social and cultural in origin. Moreover, failing to properly situate aspects of intelligence at the social level contributes to the treatment of the societal consequences of technology as an afterthought. The path forward is not merely more data and compute, and not merely more attention paid to cognitive or symbolic representations, but a thorough blending of economic and social concepts with computational and inferential concepts at the level of algorithm design.
❌