IF 7.4

Patterns Pub Date : 2026-04-10 DOI: 10.1016/j.patter.2026.101539

Alec D MacCall

引用次数: 0

Intentional policy graphs: A pipeline for explaining agent behavior through intentions. 意向策略图：通过意向解释代理行为的管道。

IF 7.4

Patterns Pub Date : 2026-04-10 DOI: 10.1016/j.patter.2026.101513

Victor Gimenez-Abalos, Sergio Alvarez-Napagao, Adrian Tormos, Sara Montese, Ulises Cortés, Javier Vázquez-Salceda

{"title":"Intentional policy graphs: A pipeline for explaining agent behavior through intentions.","authors":"Victor Gimenez-Abalos, Sergio Alvarez-Napagao, Adrian Tormos, Sara Montese, Ulises Cortés, Javier Vázquez-Salceda","doi":"10.1016/j.patter.2026.101513","DOIUrl":"10.1016/j.patter.2026.101513","url":null,"abstract":"Agents increasingly operate in complex environments, where coherent behavior often emerges from opaque decision-making processes. While such systems can be highly effective, this lack of transparency limits trust, auditing, and meaningful human understanding. We introduce intentional policy graphs, a post hoc, model-agnostic framework that explains agent behavior in terms of intentions: probabilistic commitments to desired outcomes inferred from partial observations. By extending policy graphs with a formal notion of intention, we move beyond action-level descriptions toward telic explanations of why agents pursue particular trajectories. The framework provides a complete construction pipeline, design principles, and quantitative metrics that explicitly characterize the trade-off between interpretability and reliability. Intentions support structured answers to what, how, and why questions, enabling both local and global explanations of behavior. We demonstrate the approach in a cooperative multi-agent game and on real-world human driving data, highlighting its generality and explanatory power without access to internal reasoning models.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 4","pages":"101513"},"PeriodicalIF":7.4,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13083655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Organizing across disciplines to tackle shared computational challenges. 跨学科组织以解决共享的计算挑战。

IF 7.4

Patterns Pub Date : 2026-04-10 DOI: 10.1016/j.patter.2026.101518

Wojtek Treyde, Aleksy Kwiatkowski, Jascha Achterberg, Danyal Akarca, Martin Buttenschoen, Rory T Byrne, Kieran Didi, Kristina Kordova, Jakub Lála, Jonathon Langford, Austin M Mroz, Puria Radmard, Filip T Szczypiński, Eva Sevenster, Inga Van den Bossche, Michał Wójcik

引用次数: 0

Breaking the glass ceiling: An interview with Yuhao (Jet) Zhou, María Óskarsdóttir, and Cristián Bravo. 打破玻璃天花板：采访周宇浩（Jet） María Óskarsdóttir和Cristián Bravo。

IF 7.4

Patterns Pub Date : 2026-04-10 DOI: 10.1016/j.patter.2026.101541

Yuhao Zhou, María Óskarsdóttir, Cristián Bravo

引用次数: 0

AI for scientific discovery is a social problem. 人工智能用于科学发现是一个社会问题。

IF 7.4

Patterns Pub Date : 2026-03-13 DOI: 10.1016/j.patter.2026.101497

Georgia Channing, Avijit Ghosh

{"title":"AI for scientific discovery is a social problem.","authors":"Georgia Channing, Avijit Ghosh","doi":"10.1016/j.patter.2026.101497","DOIUrl":"https://doi.org/10.1016/j.patter.2026.101497","url":null,"abstract":"AI is being increasingly applied to scientific research, but its benefits remain unevenly distributed across different communities and disciplines. While technical challenges such as limited data, fragmented standards, and unequal access to computational resources are already well known, social and institutional factors are often the primary constraints. Narratives emphasizing autonomous \"AI scientists,\" the underrecognition of data and infrastructure work, misaligned incentives, and gaps between domain experts and machine-learning researchers all limit the impact of AI on scientific discovery. Four interconnected challenges are highlighted in this paper: community coordination, the misalignment of research priorities with upstream needs, data fragmentation, and infrastructure inequities. We argue that addressing these challenges requires not only technical innovations but also intentional community-building efforts, cross-disciplinary education, shared benchmarks, and accessible infrastructure. We call for reframing AI for science as a collective social project, where sustainable collaboration and equitable participation are treated as prerequisites for achieving technical progress.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 3","pages":"101497"},"PeriodicalIF":7.4,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13100680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Agentic AI as a coordination paradigm in digital health and agri-food systems. 人工智能作为数字卫生和农业粮食系统的协调范例。

IF 7.4

Patterns Pub Date : 2026-03-13 DOI: 10.1016/j.patter.2026.101496

Anand K Gavai, Miranda P M Meuwissen

{"title":"Agentic AI as a coordination paradigm in digital health and agri-food systems.","authors":"Anand K Gavai, Miranda P M Meuwissen","doi":"10.1016/j.patter.2026.101496","DOIUrl":"https://doi.org/10.1016/j.patter.2026.101496","url":null,"abstract":"Digital health and agri-food data systems increasingly rely on sophisticated machine learning and data-sharing infrastructures. Yet persistent challenges in scalability, accountability, and public trust indicate that technical capability alone does not resolve systemic failure. This perspective argues that these limitations primarily arise from architectural misalignment with governance rather than from algorithmic insufficiency. Through a comparative examination of federated learning, blockchain-based infrastructures, and FAIR-aligned platforms, recurring coordination bottlenecks are identified across both health and agricultural domains. Building on these observations, this perspective introduces an agentic coordination model in which task-bounded agentic components operate under explicit institutional and regulatory constraints. The model context protocol (MCP) is presented as a reference mechanism for mediating policy, provenance, and accountability across distributed agents without centralizing control. Rather than prescribing a universal solution, this work frames agentic architectures as a governance-aware design space for future digital health and food systems.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 3","pages":"101496"},"PeriodicalIF":7.4,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13100677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping the flow of painterly gesture. 绘制绘画式手势的流程。

IF 7.4

Patterns Pub Date : 2026-03-13 DOI: 10.1016/j.patter.2026.101516

Lizhen Zhu, Chaewan Chun, Kathryn Brown, James Z Wang

引用次数: 0

Unveiling gender disparities in corporate board career paths using deep learning. 利用深度学习揭示公司董事会职业道路中的性别差异。

IF 7.4

Patterns Pub Date : 2026-03-12 eCollection Date: 2026-04-10 DOI: 10.1016/j.patter.2026.101495

Yuhao Zhou, Wenhao Chen, María Óskarsdóttir, Matt Davison, Cristián Bravo

引用次数: 0

A call to join a collective effort on AI evaluation. 呼吁加入人工智能评估的集体努力。

IF 7.4

Patterns Pub Date : 2026-03-06 eCollection Date: 2026-03-13 DOI: 10.1016/j.patter.2026.101512

Irving Torres

引用次数: 0

Modeling of longitudinal immune profiles reveals distinct immunogenic signatures following five COVID-19 vaccinations among people living with HIV. 纵向免疫谱建模揭示了艾滋病毒感染者接种五次COVID-19疫苗后不同的免疫原性特征。

IF 7.4

Patterns Pub Date : 2026-03-04 eCollection Date: 2026-03-13 DOI: 10.1016/j.patter.2025.101474

Chapin S Korosec, Jessica M Conway, Vitaliy A Matveev, Mario Ostrowski, Jane M Heffernan, Mohammad Sajjad Ghaemi

{"title":"Modeling of longitudinal immune profiles reveals distinct immunogenic signatures following five COVID-19 vaccinations among people living with HIV.","authors":"Chapin S Korosec, Jessica M Conway, Vitaliy A Matveev, Mario Ostrowski, Jane M Heffernan, Mohammad Sajjad Ghaemi","doi":"10.1016/j.patter.2025.101474","DOIUrl":"https://doi.org/10.1016/j.patter.2025.101474","url":null,"abstract":"The immune response to vaccination is highly heterogeneous and arises from a dynamic interplay of immune components. Harnessing machine learning (ML) to learn immune interdependencies offers the potential not only to decode immune signatures linked to a specified comorbidity but also to reveal individualized patterns laying the groundwork for precision-guided vaccination and targeted clinical follow-up. We employ a random forest (RF) approach to classify informative differences in immunogenicity between older people living with HIV (PLWH) on antiretroviral therapy (ART) and an age-matched control group who received up to five SARS-CoV-2 vaccinations. RFs identify evidence for T helper 1 (Th1) imprinting and reveal novel distinguishing immune features, such as saliva-based antibody screening, as promising diagnostic tools (whereas serum IgG is not). Our modeling approach reveals a subset of PLWH whose immune signatures are indistinguishable from the HIV- control group, which we interpret as near-complete immune restoration from a longitudinal vaccine-elicited immunogenic perspective. To expand the utility of our findings, we generate privacy-preserving synthetic \"virtual patients\" that accurately approximate the original longitudinal immunologic data and show, via train-on-synthetic/test-on-real evaluation, that RF classifiers trained solely on virtual patients generalize to held-out real patients. Our results highlight the effectiveness in utilizing informative immune feature interdependencies for classification tasks and suggest broad impacts of ML applications for personalized vaccination strategies among high-risk populations.","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"7 3","pages":"101474"},"PeriodicalIF":7.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13100683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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