{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:51:50Z","timestamp":1783119110008,"version":"3.54.6"},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013290","name":"National Key Research and Development Program of China Stem Cell and Translational Research","doi-asserted-by":"publisher","award":["2022YFB4003801"],"award-info":[{"award-number":["2022YFB4003801"]}],"id":[{"id":"10.13039\/501100013290","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.eswa.2026.132292","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:57:58Z","timestamp":1775145478000},"page":"132292","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["Geometric prompt optimization: An efficient framework for engineering applications of large language models"],"prefix":"10.1016","volume":"321","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8881-9063","authenticated-orcid":false,"given":"Qianqi","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3200-3860","authenticated-orcid":false,"given":"Zeling","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8492-7684","authenticated-orcid":false,"given":"Yuntao","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8134-0538","authenticated-orcid":false,"given":"Dagang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132292_sbref0001","series-title":"Annual meeting of the association for computational linguistics","article-title":"Intrinsic dimensionality explains the effectiveness of language model fine-tuning","author":"Aghajanyan","year":"2020"},{"issue":"8","key":"10.1016\/j.eswa.2026.132292_bib0002","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/JAS.2021.1004075","article-title":"Learning convex optimization models","volume":"8","author":"Agrawal","year":"2021","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.eswa.2026.132292_sbref0003","series-title":"The twelfth international conference on learning representations","article-title":"Task structure and nonlinearity jointly determine learned representational geometry","author":"Alleman","year":"2024"},{"issue":"8","key":"10.1016\/j.eswa.2026.132292_bib0004","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132292_bib0005","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"17682","article-title":"Graph of thoughts: Solving elaborate problems with large language models","volume":"vol. 38","author":"Besta","year":"2024"},{"issue":"2","key":"10.1016\/j.eswa.2026.132292_bib0006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3604932","article-title":"Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries","volume":"56","author":"Besta","year":"2023","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.eswa.2026.132292_sbref0007","series-title":"The twelfth international conference on learning representations","article-title":"Turning large language models into cognitive models","author":"Binz","year":"2024"},{"key":"10.1016\/j.eswa.2026.132292_bib0008","unstructured":"Bronstein, M. M., Bruna, J., Cohen, T., & Veli\u010dkovi\u0107, P. (2021). Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv: 2104.13478."},{"key":"10.1016\/j.eswa.2026.132292_bib0009","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132292_bib0010","first-page":"4869","article-title":"Hyperbolic graph convolutional neural networks","volume":"32","author":"Chami","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"3","key":"10.1016\/j.eswa.2026.132292_bib0011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3641289","article-title":"A survey on evaluation of large language models","volume":"15","author":"Chang","year":"2024","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"10.1016\/j.eswa.2026.132292_bib0012","first-page":"8294","article-title":"Measuring generalization with optimal transport","volume":"34","author":"Chuang","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132292_bib0013","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.conb.2021.10.010","article-title":"Neural population geometry: An approach for understanding biological and artificial neural networks","volume":"70","author":"Chung","year":"2021","journal-title":"Current Opinion in Neurobiology"},{"issue":"1","key":"10.1016\/j.eswa.2026.132292_bib0014","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1038\/s41467-020-14578-5","article-title":"Separability and geometry of object manifolds in deep neural networks","volume":"11","author":"Cohen","year":"2020","journal-title":"Nature Communications"},{"issue":"9","key":"10.1016\/j.eswa.2026.132292_bib0015","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1109\/TPAMI.2016.2615921","article-title":"Optimal transport for domain adaptation","volume":"39","author":"Courty","year":"2016","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132292_bib0016","first-page":"2292","article-title":"Sinkhorn distances: Lightspeed computation of optimal transport","volume":"26","author":"Cuturi","year":"2013","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132292_bib0017","series-title":"The eleventh international conference on learning representations","article-title":"Latent graph inference using product manifolds","author":"de Oc\u00e1riz Borde","year":"2023"},{"key":"10.1016\/j.eswa.2026.132292_bib0018","series-title":"Findings of the association for computational linguistics ACL 2024","first-page":"3563","article-title":"Chain-of-verification reduces hallucination in large language models","author":"Dhuliawala","year":"2024"},{"key":"10.1016\/j.eswa.2026.132292_bib0019","series-title":"Findings of the association for computational linguistics: EMNLP 2021","first-page":"1935","article-title":"Grouped-attention for content-selection and content-plan generation","author":"Distiawan","year":"2021"},{"issue":"4","key":"10.1016\/j.eswa.2026.132292_bib0020","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1038\/s42256-025-01010-0","article-title":"Optimal transport for generating transition states in chemical reactions","volume":"7","author":"Duan","year":"2025","journal-title":"Nature Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132292_bib0021","series-title":"International conference on machine learning","first-page":"1367","article-title":"Computational optimal transport: Complexity by accelerated gradient descent is better than by sinkhorn\u2019s algorithm","author":"Dvurechensky","year":"2018"},{"issue":"2","key":"10.1016\/j.eswa.2026.132292_bib0022","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1038\/s42256-021-00438-4","article-title":"Geometry-enhanced molecular representation learning for property prediction","volume":"4","author":"Fang","year":"2022","journal-title":"Nature Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132292_bib0023","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JAS.2025.125165","article-title":"Unsupervised dynamic discrete structure learning: A geometric evolution method","volume":"12","author":"Fei","year":"2025","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"78","key":"10.1016\/j.eswa.2026.132292_bib0024","first-page":"1","article-title":"Pot: Python optimal transport","volume":"22","author":"Flamary","year":"2021","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.132292_bib0025","first-page":"14504","article-title":"A confederacy of models: A comprehensive evaluation of LLMs on creative writing","author":"G\u00f3mez-Rodr\u00edguez","year":"2023","journal-title":"Findings of the Association for Computational Linguistics: EMNLP 2023"},{"issue":"7","key":"10.1016\/j.eswa.2026.132292_bib0026","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1038\/s42256-024-00863-1","article-title":"Automated construction of cognitive maps with visual predictive coding","volume":"6","author":"Gornet","year":"2024","journal-title":"Nature Machine Intelligence"},{"issue":"3","key":"10.1016\/j.eswa.2026.132292_bib0027","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Computational Visual Media"},{"key":"10.1016\/j.eswa.2026.132292_sbref0027","series-title":"The twelfth international conference on learning representations, ICLR 2024, Vienna, Austria, May 7-11, 2024","article-title":"Connecting large language models with evolutionary algorithms yields powerful prompt optimizers","author":"Guo","year":"2024"},{"issue":"11","key":"10.1016\/j.eswa.2026.132292_bib0029","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2413449122","article-title":"A unified neural representation model for spatial and conceptual computations","volume":"122","author":"Haga","year":"2025","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"2","key":"10.1016\/j.eswa.2026.132292_bib0030","first-page":"3","article-title":"Lora: Low-rank adaptation of large language models","volume":"1","author":"Hu","year":"2022","journal-title":"ICLR"},{"key":"10.1016\/j.eswa.2026.132292_bib0031","first-page":"29705","article-title":"Manifold interpolating optimal-transport flows for trajectory inference","volume":"35","author":"Huguet","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.1016\/j.eswa.2026.132292_bib0032","doi-asserted-by":"crossref","first-page":"8506","DOI":"10.1038\/s41467-023-43958-w","article-title":"Revealing hidden patterns in deep neural network feature space continuum via manifold learning","volume":"14","author":"Islam","year":"2023","journal-title":"Nature Communications"},{"key":"10.1016\/j.eswa.2026.132292_bib0033","first-page":"22199","article-title":"Large language models are zero-shot reasoners","volume":"35","author":"Kojima","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132292_bib0034","first-page":"14593","article-title":"Do neural optimal transport solvers work? A continuous wasserstein-2 benchmark","volume":"34","author":"Korotin","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132292_bib0035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cagd.2018.10.005","article-title":"A geometric view of optimal transportation and generative model","volume":"68","author":"Lei","year":"2019","journal-title":"Computer Aided Geometric Design"},{"issue":"27","key":"10.1016\/j.eswa.2026.132292_bib0036","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2311805121","article-title":"Representations and generalization in artificial and brain neural networks","volume":"121","author":"Li","year":"2024","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"9","key":"10.1016\/j.eswa.2026.132292_bib0037","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/JAS.2023.123399","article-title":"Geometry flow-based deep riemannian metric learning","volume":"10","author":"Li","year":"2023","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.eswa.2026.132292_sbref0037","series-title":"ICML 2024 workshop on models of human feedback for AI alignment","article-title":"Prompt optimization with human feedback","author":"Lin","year":"2024"},{"issue":"9","key":"10.1016\/j.eswa.2026.132292_bib0039","first-page":"1","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu","year":"2023","journal-title":"ACM Computing Surveys"},{"issue":"7","key":"10.1016\/j.eswa.2026.132292_bib0040","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/JAS.2020.1003402","article-title":"Global-attention-based neural networks for vision language intelligence","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"9","key":"10.1016\/j.eswa.2026.132292_bib0041","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1109\/JAS.2024.124332","article-title":"Cognitive navigation for intelligent mobile robots: A learning-based approach with topological memory configuration","volume":"11","author":"Liu","year":"2024","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"1","key":"10.1016\/j.eswa.2026.132292_bib0042","doi-asserted-by":"crossref","first-page":"3252","DOI":"10.1038\/s41467-025-58532-9","article-title":"Optimization on multifractal loss landscapes explains a diverse range of geometrical and dynamical properties of deep learning","volume":"16","author":"Ly","year":"2025","journal-title":"Nature Communications"},{"issue":"1","key":"10.1016\/j.eswa.2026.132292_bib0043","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1146\/annurev-statistics-040522-115238","article-title":"Manifold learning: What, how, and why","volume":"11","author":"Meil\u0103","year":"2024","journal-title":"Annual Review of Statistics and Its Application"},{"issue":"2","key":"10.1016\/j.eswa.2026.132292_bib0044","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1109\/TPAMI.2024.3489030","article-title":"Recent advances in optimal transport for machine learning","volume":"47","author":"Montesuma","year":"2025","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.132292_sbref0045","series-title":"International conference on learning representations","article-title":"Generative modeling with optimal transport maps","author":"Rout","year":"2022"},{"key":"10.1016\/j.eswa.2026.132292_bib0046","series-title":"International conference on machine learning","first-page":"9276","article-title":"Momentum residual neural networks","author":"Sander","year":"2021"},{"key":"10.1016\/j.eswa.2026.132292_bib0047","first-page":"55565","article-title":"Are emergent abilities of large language models a mirage?","volume":"36","author":"Schaeffer","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"45","key":"10.1016\/j.eswa.2026.132292_bib0048","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2105646118","article-title":"The neural architecture of language: Integrative modeling converges on predictive processing","volume":"118","author":"Schrimpf","year":"2021","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"10.1016\/j.eswa.2026.132292_bib0049","series-title":"International conference on machine learning","first-page":"30706","article-title":"Synthetic prompting: Generating chain-of-thought demonstrations for large language models","author":"Shao","year":"2023"},{"issue":"4","key":"10.1016\/j.eswa.2026.132292_bib0050","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/JAS.2017.7510583","article-title":"Generative adversarial networks: Introduction and outlook","volume":"4","author":"Wang","year":"2017","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.eswa.2026.132292_sbref0051","series-title":"The eleventh international conference on learning representations","article-title":"Self-consistency improves chain of thought reasoning in language models","author":"Wang","year":"2023"},{"key":"10.1016\/j.eswa.2026.132292_sbref0052","article-title":"Emergent abilities of large language models","author":"Wei","year":"2022","journal-title":"Transactions on Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.132292_bib0053","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132292_bib0054","first-page":"51008","article-title":"Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery","volume":"36","author":"Wen","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"6","key":"10.1016\/j.eswa.2026.132292_bib0055","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1109\/JAS.2019.1911771","article-title":"A novel statistical manifold algorithm for position estimation","volume":"6","author":"Xia","year":"2019","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.eswa.2026.132292_bib0056","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"27723","article-title":"Evaluating mathematical reasoning beyond accuracy","volume":"vol. 39","author":"Xia","year":"2025"},{"issue":"7","key":"10.1016\/j.eswa.2026.132292_bib0057","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/JAS.2024.124344","article-title":"Low-rank optimal transport for robust domain adaptation","volume":"11","author":"Xu","year":"2024","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"10.1016\/j.eswa.2026.132292_sbref0058","series-title":"The twelfth international conference on learning representations, ICLR 2024, Vienna, Austria, May 7-11, 2024","article-title":"Large language models as optimizers","author":"Yang","year":"2024"},{"key":"10.1016\/j.eswa.2026.132292_bib0059","first-page":"11809","article-title":"Tree of thoughts: Deliberate problem solving with large language models","volume":"36","author":"Yao","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132292_bib0060","series-title":"International conference on learning representations (ICLR)","article-title":"React: Synergizing reasoning and acting in language models","author":"Yao","year":"2023"},{"issue":"1","key":"10.1016\/j.eswa.2026.132292_bib0061","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1038\/s41593-022-01212-4","article-title":"Hippocampal spatial representations exhibit a hyperbolic geometry that expands with experience","volume":"26","author":"Zhang","year":"2023","journal-title":"Nature Neuroscience"},{"key":"10.1016\/j.eswa.2026.132292_bib0062","unstructured":"Zhang, Z. (2025). Comprehension without competence: Architectural limits of LLMs in symbolic computation and reasoning. arXiv preprint arXiv: 2507.10624."},{"key":"10.1016\/j.eswa.2026.132292_sbref0063","article-title":"Multimodal chain-of-thought reasoning in language models","volume":"2024","author":"Zhang","year":"2023","journal-title":"Transactions on Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.132292_bib0064","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"19724","article-title":"Memorybank: Enhancing large language models with long-term memory","volume":"vol. 38","author":"Zhong","year":"2024"},{"key":"10.1016\/j.eswa.2026.132292_bib0065","article-title":"Transformer-driven multi-center manifold modeling deep SVDD for anomaly detection in IIoT networks","author":"Zhong","year":"2026","journal-title":"IEEE Internet of Things Journal"},{"key":"10.1016\/j.eswa.2026.132292_sbref0066","series-title":"The eleventh international conference on learning representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023","article-title":"Large language models are human-level prompt engineers","author":"Zhou","year":"2023"},{"issue":"276","key":"10.1016\/j.eswa.2026.132292_bib0067","first-page":"1","article-title":"Functional optimal transport: Regularized map estimation and domain adaptation for functional data","volume":"25","author":"Zhu","year":"2024","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.132292_bib0068","article-title":"Generative AI-driven dynamic information prioritization for enhanced autonomous driving","author":"Zou","year":"2025","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.eswa.2026.132292_bib0069","doi-asserted-by":"crossref","DOI":"10.1109\/TITS.2025.3572404","article-title":"Few-shot learning with manifold-enhanced LLM for handling anomalous perception inputs in autonomous driving","author":"Zou","year":"2025","journal-title":"IEEE Transactions on Intelligent Transportation Systems"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426012054?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426012054?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T15:59:31Z","timestamp":1780934371000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426012054"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":69,"alternative-id":["S0957417426012054"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132292","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Geometric prompt optimization: An efficient framework for engineering applications of large language models","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132292","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132292"}}