{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:05:13Z","timestamp":1784203513421,"version":"3.55.0"},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010880","name":"State Grid Corporation of China","doi-asserted-by":"publisher","award":["5700-202458333A-2-1-ZX"],"award-info":[{"award-number":["5700-202458333A-2-1-ZX"]}],"id":[{"id":"10.13039\/501100010880","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.neunet.2026.109037","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T15:47:21Z","timestamp":1776959241000},"page":"109037","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Tool retrieval bridge: Aligning vague instructions with retriever preferences via bridge model"],"prefix":"10.1016","volume":"202","author":[{"given":"Kunfeng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1653-9843","authenticated-orcid":false,"given":"Luyao","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3907-8820","authenticated-orcid":false,"given":"Juhua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109037_bib0001","unstructured":"Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S. et al. (2023). Gpt-4 technical report. arXiv preprint arXiv: 2303.08774."},{"issue":"1","key":"10.1016\/j.neunet.2026.109037_bib0002","doi-asserted-by":"crossref","first-page":"226","DOI":"10.3390\/su14010226","article-title":"Topic extraction and interactive knowledge graphs for learning resources","volume":"14","author":"Badawy","year":"2021","journal-title":"Sustainability"},{"key":"10.1016\/j.neunet.2026.109037_bib0003","unstructured":"Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F. et al. (2023). Qwen technical report. arXiv preprint arXiv: 2309.16609."},{"key":"10.1016\/j.neunet.2026.109037_bib0004","series-title":"Thirty-fourth conference on neural information processing systems","article-title":"Language models are few-shot learners","author":"Brown","year":"2020"},{"key":"10.1016\/j.neunet.2026.109037_bib0005","series-title":"The twelfth international conference on learning representations","article-title":"Large language models as tool makers","author":"Cai","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0006","series-title":"Proceedings of the 63th annual meeting of the association for computational linguistics","article-title":"ToolCoder: A systematic code-empowered tool learning framework for large language models","author":"Ding","year":"2025"},{"key":"10.1016\/j.neunet.2026.109037_bib0007","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1961","article-title":"Detecting cyberbullying using deep learning techniques: A pre-trained glove and focal loss technique","volume":"10","author":"El Koshiry","year":"2024","journal-title":"PeerJ Computer Science"},{"issue":"3","key":"10.1016\/j.neunet.2026.109037_bib0008","first-page":"39","article-title":"Building an effective and accurate associative classifier based on support vector machine","volume":"164","author":"Farghaly","year":"2020","journal-title":"Sylwan"},{"key":"10.1016\/j.neunet.2026.109037_bib0009","series-title":"Computer science on-line conference","first-page":"56","article-title":"Developing an efficient method for automatic threshold detection based on hybrid feature selection approach","author":"Farghaly","year":"2020"},{"key":"10.1016\/j.neunet.2026.109037_bib0010","series-title":"Proceedings of the 61st annual meeting of the association for computational linguistics","first-page":"1762","article-title":"Precise zero-shot dense retrieval without relevance labels","author":"Gao","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0011","series-title":"International conference on machine learning","first-page":"10764","article-title":"Pal: Program-aided language models","author":"Gao","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0012","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"18030","article-title":"Confucius: Iterative tool learning from introspection feedback by easy-to-difficult curriculum","volume":"vol. 38","author":"Gao","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0013","unstructured":"Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A. et al. (2024). The LLama 3 herd of models. arXiv preprint arXiv: 2407.21783."},{"key":"10.1016\/j.neunet.2026.109037_bib0014","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"14953","article-title":"Visual programming: Compositional visual reasoning without training","author":"Gupta","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0015","series-title":"Proceedings of the 37th international conference on neural information processing systems","first-page":"45870","article-title":"ToolkenGPT: Augmenting frozen language models with massive tools via tool embeddings","author":"Hao","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0016","unstructured":"Hsieh, C.-Y., Chen, S.-A., Li, C.-L., Fujii, Y., Ratner, A., Lee, C.-Y., Krishna, R., & Pfister, T. (2023). Tool documentation enables zero-shot tool-usage with large language models. arXiv preprint arXiv: 2308.00675."},{"key":"10.1016\/j.neunet.2026.109037_bib0017","series-title":"Findings of the association for computational linguistics","first-page":"975","article-title":"Planning and editing what you retrieve for enhanced tool learning","author":"Huang","year":"2024"},{"issue":"4","key":"10.1016\/j.neunet.2026.109037_bib0018","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1145\/582415.582418","article-title":"Cumulated gain-based evaluation of IR techniques","volume":"20","author":"J\u00e4rvelin","year":"2002","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"10.1016\/j.neunet.2026.109037_bib0019","series-title":"Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics (NAACL)","first-page":"1","article-title":"Pre-training of deep bidirectional transformers for language understanding","author":"Kenton","year":"2019"},{"key":"10.1016\/j.neunet.2026.109037_bib0020","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.procs.2021.05.080","article-title":"Automatic detection of cyberbullying and abusive language in arabic content on social networks: A survey","volume":"189","author":"Khairy","year":"2021","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.neunet.2026.109037_bib0021","series-title":"Proceedings of the 2024 conference on empirical methods in natural language processing: Industry track","first-page":"371","article-title":"TPTU-v2: Boosting task planning and tool usage of large language model-based agents in real-world industry systems","author":"Kong","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0022","series-title":"Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers)","first-page":"13357","article-title":"On complementarity objectives for hybrid retrieval","author":"Lee","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0023","series-title":"Proceedings of the 2023 conference on empirical methods in natural language processing","first-page":"3102","article-title":"API-bank: A comprehensive benchmark for tool-augmented LLMs","author":"Li","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0024","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1162\/tacl_a_00638","article-title":"Lost in the middle: How language models use long contexts","volume":"12","author":"Liu","year":"2024","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"10.1016\/j.neunet.2026.109037_bib0025","series-title":"Proceedings of the 37th international conference on neural information processing systems","first-page":"43447","article-title":"Chameleon: Plug-and-play compositional reasoning with large language models","author":"Lu","year":"2023"},{"issue":"8","key":"10.1016\/j.neunet.2026.109037_bib0026","first-page":"698","article-title":"A framework for an e-learning system based on semantic web","volume":"5","author":"Mahmoud","year":"2013","journal-title":"International Journal on Computer Science and Engineering"},{"issue":"25","key":"10.1016\/j.neunet.2026.109037_bib0027","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.7258","article-title":"A new feature selection method based on frequent and associated itemsets for text classification","volume":"34","author":"Mamdouh Farghaly","year":"2022","journal-title":"Concurrency and Computation: Practice and Experience"},{"issue":"16","key":"10.1016\/j.neunet.2026.109037_bib0028","doi-asserted-by":"crossref","first-page":"11259","DOI":"10.1007\/s00500-023-08587-x","article-title":"A high-quality feature selection method based on frequent and correlated items for text classification","volume":"27","author":"Mamdouh Farghaly","year":"2023","journal-title":"Soft Computing"},{"issue":"1","key":"10.1016\/j.neunet.2026.109037_bib0029","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-44113-7","article-title":"Quantum computing and machine learning for arabic language sentiment classification in social media","volume":"13","author":"Omar","year":"2023","journal-title":"Scientific Reports"},{"key":"10.1016\/j.neunet.2026.109037_bib0030","series-title":"The international conference on artificial intelligence and computer vision","first-page":"247","article-title":"Comparative performance of machine learning and deep learning algorithms for arabic hate speech detection in OSNS","author":"Omar","year":"2020"},{"key":"10.1016\/j.neunet.2026.109037_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.is.2021.101785","article-title":"Multi-label arabic text classification in online social networks","volume":"100","author":"Omar","year":"2021","journal-title":"Information Systems"},{"key":"10.1016\/j.neunet.2026.109037_bib0032","series-title":"Forty-second international conference on machine learning","article-title":"The berkeley function calling leaderboard (BFCL): From tool use to agentic evaluation of large language models","author":"Patil","year":"2025"},{"key":"10.1016\/j.neunet.2026.109037_bib0033","series-title":"The thirty-eighth annual conference on neural information processing systems","article-title":"Gorilla: Large language model connected with massive APIs","author":"Patil","year":"2024"},{"issue":"4","key":"10.1016\/j.neunet.2026.109037_bib0034","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1109\/TSE.2022.3197063","article-title":"Revisiting, benchmarking and exploring API recommendation: How far are we?","volume":"49","author":"Peng","year":"2022","journal-title":"IEEE Transactions on Software Engineering"},{"key":"10.1016\/j.neunet.2026.109037_bib0035","series-title":"Proceedings of the 62nd annual meeting of the association for computational linguistics","first-page":"1088","article-title":"Tell me more! towards implicit user intention understanding of language model driven agents","author":"Qian","year":"2024"},{"issue":"4","key":"10.1016\/j.neunet.2026.109037_bib0036","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3704435","article-title":"Tool learning with foundation models","volume":"57","author":"Qin","year":"2024","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.neunet.2026.109037_bib0037","series-title":"The twelfth international conference on learning representations","article-title":"ToolLLM: Facilitating large language models to master 16000+ real-world APIs","author":"Qin","year":"2023"},{"issue":"8","key":"10.1016\/j.neunet.2026.109037_bib0038","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-024-40678-2","article-title":"Tool learning with large language models: A survey","volume":"19","author":"Qu","year":"2025","journal-title":"Frontiers of Computer Science"},{"key":"10.1016\/j.neunet.2026.109037_bib0039","series-title":"Thirty-seventh conference on neural information processing systems","article-title":"Direct preference optimization: Your language model is secretly a reward model","author":"Rafailov","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0040","series-title":"Proceedings of the first instructional conference on machine learning","first-page":"29","article-title":"Using TF-IDF to determine word relevance in document queries","volume":"vol. 242","author":"Ramos","year":"2003"},{"issue":"4","key":"10.1016\/j.neunet.2026.109037_bib0041","first-page":"333","article-title":"The probabilistic relevance framework: BM25 and beyond","volume":"3","author":"Robertson","year":"2009","journal-title":"Foundations and Trends\u00ae in Information Retrieval"},{"key":"10.1016\/j.neunet.2026.109037_bib0042","series-title":"NeurIPS 2023 foundation models for decision making workshop","article-title":"TPTU: Task planning and tool usage of large language model-based AI agents","author":"Ruan","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0043","series-title":"Proceedings of the 2022 conference of the North American chapter of the association for computational linguistics: Human language technologies","first-page":"3715","article-title":"ColBERTv2: Effective and efficient retrieval via lightweight late interaction","author":"Santhanam","year":"2022"},{"key":"10.1016\/j.neunet.2026.109037_bib0044","series-title":"Thirty-seventh conference on neural information processing systems","article-title":"ToolFormer: Language models can teach themselves to use tools","author":"Schick","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0045","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv: 1707.06347."},{"key":"10.1016\/j.neunet.2026.109037_bib0046","series-title":"International conference on intelligent manufacturing and energy sustainability","first-page":"463","article-title":"Dynamic two-way sign language interpretation","author":"Shams","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0047","unstructured":"Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., Bi, X., Zhang, H., Zhang, M., Li, Y. K., Wu, Y. et al. (2024). DeepSeekMath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv: 2402.03300."},{"key":"10.1016\/j.neunet.2026.109037_bib0048","series-title":"Proceedings of the ACM on web conference 2025","first-page":"2222","article-title":"Tool learning in the wild: Empowering language models as automatic tool agents","author":"Shi","year":"2025"},{"key":"10.1016\/j.neunet.2026.109037_bib0049","series-title":"Proceedings of the 62nd annual meeting of the association for computational linguistics","first-page":"7584","article-title":"Trial and error: Exploration-based trajectory optimization of LLM agents","author":"Song","year":"2024"},{"issue":"3","key":"10.1016\/j.neunet.2026.109037_bib0050","doi-asserted-by":"crossref","first-page":"181","DOI":"10.3934\/mmc.2023016","article-title":"Fault-tolerant control of a hydraulic servo actuator via adaptive dynamic programming","volume":"3","author":"Stojanovic","year":"2023","journal-title":"Mathematical Modelling and Control"},{"key":"10.1016\/j.neunet.2026.109037_bib0051","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"11888","article-title":"ViperGPT: Visual inference via python execution for reasoning","author":"Sur\u00eds","year":"2023"},{"key":"10.1016\/j.neunet.2026.109037_bib0052","unstructured":"Tang, Q., Deng, Z., Lin, H., Han, X., Liang, Q., Cao, B., & Sun, L. (2023). ToolAlpaca: Generalized tool learning for language models with 3000 simulated cases. arXiv preprint arXiv: 2306.05301."},{"key":"10.1016\/j.neunet.2026.109037_bib0053","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi\u00e8re, B., Goyal, N., Hambro, E., Azhar, F. et al. (2023). LLama: Open and efficient foundation language models. arXiv preprint arXiv: 2302.13971."},{"key":"10.1016\/j.neunet.2026.109037_bib0054","series-title":"Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers)","first-page":"10583","article-title":"Llms in the imaginarium: Tool learning through simulated trial and error","author":"Wang","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0055","series-title":"Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval","first-page":"2983","article-title":"Empowering large language models: Tool learning for real-world interaction","author":"Wang","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0056","unstructured":"Wang, R., Han, X., Ji, L., Wang, S., Baldwin, T., & Li, H. (2024c). ToolGEN: Unified tool retrieval and calling via generation. arXiv preprint arXiv: 2410.03439."},{"key":"10.1016\/j.neunet.2026.109037_bib0057","series-title":"Proceedings of the 2024 conference on empirical methods in natural language processing","first-page":"18315","article-title":"ToolPlanner: A tool augmented LLM for multi granularity instructions with path planning and feedback","author":"Wu","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0058","series-title":"Findings of the association for computational linguistics","first-page":"9609","article-title":"Enhancing tool retrieval with iterative feedback from large language models","author":"Xu","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0059","doi-asserted-by":"crossref","unstructured":"Yuan, S., Song, K., Chen, J., Tan, X., Shen, Y., Ren, K., Li, D., & Yang, D. EasyTool: Enhancing LLM-based agents with concise tool instruction. In ICLR 2024 workshop on large language model (LLM) agents.","DOI":"10.18653\/v1\/2025.naacl-long.44"},{"key":"10.1016\/j.neunet.2026.109037_bib0060","series-title":"Findings of the association for computational linguistics ACL 2024","first-page":"3053","article-title":"AgentTuning: Enabling generalized agent abilities for LLMs","author":"Zeng","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0061","doi-asserted-by":"crossref","first-page":"3086","DOI":"10.1109\/TASE.2024.3389020","article-title":"ADP-based prescribed-time control for nonlinear time-varying delay systems with uncertain parameters","volume":"22","author":"Zhang","year":"2024","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"10.1016\/j.neunet.2026.109037_bib0062","series-title":"Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation (LREC-COLING 2024)","first-page":"16263","article-title":"ToolReRank: Adaptive and hierarchy-aware reranking for tool retrieval","author":"Zheng","year":"2024"},{"key":"10.1016\/j.neunet.2026.109037_bib0063","unstructured":"Zhong, Q., Li, H., Zhuang, L., Liu, J., & Du, B. (2024). Iterative data generation with large language models for aspect-based sentiment analysis. arXiv preprint arXiv: 2407.00341."},{"key":"10.1016\/j.neunet.2026.109037_bib0064","series-title":"Proceedings of the 37th international conference on neural information processing systems","first-page":"50117","article-title":"ToolQA: A dataset for LLM question answering with external tools","author":"Zhuang","year":"2023"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026004971?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026004971?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:17:35Z","timestamp":1784200655000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026004971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":64,"alternative-id":["S0893608026004971"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109037","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Tool retrieval bridge: Aligning vague instructions with retriever preferences via bridge model","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109037","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":"109037"}}