{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:27:44Z","timestamp":1781018864432,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"PRIMUS\/24\/SCI\/008","award":["24\/SCI\/008"],"award-info":[{"award-number":["24\/SCI\/008"]}]},{"name":"MUR (Italian ministry of research)","award":["ECS00000043"],"award-info":[{"award-number":["ECS00000043"]}]},{"name":"MUR","award":["PE0000013"],"award-info":[{"award-number":["PE0000013"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,3,23]]},"DOI":"10.1145\/3748522.3779772","type":"proceedings-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:17:49Z","timestamp":1781014669000},"page":"908-916","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Iterative In-Context Learning to Enhance LLMs Abstract Reasoning: The Case-Study of Algebraic Tasks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6918-1805","authenticated-orcid":false,"given":"Stefano","family":"Fioravanti","sequence":"first","affiliation":[{"name":"University of Padova, Padua, Italy"},{"name":"Department of Algebra, Charles University Prague, Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6696-2972","authenticated-orcid":false,"given":"Matteo","family":"Zavatteri","sequence":"additional","affiliation":[{"name":"University of Padova, Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0936-2123","authenticated-orcid":false,"given":"Roberto","family":"Confalonieri","sequence":"additional","affiliation":[{"name":"University of Padua, Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3014-2511","authenticated-orcid":false,"given":"Kamyar","family":"Zeinalipour","sequence":"additional","affiliation":[{"name":"University of Siena, Padua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3227-0019","authenticated-orcid":false,"given":"Paolo","family":"Frazzetto","sequence":"additional","affiliation":[{"name":"University of Padua, Padua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8686-850X","authenticated-orcid":false,"given":"Alessandro","family":"Sperduti","sequence":"additional","affiliation":[{"name":"University of Padua, Padua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4108-1754","authenticated-orcid":false,"given":"Nicol\u00f2","family":"Navarin","sequence":"additional","affiliation":[{"name":"University of Padova, Padua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"MATH-AI Workshop. International Conference on Learning Representations. (May","author":"Agarwal Vishesh","year":"2021","unstructured":"Vishesh Agarwal, Somak Aditya, and Navin Goyal. 2021. Analyzing the nuances of transformers' polynomial simplification abilities. In MATH-AI Workshop. International Conference on Learning Representations. (May 2021)."},{"key":"e_1_3_2_1_2_1","unstructured":"Cem Anil et al. 2022. Exploring length generalization in large language models. In NeurIPS 2022."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_1_4_1","unstructured":"Xiao Bi et al. 2024. Deepseek llm: scaling open-source language models with longtermism. arXiv:2401.02954."},{"key":"e_1_3_2_1_5_1","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown Tom B","year":"2020","unstructured":"Tom B Brown et al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877\u20131901.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bica.2018.07.004"},{"key":"e_1_3_2_1_7_1","first-page":"6278","article-title":"Learning to perform local rewriting for combinatorial optimization","volume":"2019","author":"Chen Xinyun","year":"2019","unstructured":"Xinyun Chen and Yuandong Tian. 2019. Learning to perform local rewriting for combinatorial optimization. In NeurIPS 2019, 6278\u20136289.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_8_1","unstructured":"Ethan Chern Haoyang Zou Xuefeng Li Jiewen Hu Kehua Feng Junlong Li and Pengfei Liu. 2023. Generative ai for math: abel. https:\/\/github.com\/GAIR-NLP\/abel. (2023)."},{"key":"e_1_3_2_1_9_1","unstructured":"Aakanksha Chowdhery et al. 2022. Palm: scaling language modeling with pathways. arXiv:2204.02311."},{"key":"e_1_3_2_1_10_1","volume-title":"ICLR","author":"Csord\u00e1s R\u00f3bert","year":"2022","unstructured":"R\u00f3bert Csord\u00e1s, Kazuki Irie, and J\u00fcrgen Schmidhuber. 2022. The neural data router: adaptive control flow in transformers improves systematic generalization. In ICLR 2022."},{"key":"e_1_3_2_1_11_1","unstructured":"DeepMind. 2023. Gemini: google's large multimodal language model. https:\/\/deepmind.google\/technologies\/gemini\/. Accessed: 2024-04-28. (2023)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.73"},{"key":"e_1_3_2_1_13_1","volume-title":"Lucie Charlotte Magister, Pietro Li\u00f3, and Pietro Barbiero.","author":"Giannini Francesco","year":"2023","unstructured":"Francesco Giannini, Stefano Fioravanti, Oguzhan Keskin, Alisia Maria Lupidi, Lucie Charlotte Magister, Pietro Li\u00f3, and Pietro Barbiero. 2023. Interpretable graph networks formulate universal algebra conjectures. In Advances in Neural Information Processing Systems. Vol. 36."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.11674"},{"key":"e_1_3_2_1_15_1","first-page":"156","article-title":"Bridging equational properties and patterns on graphs: an ai-based approach","volume":"221","author":"Keskin Oguzhan","year":"2023","unstructured":"Oguzhan Keskin, Alisia Lupidi, Francesco Giannini, Stefano Fioravanti, Lucie Charlotte Magister, Pietro Barbiero, and Pietro Li\u00f2. 2023. Bridging equational properties and patterns on graphs: an ai-based approach. In Proceedings of Machine Learning Research. Vol. 221, 156\u2013168.","journal-title":"Proceedings of Machine Learning Research."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5220\/0002319704520458"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Brenden M Lake and Marco Baroni. 2023. Human-like systematic generalization through a meta-learning neural network. Nature 623 7985 115\u2013121.","DOI":"10.1038\/s41586-023-06668-3"},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Learning Representation.","author":"Lample Guillaume","year":"2020","unstructured":"Guillaume Lample and Fran\u00e7ois Charton. 2020. Deep learning for symbolic mathematics. International Conference on Learning Representation."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Guillaume Lample Timothee Lacroix Marie-Anne Lachaux Aurelien Rodriguez Amaury Hayat Thibaut Lavril Gabriel Ebner and Xavier Martinet. 2022. Hypertree proof search for neural theorem proving. Advances in neural information processing systems 35 26337\u201326349.","DOI":"10.52202\/068431-1910"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS '22)","author":"Aitor","unstructured":"Aitor Lewkowycz et al. 2022. Solving quantitative reasoning problems with language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS '22) Article 278. Curran Associates Inc., 15 pages. isbn: 9781713871088."},{"key":"e_1_3_2_1_21_1","volume-title":"Memorize or generalize? searching for a compositional RNN in a haystack. CoRR, abs\/1802.06467. arXiv","author":"Liska Adam","year":"1802","unstructured":"Adam Liska, Germ\u00e1n Kruszewski, and Marco Baroni. 2018. Memorize or generalize? searching for a compositional RNN in a haystack. CoRR, abs\/1802.06467. arXiv: 1802.06467."},{"key":"e_1_3_2_1_22_1","unstructured":"Haipeng Luo et al. 2023. Wizardmath: empowering mathematical reasoning for large language models via reinforced evol-instruct. arXiv:2308.09583."},{"key":"e_1_3_2_1_23_1","volume-title":"Is a modular architecture enough? In NeurIPS","author":"Mittal Sarthak","year":"2022","unstructured":"Sarthak Mittal, Yoshua Bengio, and Guillaume Lajoie. 2022. Is a modular architecture enough? In NeurIPS 2022."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/N18-4013"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2022.ACL-LONG.251"},{"key":"e_1_3_2_1_26_1","unstructured":"OpenAI. 2023. Gpt-4 technical report. (2023). arXiv: 2303.08774 [cs.CL]."},{"key":"e_1_3_2_1_27_1","unstructured":"Flavio Petruzzellis. 2024. Flavio2018\/itersolv-public: first release. (2024). https:\/\/zenodo.org\/account\/settings\/github\/repository\/flavio2018\/itersolv-public."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.3233\/FAIA240854"},{"key":"e_1_3_2_1_29_1","unstructured":"Stanislas Polu and Ilya Sutskever. 2021. Generative language modeling for automated theorem proving. arXiv preprint arXiv:2009.03393."},{"key":"e_1_3_2_1_30_1","volume-title":"Findings of the Association for Computational Linguistics: EMNLP","author":"Ofir","year":"2023","unstructured":"Ofir Press et al. 2023. Measuring and narrowing the compositionality gap in language models. In Findings of the Association for Computational Linguistics: EMNLP 2023. Houda Bouamor, Juan Pino, and Kalika Bali, (Eds.) Association for Computational Linguistics, 5687\u20135711."},{"key":"e_1_3_2_1_31_1","volume-title":"7th International Conference on Learning Representations (ICLR).","author":"Saxton David","year":"2019","unstructured":"David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. 2019. Analysing mathematical reasoning abilities of neural models. In 7th International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_32_1","unstructured":"Parshin Shojaee Iman Mirzadeh Keivan Alizadeh Maxwell Horton Samy Bengio and Mehrdad Farajtabar. 2025. The illusion of thinking: understanding the strengths and limitations of reasoning models via the lens of problem complexity. (2025). https:\/\/arxiv.org\/abs\/2506.06941 arXiv: 2506.06941 [cs.AI]."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Alberto Testolin. 2024. Can neural networks do arithmetic? a survey on the elementary numerical skills of state-of-the-art deep learning models. Applied Sciences.","DOI":"10.3390\/app14020744"},{"key":"e_1_3_2_1_34_1","volume-title":"The Eleventh International Conference on Learning Representations, ICLR","author":"Xuezhi","year":"2023","unstructured":"Xuezhi Wang et al. 2023. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, ICLR 2023."},{"key":"e_1_3_2_1_35_1","volume-title":"ICLR","author":"Wang Xuezhi","year":"2023","unstructured":"Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. Self-consistency improves chain of thought reasoning in language models. In ICLR 2023. https:\/\/arxiv.org\/abs\/2203.11171."},{"key":"e_1_3_2_1_36_1","volume-title":"Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2022. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022."},{"key":"e_1_3_2_1_37_1","unstructured":"Huajian Xin Daya Guo Zhihong Shao Zhizhou Ren Qihao Zhu Bo Liu Chong Ruan Wenda Li and Xiaodan Liang. 2024. Deepseek-prover: advancing theorem proving in llms through large-scale synthetic data. arXiv:2405.14333."},{"key":"e_1_3_2_1_38_1","unstructured":"An Yang et al. 2024. Qwen2. 5-math technical report: toward mathematical expert model via self-improvement. arXiv:2409.12122."},{"key":"e_1_3_2_1_39_1","unstructured":"Zhen Yang et al. 2024. Mathglm-vision: solving mathematical problems with multi-modal large language model. arXiv:2409.13729."},{"key":"e_1_3_2_1_40_1","unstructured":"Longhui Yu et al. 2023. Metamath: bootstrap your own mathematical questions for large language models. arXiv:2309.12284."},{"key":"e_1_3_2_1_41_1","unstructured":"Liang Zeng et al. 2024. Skywork-math: data scaling laws for mathematical reasoning in large language models-the story goes on. arXiv:2407.08348."},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4571\u20134581","author":"Xin Wayne","unstructured":"Wayne Xin Zhao et al. 2022. Jiuzhang: a chinese pre-trained language model for mathematical problem understanding. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4571\u20134581."}],"event":{"name":"SAC '26: 41st ACM\/SIGAPP Symposium on Applied Computing","location":"Grand Hotel Palace Thessaloniki Greece","acronym":"SAC '26","sponsor":["SIGAPP ACM Special Interest Group on Applied Computing"]},"container-title":["Proceedings of the 41st ACM\/SIGAPP Symposium on Applied Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3748522.3779772","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:37:59Z","timestamp":1781015879000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3748522.3779772"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,23]]},"references-count":42,"alternative-id":["10.1145\/3748522.3779772","10.1145\/3748522"],"URL":"https:\/\/doi.org\/10.1145\/3748522.3779772","relation":{},"subject":[],"published":{"date-parts":[[2026,3,23]]},"assertion":[{"value":"2026-06-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}