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Syst."],"published-print":{"date-parts":[[2026,9,30]]},"abstract":"<jats:p>\n                    The rapid advancement of the automotive industry toward automated and semi-automated vehicles has rendered traditional methods of vehicle interaction, such as touch-based and voice command systems, inadequate for a widening range of non-driving related tasks, such as referencing objects outside of the vehicle. Consequently, research has shifted toward gestural input (e.g., hand, gaze, and head pose gestures) as a more suitable mode of interaction during driving. However, due to the dynamic nature of driving and individual variation, there are significant differences in drivers\u2019 gestural input performance. While, in theory, this inherent variability could be moderated by substantial data-driven machine learning models, prevalent methodologies lean toward constrained, single-instance trained models for object referencing. These models show a limited capacity to continuously adapt to the divergent behaviors of individual drivers and the variety of driving scenarios. To address this, we proposed, in our previous work, an incremental learning approach, adapting to changing user behavior. Although this method proved superior over state-of-the-art approaches, it still lacked in performance and had some computational resource limitations, mainly the small-sized memory needed to store a subset of old data for incremental learning. Therefore, in this work, we propose an alternative solution using reinforcement learning that can be used to overcome memory storage limitations, achieving a\n                    <jats:italic toggle=\"yes\">zero data storage<\/jats:italic>\n                    approach for incremental learning without performance loss. Our newly enhanced solution has been added to our previous framework at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/amrgomaaelhady\/IcRegress\">https:\/\/github.com\/amrgomaaelhady\/IcRegress<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3820490","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T13:45:45Z","timestamp":1781099145000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Looking for an Even Better Fit? A Personalized Incremental\u00a0Reinforcement Learning Multimodal Object Referencing\u00a0Framework"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0955-3181","authenticated-orcid":false,"given":"Amr","family":"Gomaa","sequence":"first","affiliation":[{"name":"Cognitive Assistants, German Research Centre for Artificial Intelligence, Saarbrucken, Germany and Computer Science, Saarland University, Saarbrucken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5303-1949","authenticated-orcid":false,"given":"Kiran","family":"Gani","sequence":"additional","affiliation":[{"name":"Cognitive Assistants, German Research Centre for Artificial Intelligence, Saarbrucken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6755-5287","authenticated-orcid":false,"given":"Michael","family":"Feld","sequence":"additional","affiliation":[{"name":"Cognitive Assistants, German Research Centre for Artificial Intelligence, Saarbrucken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8055-8367","authenticated-orcid":false,"given":"Antonio","family":"Krueger","sequence":"additional","affiliation":[{"name":"Cognitive Assistants, German Research Centre for Artificial Intelligence, Saarbrucken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2019. 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