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Since INRs are lossy representations that introduce errors in data reconstruction, it is crucial to convey the uncertainty of the results to scientists to prevent them from being misled. However, current uncertainty quantification schemes generally face the challenge of compromising data reconstruction accuracy while maintaining the compactness of INRs. To address this issue, we formulate data reconstruction and uncertainty quantification as a multi\u2010task learning (MTL) problem and treat uncertainty quantification as an auxiliary task to the reconstruction task. Unlike conventional auxiliary tasks which aim to improve the performance of the primary task, our objective is to obtain highly credible uncertainty estimates while minimizing the negative impact on the primary reconstruction task. To this end, we address this MTL problem with a Multi\u2010gate Mixture\u2010of\u2010Experts (MMoE) architecture that partitions the network into multiple sub\u2010networks and employs distinct gates to weight their outputs for each task. Furthermore, we propose a virtual expert mechanism to mitigate the trade\u2010off between model flexibility and sub\u2010network performance. To ensure that both tasks are fully optimized, we propose a loss weighting method combining dynamic and static weights. The dynamic weights adapt based on task convergence, while the static weights scale the losses and balance their relative importance. We comprehensively evaluated existing uncertainty quantification methods and compared them with our method. The results indicate that our method achieves higher reconstruction accuracy and more reliable uncertainty than existing methods. Source code is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/LiuLan2001\/MTL-SV\">https:\/\/github.com\/LiuLan2001\/MTL\u2010SV<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1111\/cgf.70473","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T10:59:41Z","timestamp":1781175581000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Volume Data Reconstruction and Uncertainty Quantification in an Implicit Neural Representation via Multi\u2010Task Learning"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4814-0187","authenticated-orcid":false,"given":"Weiyang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology Northeast Normal University  China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8006-4845","authenticated-orcid":false,"given":"Huijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology Northeast Normal University  China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9961-7871","authenticated-orcid":false,"given":"Zhaohan","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology Northeast Normal University  China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4131-2147","authenticated-orcid":false,"given":"Yiming","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology Northeast Normal University  China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"e_1_2_9_2_2","doi-asserted-by":"crossref","unstructured":"CaruanaR.: Multitask learning: A knowledge-based source of inductive bias1. 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