{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:26:45Z","timestamp":1773804405197,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"33","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Multi-task test-time adaptation (MT-TTA) aims to adapt pre-trained models to dynamic environments during multi-task inference by leveraging unlabeled test data. This task is particularly challenging as different tasks respond divergently to distribution shifts, and mixed input streams containing both in-distribution (ID) and out-of-distribution (OOD) samples make the models after test-time adaptation prone to catastrophic forgetting of ID knowledge. Although the existing methods like M-TENT extend the classic test entropy minimization (TENT) by minimizing multi-task entropies and employing task-average gradient to adapt a model, it suffers from two key limitations: 1) the average gradient strategy proposed by M-TENT may exacerbate multi-task test-time optimization conflicts, harming individual tasks when gradients are directionally non-consensual; 2) aggressive updates on mixed ID\/OOD data cause severe forgetting of ID knowledge. In this paper, we theoretically establish a formal connection between multi-task loss differences and test-time performance under the first-order Taylor analysis, demonstrating that consensual multi-task entropy reductions are likely to increase the performance, while non-consensual ones might decrease the performance. To this end, we propose Consensus-driven Constrained Multi-Task Test-Time Adaptation (CoCo-MT-TTA), consisting of 1) multi-task gradient consensus adaptation, which aligns cross-task gradient directions to seek a consensus gradient; 2) multi-task plasticity-constraint adaptation, which constrains parameter updates using second-moment statistics to preserve ID knowledge. Extensive experiments on benchmark datasets, including CelebA and PlantData, demonstrate that our method achieves an absolute improvement of up to 16.02% in mean ID\/OOD F1-score (Mean I&amp;O) under domain shifts over non-adapted models, outperforming the recent baselines.<\/jats:p>","DOI":"10.1609\/aaai.v40i33.40006","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:21:20Z","timestamp":1773800480000},"page":"27836-27843","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Task Test-time Adaptation via Gradient Consensus and Plasticity Constraint"],"prefix":"10.1609","volume":"40","author":[{"given":"Zhong","family":"Ye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenguo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/40006\/43967","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/40006\/43967","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:21:20Z","timestamp":1773800480000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/40006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"33","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i33.40006","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}