Deconstructing Multi-Task Active Learning: The Paradox of Gradient Conflict and Orthogonal Decomposition

Existing multi-task active learning (MTL-AL) methods predominantly rely on the linear scalarization of heterogeneous objectives, such as uncertainty, diversity, and gradient conflict.We argue that this entangled formulation is fundamentally limited, as linear combination fails to capture Pareto-optimal trade-offs under conflicting objectives. To address this limitation, we propose a paradigm shift from combination to deconstruction and introduce a principled framework that rethinks MTL-AL along three orthogonal dimensions. First, we identify the Conflict Paradox: contrary to conventional wisdom, samples with high gradient conflict are not noise to be avoided but carry maximal information about unresolved inter-task trade-offs. We therefore decouple sample selection, which embraces conflict, from optimization, which resolves it via gradient surgery. Second, we introduce Orthogonal Decomposition, separating the acquisition objective into two distinct axes: Informativeness and Exploration. This two-stage process, consisting of subspace projection followed by manifold coverage, prevents ``diversity dilution'' by excluding uninformative outliers before enforcing diversity. Third, we propose Cross-Task Mutual Information (CTMI), a distribution-free proxy for inter-task dependency derived from a variational lower bound under the Maximum Entropy principle. Extensive experiments on COCO, Cityscapes, and NYUv2 — including comparisons against recent multi-task active learning baselines — demonstrate consistent improvements over state-of-the-art MTL-AL methods, highlighting that structural rethinking of acquisition yields larger gains than incremental heuristic design. Our code is publicly available at: \href{https://anonymous.4open.science/r/multitaskactivelearning-1545}{\texttt{https://anonymous/CTMI}}