Offline policy learning from logged data is a critical paradigm for enabling effective decision-making without costly online exploration. However, its application has been largely confined to single-objective problems, a stark contrast to real-world scenarios where decision-making inherently involves navigating multiple, often conflicting, objectives. This paper introduces a comprehensive framework for Offline Multi-Objective Bandits (OffMOB), providing a principled solution to the fundamental challenge of learning Pareto-optimal policies from a static dataset. Our core contribution is a novel algorithm that uniquely integrates the pessimism principle with multi-objective optimization to safely learn from off-policy data. Crucially, our approach transcends the primary limitation of scalarization techniques, which are restricted to finding a single policy for a pre-defined preference. Instead, OffMOB directly approximates the entire Pareto front, learning a single, flexible policy model capable of generating an optimal action for any desired trade-off. To rigorously evaluate performance, we introduce the Tchebycheff sub-optimality metric and establish the first finite-sample generalization bounds for this problem class, proving that our algorithm converges to the true Pareto front under practical data coverage assumptions. Extensive experiments on complex benchmarks demonstrate that OffMOB significantly outperforms existing methods, identifying the complete set of optimal trade-offs where naive extensions fail.

While LLMs show promise in automating educational content creation, their ability to generate questions that stimulate higher-order thinking remains understudied. This work evaluates six widely used LLMs through a Bloom's Taxonomy lens, focusing on their capacity to transcend rote memorization and achieve cognitive leaps. Using a hybrid human-AI evaluation protocol, we generate and analyze 20,700 questions across computer science, K-12 math, and social-science domains. Key contributions include: (1) a fine-grained prompting strategy that reduces question repetitiveness by 24.45% for Qwen2.5-7B-Instruct, and increases the proportion of higher-order cognitive-level outputs by 11.53% for InternLM3-8B-Instruct; (2) quantitative metrics for cognitive shift intensity (CogShift) and category drift, revealing InternLM3's superior performance in multi-level transitions; (3) an interpretability analysis revealing metric-level correlations that enhance the transparency of Chain-of-Thought prompting. Our findings highlight the importance of cognitive-aware prompt design and provide benchmarks for deploying LLMs in personalized learning systems.

Continual learning aims to learn multiple tasks sequentially. A key challenge in continual learning is balancing between two objectives: retaining knowledge from old tasks (stability) and adapting to new tasks (plasticity). Experience replay methods, which store and replay past data alongside new data, have become a widely adopted approach to mitigate catastrophic forgetting. However, these methods neglect the dynamic nature of the stability-plasticity trade-off and aim to find a fixed and unchanging balance, resulting in suboptimal adaptation during training and inference. In this paper, we propose Pareto Continual Learning (ParetoCL), a novel framework that reformulates the stability-plasticity trade-off in continual learning as a multi-objective optimization (MOO) problem. ParetoCL introduces a preference-conditioned model to efficiently learn a set of Pareto optimal solutions representing different trade-offs and enables dynamic adaptation during inference. From a generalization perspective, ParetoCL can be seen as an objective augmentation approach that learns from different objective combinations of stability and plasticity. Extensive experiments across multiple datasets and settings demonstrate that ParetoCL outperforms state-of-the-art methods and adapts to diverse continual learning scenarios.

Online Continual Learning (OCL) requires models to learn sequentially from data streams with limited memory. Rehearsal-based methods, particularly Experience Replay (ER), are commonly used in OCL scenarios. This paper revisits ER through the lens of $\epsilon$-constraint optimization, revealing that ER implicitly employs a soft constraint on past task performance, with its weighting parameter post-hoc defining a slack variable. While effective, ER's implicit and fixed slack strategy has limitations: it can inadvertently lead to updates that negatively impact generalization, and its fixed trade-off between plasticity and stability may not optimally balance current streaming with memory retention, potentially overfitting to the memory buffer. To address these shortcomings, we propose the \textbf{G}radient-Guided \textbf{E}psilon \textbf{C}onstraint (\textbf{GEC}) method for online continual learning. GEC explicitly formulates the OCL update as an $\epsilon$-constraint optimization problem, which minimize the loss on the current task data and transform the stability objective as constraints and propose a gradient-guided method to dynamically adjusts the update direction based on whether the performance on memory samples violates a predefined slack tolerance $\bar{\varepsilon}$: if forgetting exceeds this tolerance, GEC prioritizes constraint satisfaction; otherwise, it focuses on the current task while controlling the rate of increase in memory loss. Empirical evaluations on standard OCL benchmarks demonstrate GEC's ability to achieve a superior trade-off, leading to improved overall performance. Code is available at https://github.com/laisong-22004009/GEC_OCL.

Real-world data generally follows a long-tailed distribution, which makes traditional high-performance training strategies unable to show their usual effects. Various insights have been proposed to alleviate this challenging distribution. However, some observations indicate that models trained on long-tailed distributions always show a trade-off between the performance of head and tail classes. For a profound understanding of the trade-off, we first theoretically analyze the trade-off problem in long-tailed learning and creatively transform the trade-off problem in long-tailed learning into a multi-objective optimization (MOO) problem. Motivated by these analyses, we propose the idea of strategy fusion for MOO long-tailed learning and point out the potential conflict problem. We further design a Multi-Objective Optimization based Strategy Fusion (MOOSF), which effectively resolves conflicts, and achieves an efficient fusion of heterogeneous strategies. Comprehensive experiments on mainstream datasets show that even the simplest strategy fusion can outperform complex long-tailed strategies. More importantly, it provides a new perspective for generalized long-tailed learning. The code is available in the accompanying supplementary materials.

Recently, automatic multi-domain fake news detection has attracted widespread attention. Many methods achieve domain adaptation by modeling domain category gate networks and domain-invariant features. However, existing multi-domain fake news detection faces three main challenges: (1) Inter-domain modal semantic deviation, where similar texts and images carry different meanings across various domains. (2) Inter-domain modal dependency deviation, where the dependence on different modalities varies across domains. (3) Inter-domain knowledge dependency deviation, where the reliance on cross-domain knowledge and domain-specific knowledge differs across domains. To address these issues, we propose a Multi-modal Multi-Domain Fake News Detection Model (MMDFND). MMDFND incorporates domain embeddings and attention mechanisms into a progressive hierarchical extraction network to achieve domain-adaptive domain-related knowledge extraction. Furthermore, MMDFND utilizes Stepwise Pivot Transformer networks and adaptive instance normalization to effectively utilize information from different modalities and domains. We validate the effectiveness of MMDFND through comprehensive comparative experiments on two real-world datasets and conduct ablation experiments to verify the effectiveness of each module, achieving state-of-the-art results on both datasets. The source code is available at https://github.com/yutchina/MMDFND.

Traditional long-tailed learning methods often perform poorly when dealing with inconsistencies between training and test data distributions, and they cannot flexibly adapt to different user preferences for trade-offs between head and tail classes. To address this issue, we propose a novel long-tailed learning paradigm that aims to tackle distribution shift in real-world scenarios and accommodate different user preferences for the trade-off between head and tail classes. We generate a set of diverse expert models via hypernetworks to cover all possible distribution scenarios, and optimize the model ensemble to adapt to any test distribution. Crucially, in any distribution scenario, we can flexibly output a dedicated model solution that matches the user's preference. Extensive experiments demonstrate that our method not only achieves higher performance ceilings but also effectively overcomes distribution shift while allowing controllable adjustments according to user preferences. We provide new insights and a paradigm for the long-tailed learning problem, greatly expanding its applicability in practical scenarios. The code can be found here: https://github.com/DataLab-atom/PRL.