Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundation models primarily focus on forecasting tasks and often overlook classification-specific challenges, such as modeling interpretable shapelets that capture class-discriminative temporal features. To bridge this gap, we propose UniShape, a unified shape-aware foundation model designed for time series classification. UniShape incorporates a shape-aware adapter that adaptively aggregates multiscale discriminative subsequences (shapes) into class tokens, effectively selecting the most relevant subsequence scales to enhance model interpretability. Meanwhile, a prototype-based pretraining module is introduced to jointly learn instance- and shape-level representations, enabling the capture of transferable shape patterns. Pre-trained on a large-scale multi-domain time series dataset comprising 1.89 million samples, UniShape exhibits superior generalization across diverse target domains. Experiments on 128 UCR datasets and 30 additional time series datasets demonstrate that UniShape achieves state-of-the-art classification performance, with interpretability and ablation analyses further validating its effectiveness.

Sequential Recommendation predicts the next item based on users' past behaviors, but sparse interaction data makes user preferences hard to learn. Recently, contrastive learning has shown promise in this area. It augments data to form positive pairs and maximizing their similarity, allowing the model to learn more generalizable user interests. However, they mainly adopt uniform augmentation and alignment to all sequences, ignoring the challenges arising from their distinct interest structure, namely semantic discrepancy and semantic bias. In this paper, we first study the impact of augmentation on sequence's semantic through Interest Entropy, which measures the diversity and density of interest distribution. Our finding shows only a small fraction of sequences are stable under perturbation. These sequences mainly exhibit low or high entropy, reflecting focused or casual interests. This limits the effectiveness of contrastive learning, which relies on semantically consistent positive pairs. Furthermore, with spectral analysis, we show that positive alignment may cause low-entropy sequences to overlook niche interests, while high-entropy sequences may amplify interest-irrelevant signals, which we term semantic bias. Finally, based on Interest Entropy, we propose IERec, a simple yet effective mutual retrieval augmented contrastive learning method that mitigates the above issues in a unified manner. For each anchor sequence (those with low or high entropy), we retrieve semantically similar sequences with complementary entropy, and concatenate them to form a positive view. Sequences that are easily affected, mainly those with medium entropy, are excluded from augmentation. This approach can avoid harmful semantic discrepancy of positive pairs and reduce the effect of the semantic bias, leading to improved performance. Moreover, using interest entropy to guide contrastive learning can further improve existing CL-based SR methods.

Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in quick, heuristic decisions, System 2 relies on logical reasoning for more accurate judgments and reduced biases. Foundational Large Language Models (LLMs) excel at fast decision-making but lack the depth for complex reasoning, as they have not yet fully embraced the step-by-step analysis characteristic of true System 2 thinking. Recently, reasoning LLMs like OpenAI's o1/o3 and DeepSeek's R1 have demonstrated expert-level performance in fields such as mathematics and coding, closely mimicking the deliberate reasoning of System 2 and showcasing human-like cognitive abilities. This survey begins with a brief overview of the progress in foundational LLMs and the early development of System 2 technologies, exploring how their combination has paved the way for reasoning LLMs. Next, we discuss how to construct reasoning LLMs, trace the evolution of various reasoning models, and examine the core methods that enable advanced reasoning behind them. Additionally, we provide an overview of reasoning benchmarks, offering an in-depth comparison of the performance of representative reasoning LLMs. Finally, we explore promising directions for advancing reasoning LLMs and maintain a real-time GitHub Repository to track the latest developments. We hope this survey will serve as a valuable resource to inspire innovation and drive progress in this rapidly evolving field.

Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously adapt in dynamic environments. While large language models (LLMs) have demonstrated impressive capabilities in natural language processing, existing LLM agents are typically designed for static systems and lack the ability to adapt over time in response to new challenges. This survey is the first to systematically summarize the potential techniques for incorporating lifelong learning into LLM-based agents. We categorize the core components of these agents into three modules: the perception module for multimodal input integration, the memory module for storing and retrieving evolving knowledge, and the action module for grounded interactions with the dynamic environment. We highlight how these pillars collectively enable continuous adaptation, mitigate catastrophic forgetting, and improve long-term performance. This survey provides a roadmap for researchers and practitioners working to develop lifelong learning capabilities in LLM agents, offering insights into emerging trends, evaluation metrics, and application scenarios.

This paper investigates a dynamic heterogeneous mobile edge computing network (HMECN), where mobile devices (MDs) could offload their full tasks to a small base station (SBS) directly or the macro base station (MBS) in direct or relay mode. As age of information (AoI) is a comprehensive and accurate metric to capture the freshness of computation results, we formulate a long-term weighted sum AoI (LWSA) minimization problem in the HMECN by jointly optimizing the offloading decisions of MDs as well as the bandwidth and computation resource allocation of all base stations, subject to energy, delay and peak AoI constraints. To address the formulated non-convex mixed integer nonlinear programming problem, we decompose it into the offloading decision optimization (ODO) top-problem and the resource allocation optimization (RAO) sub-problem. Based on the decomposition, we propose a federated learning (FL)-assisted hybrid DRL and convex approach that is comprised of a safe multi-agent DRL algorithm, convex optimization and FL. The ODO top-problem is solved by the safe multi-agent DRL algorithm, which strictly ensures that the actions of each agent do not exceed its energy constraint and then paves the way for using convex optimization to solve the RAO sub-problem. FL is used to alleviate the training instability problem aggravated by multi agent settings via breaking the limitation of partial knowledge for each individual agent. Simulation results demonstrate the superiority of the proposed approach in terms of the LWSA, convergence, scalability and robustness in dynamic environments.

Developing reliable defenses against patch attacks on object detectors has attracted increasing interest. However, we identify that existing defense evaluations lack a unified and comprehensive framework, resulting in inconsistent and incomplete assessments of current methods. To address this issue, we revisit 11 representative defenses and present the first patch defense benchmark, involving 2 attack goals, 13 patch attacks, 11 object detectors, and 4 diverse metrics. This leads to the large-scale adversarial patch dataset with 94 types of patches and 94,000 images. Our comprehensive analyses reveal new insights: (1) The difficulty in defending against naturalistic patches lies in the data distribution, rather than the commonly believed high frequencies. Our new dataset with diverse patch distributions can be used to improve existing defenses by 15.09% [email protected]. (2) The average precision of the attacked object, rather than the commonly pursued patch detection accuracy, shows high consistency with defense performance. (3) Adaptive attacks can substantially bypass existing defenses, and defenses with complex/stochastic models or universal patch properties are relatively robust. We hope that our analyses will serve as guidance on properly evaluating patch attacks/defenses and advancing their design. Code and dataset are available at https://github.com/Gandolfczjh/APDE, where we will keep integrating new attacks/defenses.

Despite recent progress in Retrieval-Augmented Generation (RAG) achieved by large language models (LLMs), retrievers often recall uncorrelated documents, regarded as "noise" during subsequent text generation. To address this, some methods train LLMs to distinguish between relevant and irrelevant documents using labeled data, enabling them to select the most likely relevant ones as context. However, they remain sensitive to noise, as LLMs can easily make mistakes when the selected document is noisy. Some approaches increase the number of referenced documents and train LLMs to perform stepwise reasoning when presented with multiple documents. Unfortunately, these methods rely on extensive and diverse annotations to ensure generalization, which is both challenging and costly. In this paper, we propose **Backtracking Correction** to address these limitations. Specifically, we reformulate stepwise RAG into a multi-step decision-making process. Starting from the final step, we optimize the model through error sampling and self-correction, and then backtrack to the previous state iteratively. In this way, the model's learning scheme follows an easy-to-hard progression: as the target state moves forward, the context space decreases while the decision space increases. Experimental results demonstrate that **Backtracking Correction** enhances LLMs' ability to make complex multi-step assessments, improving the robustness of RAG in dealing with noisy documents.

Recent advancements in large language models (LLMs) reveal a perplexing phenomenon in continual learning: despite extensive training, models experience significant performance declines, raising questions about task alignment and underlying knowledge retention. This study first explores the concept of "spurious forgetting", proposing that such performance drops often reflect a decline in task alignment rather than true knowledge loss. Through controlled experiments with a synthesized dataset, we investigate the dynamics of model performance during the initial training phases of new tasks, discovering that early optimization steps can disrupt previously established task alignments. Our theoretical analysis connects these shifts to orthogonal updates in model weights, providing a robust framework for understanding this behavior. Ultimately, we introduce a Freezing strategy that fix the bottom layers of the model, leading to substantial improvements in four continual learning scenarios. Our findings underscore the critical distinction between task alignment and knowledge retention, paving the way for more effective strategies in continual learning.

Irregular multivariate time series (IMTS) are characterized by irregular time intervals within variables and unaligned observations across variables, posing challenges in learning temporal and variable dependencies. Many existing IMTS models either require padded samples to learn separately from temporal and variable dimensions, or represent original samples via bipartite graphs or sets. However, the former approaches often need to handle extra padding values affecting efficiency and disrupting original sampling patterns, while the latter ones have limitations in capturing dependencies among unaligned observations. To represent and learn both dependencies from original observations in a unified form, we propose HyperIMTS, a Hypergraph neural network for Irregular Multivariate Time Series forecasting. Observed values are converted as nodes in the hypergraph, interconnected by temporal and variable hyperedges to enable message passing among all observations. Through irregularity-aware message passing, HyperIMTS captures variable dependencies in a time-adaptive way to achieve accurate forecasting. Experiments demonstrate HyperIMTS’s competitive performance among state-of-the-art models in IMTS forecasting with low computational cost. Our code is available at https://github.com/qianlima-lab/PyOmniTS.

Incremental Learning (IL) has been a longstanding problem in both vision and Natural Language Processing (NLP) communities.In recent years, as Pre-trained Language Models (PLMs) have achieved remarkable progress in various NLP downstream tasks, utilizing PLMs as backbones has become a common practice in recent research of IL in NLP.Most assume that catastrophic forgetting is the biggest obstacle to achieving superior IL performance and propose various techniques to overcome this issue.However, we find that this assumption is problematic.Specifically, we revisit more than 20 methods on four classification tasks (Text Classification, Intent Classification, Relation Extraction, and Named Entity Recognition) under the two most popular IL settings (Class-Incremental and Task-Incremental) and reveal that most of them severely underestimate the inherent anti-forgetting ability of PLMs.Based on the observation, we propose a frustratingly easy method called SEQ* for IL with PLMs.The results show that SEQ* has competitive or superior performance compared with state-ofthe-art (SOTA) IL methods yet requires considerably less trainable parameters and training time.These findings urge us to revisit the IL with PLMs and encourage future studies to have a fundamental understanding of the catastrophic forgetting in PLMs.The data, code and scripts are publicly available 1 .

Hierarchical Text Classification (HTC) is a challenging task which aims to extract the labels in a tree structure corresponding to a given text. Discriminative methods usually incorporate the hierarchical structure information into the encoding process, while generative methods decode the features according to it. However, the data distribution varies widely among different categories of samples, but current methods ignore the data imbalance, making the predictions biased and susceptible to error propagation. In this paper, we propose an IMplicitly Augmented Generativ E framework with distribution modification for hierarchical text classification (IMAGE). Specifically, we translate the distributions of original samples along various directions through implicit augmentation to get more diverse data. Furthermore, given the scarcity of the samples of tail classes, we adjust their distributions by transferring knowledge from other classes in label space. In this way, the generative framework learns a better beginning of the feature sequence without a prediction bias and avoids being misled by its wrong predictions for head classes. Experimental results show that IMAGE obtains competitive results compared with state-of-the-art methods and prove its superiority on unbalanced data.

Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks. Previous physical attacks against MDE models rely on 2D adversarial patches, so they only affect a small, localized region in the MDE map but fail under various viewpoints. To address these limitations, we propose 3D Depth Fool (3D2Fool), the first 3D texture-based adversarial attack against MDE models. 3D2Fool is specifically optimized to generate 3D adversarial textures agnostic to model types of vehicles and to have improved robustness in bad weather conditions, such as rain and fog. Experimental results validate the superior performance of our 3D2Fool across various scenarios, including vehicles, MDE models, weather conditions, and viewpoints. Real-world experiments with printed 3D textures on physical vehicle models further demonstrate that our 3D2Fool can cause an MDE error of over 10 meters. The code is available at https://github.com/GandolfczjhI3D2Fool.

Complex Query Answering (CQA) over Knowledge Graphs (KGs) is a challenging task. Given that KGs are usually incomplete, neural models are proposed to solve CQA by performing multi-hop logical reasoning. However, most of them cannot perform well on both one-hop and multi-hop queries simultaneously. Recent work proposes a logical message passing mechanism based on the pre-trained neural link predictors. While effective on both one-hop and multi-hop queries, it ignores the difference between the constant and variable nodes in a query graph. In addition, during the node embedding update stage, this mechanism cannot dynamically measure the importance of different messages, and whether it can capture the implicit logical dependencies related to a node and received messages remains unclear. In this paper, we propose Conditional Logical Message Passing Transformer (CLMPT), which considers the difference between constants and variables in the case of using pre-trained neural link predictors and performs message passing conditionally on the node type. We empirically verified that this approach can reduce computational costs without affecting performance. Furthermore, CLMPT uses the transformer to aggregate received messages and update the corresponding node embedding. Through the self-attention mechanism, CLMPT can assign adaptive weights to elements in an input set consisting of received messages and the corresponding node and explicitly model logical dependencies between various elements. Experimental results show that CLMPT is a new state-of-the-art neural CQA model. https://github.com/qianlima-lab/CLMPT.

Irregularly Sampled Medical Time Series (ISMTS) are commonly found in the healthcare domain, where different variables exhibit unique temporal patterns while interrelated. However, many existing methods fail to efficiently consider the differences and correlations among medical variables together, leading to inadequate capture of fine-grained features at the variable level in ISMTS. We propose Knowledge-Empowered Dynamic Graph Network (KEDGN), a graph neural network empowered by variables' textual medical knowledge, aiming to model variable-specific temporal dependencies and inter-variable dependencies in ISMTS. Specifically, we leverage a pre-trained language model to extract semantic representations for each variable from their textual descriptions of medical properties, forming an overall semantic view among variables from a medical perspective. Based on this, we allocate variable-specific parameter spaces to capture variable-specific temporal patterns and generate a complete variable graph to measure medical correlations among variables. Additionally, we employ a density-aware mechanism to dynamically adjust the variable graph at different timestamps, adapting to the time-varying correlations among variables in ISMTS. The variable-specific parameter spaces and dynamic graphs are injected into the graph convolutional recurrent network to capture intra-variable and inter-variable dependencies in ISMTS together. Experiment results on four healthcare datasets demonstrate that KEDGN significantly outperforms existing methods.

Hierarchical Text Classification (HTC) is an essential and challenging task due to the difficulty of modeling label hierarchy. Recent generative methods have achieved state-of-the-art performance by flattening thelocal label hierarchyinto a label sequence with a specific order. However, the order between labels does not naturally exist and the generation of the current label should incorporate the information in all other target labels. Moreover, the generative methods usually suffer from the error accumulation problem. To this end, we propose a new framework named sequence-to-label (Seq2Label) with a random generative way to learn label hierarchy for hierarchical text classification. Instead of using only one specific order, we shuffle the label sequence by a Label Sequence Random Shuffling (LSRS) mechanism so that a text will be mapped to several different order label sequences during the training phase. To alleviate the error accumulation problem, we further propose a Hierarchy-aware Negative Sampling (HNS) strategy with a negative label-aware loss to better distinguish target labels and negative labels. In this way, our model can capture the hierarchical and co-occurrence information of the target labels of each text. The experimental results on three benchmark datasets show that Seq2Label achieves state-of-the-art results.

Emotion-Cause Pair Extraction (ECPE) aims to identify the document's emotion clauses and corresponding cause clauses.Like other relation extraction tasks, ECPE is closely associated with the relationship between sentences.Recent methods based on Graph Convolutional Networks focus on how to model the multiplex relations between clauses by constructing different edges.However, the data of emotions, causes, and pairs are extremely unbalanced, but current methods get their representation using the same graph structure.In this paper, we propose a Joint Constrained Learning framework with Boundary-adjusting for Emotion-Cause Pair Extraction (JCB).Specifically, through constrained learning, we summarize the prior rules existing in the data and force the model to take them into consideration in optimization, which helps the model learn a better representation from unbalanced data.Furthermore, we adjust the decision boundary of classifiers according to the relations between subtasks, which have always been ignored.No longer working independently as in the previous framework, the classifiers corresponding to three subtasks cooperate under the relation constraints.Experimental results show that JCB obtains competitive results compared with state-of-theart methods and prove its robustness on unbalanced data.

Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Noisy labels seriously degrade the generalization ability of Deep Neural Networks (DNNs) in various classification tasks. Existing studies on label-noise learning mainly focus on computer vision, while time series also suffer from the same issue. Directly applying the methods from computer vision to time series may reduce the temporal dependency due to different data characteristics. How to make use of the properties of time series to enable DNNs to learn robust representations in the presence of noisy labels has not been fully explored. To this end, this paper proposes a method that expands the distribution of Confident instances by Time-Warping (CTW) to learn robust representations of time series. Specifically, since applying the augmentation method to all data may introduce extra mislabeled data, we select confident instances to implement Time-Warping. In addition, we normalize the distribution of the training loss of each class to eliminate the model's selection preference for instances of different classes, alleviating the class imbalance caused by sample selection. Extensive experimental results show that CTW achieves state-of-the-art performance on the UCR datasets when dealing with different types of noise. Besides, the t-SNE visualization of our method verifies that augmenting confident data improves the generalization ability. Our code is available at https://github.com/qianlima-lab/CTW.

Continual Learning for Named Entity Recognition (CL-NER) aims to learn a growing number of entity types over time from a stream of data.However, simply learning Other-Class in the same way as new entity types amplifies the catastrophic forgetting and leads to a substantial performance drop.The main cause behind this is that Other-Class samples usually contain old entity types, and the old knowledge in these Other-Class samples is not preserved properly.Thanks to the causal inference, we identify that the forgetting is caused by the missing causal effect from the old data.To this end, we propose a unified causal framework to retrieve the causality from both new entity types and Other-Class.Furthermore, we apply curriculum learning to mitigate the impact of label noise and introduce a self-adaptive weight for balancing the causal effects between new entity types and Other-Class.Experimental results on three benchmark datasets show that our method outperforms the state-of-theart method by a large margin.Moreover, our method can be combined with the existing stateof-the-art methods to improve the performance in CL-NER. 1

This paper proposes an improved weighted optimization-based framework (iWOF) for solving large-scale multiobjective optimization problems (LSMOPs). Compared to the original framework, there are two main contributions in our work. Firstly, a novel evolutionary search strategy involving two different search operators with different search characteristics, i.e., a particle swarm optimization (PSO) operator and differential evolution (DE) operator, is designed in iWOF, which aims to provide a robust search ability on finding optimal solutions in a huge decision space. Secondly, different from the stage in the original framework that divides the whole evolutionary process into two independent stages, the evolutionary process in the proposed iWOF is simplified to only one stage, which effectively reduces the number of predefined parameters. Besides that, the evolving numbers of weight optimization and original optimization in iWOF are adjusted adaptively according to the evolutionary stage. The experimental results on three different groups of benchmark LSMOPs validate the superiority of the proposed iWOF over WOF and other several state-of-the-art multiobjective evolutionary algorithms.