Attribute-specific fashion retrieval aims to enhance fine-grained image retrieval by emphasizing the similarity of specific attributes. Current methods primarily rely on attention mechanisms to extract attribute-related visual features but face two key challenges: the limitations of coarse-grained localization in achieving fine-grained accuracy, and an imbalance between global and local perception, where excessive focus on local features can undermine overall performance. To address these issues, we propose the fashion microscope ProFashion, which achieves pixel-level attribute awareness through optimal transport and neural semantic aggregation. The framework begins by employing optimal transport to align semantic attributes with visual patterns from a global perspective, generating an attribute-visual value map that highlights distinctive regions while reducing interference. This is followed by simulating the human brain's perception of attribute feature patterns through superpixel generation and aggregation, capturing attribute-related features at the pixel semantic level and forming key semantic clusters that preserve microstructures. Building on this, an attribute graph is constructed to facilitate feature clustering, significantly enhancing the framework's capability to handle overlapping features and cross-scale relationships. Comprehensive experiments on the FashionAI, DeepFashion, and DARN datasets demonstrate the framework's effectiveness, achieving overall MAP improvements of 3.11%, 3.70%, and 3.49%, respectively. Additionally, the framework delivers relative average throughput gains of 26.94%, 22.22%, and 24.78% on the FashionAI, DeepFashion, and DARN datasets, respectively.

Shuaiyi Nie, Dingsiyu, Wenyuan Zhang, Linhao Yu, Tianmeng Yang, Yao Chen, Weichong Yin, Yu Sun, Hua Wu, Tingwen Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang, Tingwen Liu, Li Guo, Yafeng Deng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

User cold-start problem is a long-standing challenge in recommendation systems. Fortunately, cross-domain recommendation (CDR) has emerged as a highly effective remedy for the user cold-start challenge, with recently developed diffusion models (DMs) demonstrating exceptional performance. However, these DMs-based CDR methods focus on dealing with user-item interactions, overlooking correlations between items across the source and target domains. Meanwhile, the Gaussian noise added in the forward process of diffusion models would hurt user's personalized preference, leading to the difficulty in transferring user preference across domains. To this end, we propose a novel paradigm of Smoothing-Sharpening Process Model for CDR to cold-start users, termed as S2CDR which features a corruption-recovery architecture and is solved with respect to ordinary differential equations (ODEs). Specifically, the smoothing process gradually corrupts the original user-item/item-item interaction matrices derived from both domains into smoothed preference signals in a noise-free manner, and the sharpening process iteratively sharpens the preference signals to recover the unknown interactions for cold-start users. Wherein, for the smoothing process, we introduce the heat equation on the item-item similarity graph to better capture the correlations between items across domains, and further build the tailor-designed low-pass filter to filter out the high-frequency noise information for capturing user's intrinsic preference, in accordance with the graph signal processing (GSP) theory. Extensive experiments on three real-world CDR scenarios confirm that our S2CDR significantly outperforms previous SOTA methods in a training-free manner.

Attribute-Specific Fashion Retrieval (ASFR) aims to improve fine-grained image retrieval by focusing on specific attributes. However, existing patch-based attention and Transformer methods often misalign with irregular attribute regions and are prone to background noise, limiting their ability to capture subtle, pixel-level microstructures. To tackle these challenges, we propose Super Fashion., the first ASFR framework that adopts superpixel tokens within a Transformer architecture. Super Fashion initially employs an attribute-guided attention mechanism to extract attribute-related features, which in turn guide the cropping of semantically meaningful image regions. Superpixel segmentation is then leveraged on these regions to generate compact, semantically coherent superpixel tokens. By incorporating modality-specific embeddings for both attribute and superpixel tokens, the superpixel token-based Transformer facilitates adaptive interaction and fusion, thereby enhancing attribute localization and discrimination. Extensive experiments on FashionAI, DARN, and DeepFashion demonstrate relative overall MAP improvements of 1.84%, 9.27%, and 9.35% over prior SOTA. Super Fashion offers a new solution for web-based image retrieval.

Instruction tuning for large language models (LLMs) can drive them to produce results consistent with human goals in specific downstream tasks. However, the process of continual instruction tuning (CIT) for LLMs may bring about the catastrophic forgetting (CF) problem, where previously learned abilities are degraded. Recent methods try to alleviate the CF problem by modifying models or replaying data, which may only remember the surface-level pattern of instructions and get confused on held-out tasks. In this paper, we propose a novel continual instruction tuning method based on Key-part Information Gain (KPIG). Our method computes the information gain on masked parts to dynamically replay data and refine the training objective, which enables LLMs to capture task-aware information relevant to the correct response and alleviate overfitting to general descriptions in instructions. In addition, we propose two metrics, P-score and V-score, to measure the generalization and instruction-following abilities of LLMs. Experiments demonstrate our method achieves superior performance on both seen and held-out tasks.

The significant computational demands of large language models have increased interest in distilling reasoning abilities into smaller models via Chain-of-Thought (CoT) distillation.Current CoT distillation methods mainly focus on transferring teacher-generated rationales for complex reasoning to student models.However, they do not adequately explore teachers' dynamic attention toward critical information during reasoning.We find that language models exhibit progressive attention shifts towards key information during reasoning, which implies essential clues for drawing conclusions.Building on this observation and analysis, we introduce a novel CoT distillation framework that transfers the teacher's stepwise attention on key information to the student model.This establishes structured guidance for the student's progressive concentration on key information during reasoning.More importantly, we develop a Mixture of Layers module enabling dynamic alignment that adapts to different layers between the teacher and student.Our method achieves consistent performance improvements across multiple mathematical and commonsense reasoning datasets.To our knowledge, it is the first method to leverage stepwise attention within CoT distillation to improve small model reasoning. Input Question(a) A sample from the SVAMP dataset.The distilled student model fails to adequately utilize numerical information, leading to erroneous results, whereas the teacher model, during stepwise reasoning, effectively utilizes all numerical information to arrive at the correct final result.

Large language model (LLM) role-playing has gained widespread attention.Authentic character knowledge is crucial for constructing realistic LLM role-playing agents.However, existing works usually overlook the exploration of LLMs' ability to detect characters' known knowledge errors (KKE) and unknown knowledge errors (UKE) while playing roles, which would lead to low-quality automatic construction of character trainable corpus.In this paper, we propose RoleKE-Bench to evaluate LLMs' ability to detect errors in KKE and UKE.The results indicate that even the latest LLMs struggle to detect these two types of errors effectively, especially when it comes to familiar knowledge.We experimented with various reasoning strategies and propose an agent-based reasoning method, Self-Recollection and Self-Doubt (S 2 RD), to explore further the potential for improving error detection capabilities.Experiments show that our method effectively improves the LLMs' ability to detect error character knowledge, but it remains an issue that requires ongoing attention 1 .* indicates corresponding author. 1 The resource is accessible at https://github.com/ WYRipple/rp_kw_errors.2 In this paper, "character" also refers to "role".

Cross-domain recommendation (CDR) has demon-strated to be an effective solution for alleviating the user cold-start issue. By leveraging rich user-item interactions available in a richly informative source domain, CDR could improve the recommendation performance for cold-start users in the target domain. Previous CDR approaches mostly adhere the Embedding and Mapping (EMCDR) paradigm, which learns a user-shared mapping function to transfer users' preference from the source domain to the target domain, neglecting users' personalized preference. Recent CDR approaches further leverage the meta-learning paradigm, considering the CDR task for each user independently and learning user-specific mapping functions for each user. However, they mostly learn representations for each user individually, which ignores the common preference between different users, neglecting valuable information for CDR. In addition, all these approaches usually summarize the user's preference into an overall representation, which can hardly capture the user's multi-interest preference. To this end, we propose a personalized multi-interest modeling framework for CDR to cold-start users, termed as NF-NPCDR. Specifically, we propose a personalized preference encoder that enhances the neural process (NP) with the normalizing flow (NF) to convert the Gaussian (unimodal) distribution to a multimodal distribution, providing a novel way to capture the user's personalized multi-interest preference. Then, we propose a common preference encoder with a preference pool to capture the common preference between different users. Furthermore, we introduce a stochastic adaptive decoder to incorporate both the personalized and common preference for cold-start users, adaptively modulating both preference for better recommendation. Experimental evalu-ations demonstrate that NF-NPCDR outperforms previous SOTA approaches in five benchmark CDR scenarios.

Knowledge Graph Completion (KGC) is important in addressing the incompleteness of Knowledge Graphs (KGs) and supporting intelligent infrastructures. Recently, numerous methods have been developed for the utilization of Large Language Models (LLMs) to complete KGs in a textual generation manner. However, integrating KGs with LLMs presents multiple challenges. First, the output of LLMs is often unconstrained, and directly performing KGC with LLMs may generate entities beyond the scope of KGs and suffer from hallucination. Second, the inherent token length limitation of LLMs may hinder the integration of multi-aspect information, thereby restricting the effectiveness and efficiency in inference. Third, existing methods that integrate LLMs with KGs typically leverage partial aspects of KG contexts, overlooking the crucial role of multi-aspect information in prompting KGC. In fact, there exists crucial multi-aspect information of KG contexts that support the correctness of a factual triple, such as entity/relation descriptions, reasoning paths and entity neighbors. To this end, we propose a novel framework to explore the impact of multi-aspect information of KG contexts for KGC, termed as MAKGC. Particularly, given a target incomplete triple, MAKGC generates a list of candidate entities using an embedding model, incorporates the most relevant relation paths and entity descriptions as embeddings, and integrates them with structural embeddings into a set of instructions. In this way, the multi-aspect information facilitate LLMs in making accurate predictions. Extensive experiments demonstrate the effectiveness on benchmark datasets, and our model outperform previous competitive methods.

Sarcasm Explanation in Dialogue (SED) task aims to uncover the underlying meaning of sarcastic expressions in multimodal dialogues. While previous studies have largely focused on modeling dialogue content, they often neglect the influence of speakers and the interactions between utterances. To address this gap, we propose a novel framework called CISI, which integrates personalized communication styles, inter-speaker interaction relationships, and sarcasm-centric multimodal cues to enhance SED. To capture how personalized styles influence sarcasm expression, we model speakers' communication styles using Satir's Communication Model in psychology. Furthermore, we model the flow of sarcasm through discourse parsing, constructing explicit conversational interaction and dependencies between speakers. Lastly, we design a multimodal fusion module that aligns modality-specific cues with sarcasm-related semantics to enhance understanding. Extensive experiments on the WITS dataset demonstrate that CISI achieves superior performance. We also obtain competitive results on the MUStARD dataset for dialogue-level multimodal sarcasm detection, further showcasing the generalizability of CISI.

Low-resource complex named entity recognition focuses on identifying complex entities such as creative work, product name and so on, in scenarios where annotated training data is limited. Recent advanced works deal with this task through data augmentation and make substantial progress. However, existing methods ignore the influence of different types or levels of augmented data on model optimization in different learning stages. To address it, we propose a dual-perspective data augmentation and curriculum learning framework. Specifically, we first employ the large language model (LLM) to construct two kinds of augmented datasets from context-perspective and entity-perspective, respectively. Then, we present a multi-stage curriculum learning strategy including a novel adaptive curriculum arrangement algorithm to automatically select the most suitable kind of augmented set to optimize the target model at each training epoch, thus using the augmented data more effectively and controllably. Experimental results on the public benchmark across various low-resource settings show that our framework outperforms previous works.

Conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aims to extract the quadruple of target-aspect-opinion-sentiment within a dialogue . In DiaASQ, a quadruple’s elements often cross multiple utterances. This situation complicates the extraction process, emphasizing the need for an adequate understanding of conversational context and interactions. However, existing work independently encodes each utterance, thereby struggling to capture long-range conversational context and overlooking the deep inter-utterance dependencies. In this work, we propose a novel Dynamic Multi-scale Context Aggregation network (DMCA) to address the challenges. Specifically, we first utilize dialogue structure to generate multi-scale utterance windows for capturing rich contextual information. After that, we design a Dynamic Hierarchical Aggregation module(DHA) to integrate progressive cues between them. In addition, we form a multi-stage loss strategy to improve model performance and generalization ability. Extensive experimental results show that the DMCA model outperforms baselines significantly and achieves state-of-the-art performance1.

Chinese Spelling Correction (CSC) aims to detect and correct the misspelled characters in Chinese texts. Recent studies have achieved great success by incorporating the phonetic information for task predictions. Still, existing methods suffer from two limitations: 1) The differentiated information between textual characters and Pinyin pronunciation are underexplored. 2) The task predictions are performed with the over-emphasises on either the textual or phonetic sequence, ignoring the balanced modeling and adaptive fusion. In this work, we proposed a method to alleviate the issues above. For the first issue, a Coupled Attention Module (CAM) is proposed where a couple of attention functions capture the associated and differentiated text-phonetics information simultaneously. For the second issue, an adaptive fusion is designed to derive the phonetics-aware textual representations and text-aware phonetic representations for task predictions. Experiments on three public benchmarks demonstrate the effectiveness of our proposed method.

Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis. However, the data imbalance issue has not received sufficient attention in ASQP task. In this paper, we divide the issue into two-folds, quad-pattern imbalance and aspect-category imbalance, and propose an Adaptive Data Augmentation (ADA) framework to tackle the imbalance issue. Specifically, a data augmentation process with a condition function adaptively enhances the tail quad patterns and aspect categories, alleviating the data imbalance in ASQP. Following previous studies, we also further explore the generative framework for extracting complete quads by introducing the category prior knowledge and syntax-guided decoding target. Experimental1results demonstrate that data augmentation for imbalance in ASQP task can improve the performance, and the proposed ADA method is superior to naive data oversampling.

Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain. Traditional CDR studies follow the embedding and mapping (EMCDR) paradigm, which transfers user representations from the source to target domain by learning a user-shared mapping function, neglecting the user-specific preference. Recent CDR studies attempt to learn user-specific mapping functions in meta-learning paradigm, which regards each user's CDR as an individual task, but neglects the preference correlations among users, limiting the beneficial information for user representations. Moreover, both of the paradigms neglect the explicit user-item interactions from both domains during the mapping process. To address the above issues, this paper proposes a novel CDR framework with neural process (NP), termed as CDRNP. Particularly, it develops the meta-learning paradigm to leverage user-specific preference, and further introduces a stochastic process by NP to capture the preference correlations among the overlapping and cold-start users, thus generating more powerful mapping functions by mapping the user-specific preference and common preference correlations to a predictive probability distribution. In addition, we also introduce a preference remainer to enhance the common preference from the overlapping users, and finally devises an adaptive conditional decoder with preference modulation to make prediction for cold-start users with items in the target domain. Experimental results demonstrate that CDRNP outperforms previous SOTA methods in three real-world CDR scenarios.

Network-based Intrusion Detection(NID) is an effective means to deal with network attacks. NID is able to detect different types of network attacks by analyzing network traffic. However, in the real world, network traffic contains majority and minority class attacks as well as a large number of normal traffic samples. The imbalance in the number of training samples of various types of network traffic makes network intrusion detection very poor. Due to the lack of training samples, traditional NID can’t learn the characteristics of minority class attacks, which leads to the failure of NID to detect minority class attacks. Therefore, in order to solve the problem brought by imbalanced data, we propose a network intrusion detection algorithm based on the sampling and improved One-vs-All(OVA) technique. The dataset is balanced by downsampling the majority class data based on K-means clustering and oversampling the minority class data based on Auxiliary Classifier Generative Adversarial Network(ACGAN), improve classification accuracy through OVA-based model training and testing. We conduct validation experiments on the NSL-KDD dataset, and the experimental results show that the proposed method achieves excellent results in terms of Accuracy, Precision, Recall and F1-score. Compared with existing state-of-the-art methods, the proposed method not only achieves excellent detection performance with low false positive rate, but also addresses the learning problem of imbalanced data more effectively.

In recent years, temporal knowledge graph (TKG) reasoning has received significant attention. Most existing methods assume that all timestamps and corresponding graphs are available during training, which makes it difficult to predict future events. To address this issue, recent works learn to infer future events based on historical information. However, these methods do not comprehensively consider the latent patterns behind temporal changes, to pass historical information selectively, update representations appropriately and predict events accurately. In this paper, we propose the Historical Information Passing (HIP) network to predict future events. HIP network passes information from temporal, structural and repetitive perspectives, which are used to model the temporal evolution of events, the interactions of events at the same time step, and the known events respectively. In particular, our method considers the updating of relation representations and adopts three scoring functions corresponding to the above dimensions. Experimental results on five benchmark datasets show the superiority of HIP network, and the significant improvements on Hits@1 prove that our method can more accurately predict what is going to happen.