pwc-archive/papers-with-abstracts huggingface dataset:
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition
Authors: Yiyan Xu, Wuqiang Zheng, Wenjie Wang, Fengbin Zhu, Xinting Hu, Yang Zhang, Fuli Feng, Tat-Seng Chua
Date: 2025-04-24 00:00:00
Tasks: Disentanglement, Image Generation, Personalized Image Generation, Representation Learning

Personalized image generation has emerged as a promising direction in multimodal content creation. It aims to synthesize images tailored to individual style preferences (e.g., color schemes, character appearances, layout) and semantic intentions (e.g., emotion, action, scene contexts) by leveraging user-interacted history images and multimodal instructions. Despite notable progress, existing methods -- whether based on diffusion models, large language models, or Large Multimodal Models (LMMs) -- struggle to accurately capture and fuse user style preferences and semantic intentions. In particular, the state-of-the-art LMM-based method suffers from the entanglement of visual features, leading to Guidance Collapse, where the generated images fail to preserve user-preferred styles or reflect the specified semantics. To address these limitations, we introduce DRC, a novel personalized image generation framework that enhances LMMs through Disentangled Representation Composition. DRC explicitly extracts user style preferences and semantic intentions from history images and the reference image, respectively, to form user-specific latent instructions that guide image generation within LMMs. Specifically, it involves two critical learning stages: 1) Disentanglement learning, which employs a dual-tower disentangler to explicitly separate style and semantic features, optimized via a reconstruction-driven paradigm with difficulty-aware importance sampling; and 2) Personalized modeling, which applies semantic-preserving augmentations to effectively adapt the disentangled representations for robust personalized generation. Extensive experiments on two benchmarks demonstrate that DRC shows competitive performance while effectively mitigating the guidance collapse issue, underscoring the importance of disentangled representation learning for controllable and effective personalized image generation.
Koopman-Based Event-Triggered Control from Data
Authors: Zeyad M. Manaa, Ayman M. Abdallah, Mohamed Ismail, Samil El Ferik
Date: 2025-04-19 00:00:00

Event-triggered Control (ETC) presents a promising paradigm for efficient resource usage in networked and embedded control systems by reducing communication instances compared to traditional time-triggered strategies. This paper introduces a novel approach to ETC for discrete-time nonlinear systems using a data-driven framework. By leveraging Koopman operator theory, the nonlinear system dynamics are globally linearized (approximately in practical settings) in a higher-dimensional space. We design a state-feedback controller and an event-triggering policy directly from data, ensuring exponential stability in Lyapunov sense. The proposed method is validated through extensive simulation experiments, demonstrating significant resource savings.
You Are What You Bought: Generating Customer Personas for E-commerce Applications
Authors: Yimin Shi, Yang Fei, Shiqi Zhang, Haixun Wang, Xiaokui Xiao
Date: 2025-04-24 00:00:00

In e-commerce, user representations are essential for various applications. Existing methods often use deep learning techniques to convert customer behaviors into implicit embeddings. However, these embeddings are difficult to understand and integrate with external knowledge, limiting the effectiveness of applications such as customer segmentation, search navigation, and product recommendations. To address this, our paper introduces the concept of the customer persona. Condensed from a customer's numerous purchasing histories, a customer persona provides a multi-faceted and human-readable characterization of specific purchase behaviors and preferences, such as Busy Parents or Bargain Hunters. This work then focuses on representing each customer by multiple personas from a predefined set, achieving readable and informative explicit user representations. To this end, we propose an effective and efficient solution GPLR. To ensure effectiveness, GPLR leverages pre-trained LLMs to infer personas for customers. To reduce overhead, GPLR applies LLM-based labeling to only a fraction of users and utilizes a random walk technique to predict personas for the remaining customers. We further propose RevAff, which provides an absolute error $\epsilon$ guarantee while improving the time complexity of the exact solution by a factor of at least $O(\frac{\epsilon\cdot|E|N}{|E|+N\log N})$, where $N$ represents the number of customers and products, and $E$ represents the interactions between them. We evaluate the performance of our persona-based representation in terms of accuracy and robustness for recommendation and customer segmentation tasks using three real-world e-commerce datasets. Most notably, we find that integrating customer persona representations improves the state-of-the-art graph convolution-based recommendation model by up to 12% in terms of NDCG@K and F1-Score@K.
Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?
Authors: Kaidong Feng, Zhu Sun, Jie Yang, Hui Fang, Xinghua Qu, Wenyuan Liu
Date: 2025-04-24 00:00:00
Tasks: In-Context Learning, Knowledge Distillation, Language Modeling, Language Modelling, Large Language Model

LLMs are increasingly explored for bundle generation, thanks to their reasoning capabilities and knowledge. However, deploying large-scale LLMs introduces significant efficiency challenges, primarily high computational costs during fine-tuning and inference due to their massive parameterization. Knowledge distillation (KD) offers a promising solution, transferring expertise from large teacher models to compact student models. This study systematically investigates knowledge distillation approaches for bundle generation, aiming to minimize computational demands while preserving performance. We explore three critical research questions: (1) how does the format of KD impact bundle generation performance? (2) to what extent does the quantity of distilled knowledge influence performance? and (3) how do different ways of utilizing the distilled knowledge affect performance? We propose a comprehensive KD framework that (i) progressively extracts knowledge (patterns, rules, deep thoughts); (ii) captures varying quantities of distilled knowledge through different strategies; and (iii) exploits complementary LLM adaptation techniques (in-context learning, supervised fine-tuning, combination) to leverage distilled knowledge in small student models for domain-specific adaptation and enhanced efficiency. Extensive experiments provide valuable insights into how knowledge format, quantity, and utilization methodologies collectively shape LLM-based bundle generation performance, exhibiting KD's significant potential for more efficient yet effective LLM-based bundle generation.
Dynamic Superblock Pruning for Fast Learned Sparse Retrieval
Authors: Parker Carlson, Wentai Xie, Shanxiu He, Tao Yang
Date: 2025-04-23 00:00:00
Tasks: CPU, Retrieval

This paper proposes superblock pruning (SP) during top-k online document retrieval for learned sparse representations. SP structures the sparse index as a set of superblocks on a sequence of document blocks and conducts a superblock-level selection to decide if some superblocks can be pruned before visiting their child blocks. SP generalizes the previous flat block or cluster-based pruning, allowing the early detection of groups of documents that cannot or are less likely to appear in the final top-k list. SP can accelerate sparse retrieval in a rank-safe or approximate manner under a high-relevance competitiveness constraint. Our experiments show that the proposed scheme significantly outperforms state-of-the-art baselines on MS MARCO passages on a single-threaded CPU.
From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
Authors: Yaxiong Wu, Sheng Liang, Chen Zhang, Yichao Wang, Yongyue Zhang, Huifeng Guo, Ruiming Tang, Yong liu
Date: 2025-04-22 00:00:00

Memory is the process of encoding, storing, and retrieving information, allowing humans to retain experiences, knowledge, skills, and facts over time, and serving as the foundation for growth and effective interaction with the world. It plays a crucial role in shaping our identity, making decisions, learning from past experiences, building relationships, and adapting to changes. In the era of large language models (LLMs), memory refers to the ability of an AI system to retain, recall, and use information from past interactions to improve future responses and interactions. Although previous research and reviews have provided detailed descriptions of memory mechanisms, there is still a lack of a systematic review that summarizes and analyzes the relationship between the memory of LLM-driven AI systems and human memory, as well as how we can be inspired by human memory to construct more powerful memory systems. To achieve this, in this paper, we propose a comprehensive survey on the memory of LLM-driven AI systems. In particular, we first conduct a detailed analysis of the categories of human memory and relate them to the memory of AI systems. Second, we systematically organize existing memory-related work and propose a categorization method based on three dimensions (object, form, and time) and eight quadrants. Finally, we illustrate some open problems regarding the memory of current AI systems and outline possible future directions for memory in the era of large language models.
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
Authors: Shiyin Tan, Jaeeon Park, Dongyuan Li, Renhe Jiang, Manabu Okumura
Date: 2025-04-23 00:00:00
Tasks: Document Ranking, Document Summarization, Multi-Document Summarization, Retrieval

In the field of multi-document summarization (MDS), transformer-based models have demonstrated remarkable success, yet they suffer an input length limitation. Current methods apply truncation after the retrieval process to fit the context length; however, they heavily depend on manually well-crafted queries, which are impractical to create for each document set for MDS. Additionally, these methods retrieve information at a coarse granularity, leading to the inclusion of irrelevant content. To address these issues, we propose a novel retrieval-based framework that integrates query selection and document ranking and shortening into a unified process. Our approach identifies the most salient elementary discourse units (EDUs) from input documents and utilizes them as latent queries. These queries guide the document ranking by calculating relevance scores. Instead of traditional truncation, our approach filters out irrelevant EDUs to fit the context length, ensuring that only critical information is preserved for summarization. We evaluate our framework on multiple MDS datasets, demonstrating consistent improvements in ROUGE metrics while confirming its scalability and flexibility across diverse model architectures. Additionally, we validate its effectiveness through an in-depth analysis, emphasizing its ability to dynamically select appropriate queries and accurately rank documents based on their relevance scores. These results demonstrate that our framework effectively addresses context-length constraints, establishing it as a robust and reliable solution for MDS.
DAPLSR: Data Augmentation Partial Least Squares Regression Model via Manifold Optimization
Authors: Haoran Chen, Jiapeng Liu, Jiafan Wang, Wenjun Shi
Date: 2025-04-23 00:00:00
Tasks: Data Augmentation, regression

Traditional Partial Least Squares Regression (PLSR) models frequently underperform when handling data characterized by uneven categories. To address the issue, this paper proposes a Data Augmentation Partial Least Squares Regression (DAPLSR) model via manifold optimization. The DAPLSR model introduces the Synthetic Minority Over-sampling Technique (SMOTE) to increase the number of samples and utilizes the Value Difference Metric (VDM) to select the nearest neighbor samples that closely resemble the original samples for generating synthetic samples. In solving the model, in order to obtain a more accurate numerical solution for PLSR, this paper proposes a manifold optimization method that uses the geometric properties of the constraint space to improve model degradation and optimization. Comprehensive experiments show that the proposed DAPLSR model achieves superior classification performance and outstanding evaluation metrics on various datasets, significantly outperforming existing methods.
Information Leakage of Sentence Embeddings via Generative Embedding Inversion Attacks
Authors: Antonios Tragoudaras, Theofanis Aslanidis, Emmanouil Georgios Lionis, Marina Orozco González, Panagiotis Eustratiadis
Date: 2025-04-23 00:00:00
Tasks: Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

Text data are often encoded as dense vectors, known as embeddings, which capture semantic, syntactic, contextual, and domain-specific information. These embeddings, widely adopted in various applications, inherently contain rich information that may be susceptible to leakage under certain attacks. The GEIA framework highlights vulnerabilities in sentence embeddings, demonstrating that they can reveal the original sentences they represent. In this study, we reproduce GEIA's findings across various neural sentence embedding models. Additionally, we contribute new analysis to examine whether these models leak sensitive information from their training datasets. We propose a simple yet effective method without any modification to the attacker's architecture proposed in GEIA. The key idea is to examine differences between log-likelihood for masked and original variants of data that sentence embedding models have been pre-trained on, calculated on the embedding space of the attacker. Our findings indicate that following our approach, an adversary party can recover meaningful sensitive information related to the pre-training knowledge of the popular models used for creating sentence embeddings, seriously undermining their security. Our code is available on: https://github.com/taslanidis/GEIA
MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation
Authors: Xu Guo, Tong Zhang, Fuyun Wang, Xudong Wang, Xiaoya Zhang, Xin Liu, Zhen Cui
Date: 2025-04-23 00:00:00
Tasks: Contrastive Learning, Hypergraph Contrastive Learning, Recommendation Systems

The burgeoning presence of multimodal content-sharing platforms propels the development of personalized recommender systems. Previous works usually suffer from data sparsity and cold-start problems, and may fail to adequately explore semantic user-product associations from multimodal data. To address these issues, we propose a novel Multi-Modal Hypergraph Contrastive Learning (MMHCL) framework for user recommendation. For a comprehensive information exploration from user-product relations, we construct two hypergraphs, i.e. a user-to-user (u2u) hypergraph and an item-to-item (i2i) hypergraph, to mine shared preferences among users and intricate multimodal semantic resemblance among items, respectively. This process yields denser second-order semantics that are fused with first-order user-item interaction as complementary to alleviate the data sparsity issue. Then, we design a contrastive feature enhancement paradigm by applying synergistic contrastive learning. By maximizing/minimizing the mutual information between second-order (e.g. shared preference pattern for users) and first-order (information of selected items for users) embeddings of the same/different users and items, the feature distinguishability can be effectively enhanced. Compared with using sparse primary user-item interaction only, our MMHCL obtains denser second-order hypergraphs and excavates more abundant shared attributes to explore the user-product associations, which to a certain extent alleviates the problems of data sparsity and cold-start. Extensive experiments have comprehensively demonstrated the effectiveness of our method. Our code is publicly available at: https://github.com/Xu107/MMHCL.
Modality Reliability Guided Multimodal Recommendation
Authors: Xue Dong, Xuemeng Song, Na Zheng, Sicheng Zhao, Guiguang Ding
Date: 2025-04-23 00:00:00
Tasks: Multimodal Recommendation

Multimodal recommendation faces an issue of the performance degradation that the uni-modal recommendation sometimes achieves the better performance. A possible reason is that the unreliable item modality data hurts the fusion result. Several existing studies have introduced weights for different modalities to reduce the contribution of the unreliable modality data in predicting the final user rating. However, they fail to provide appropriate supervisions for learning the modality weights, making the learned weights imprecise. Therefore, we propose a modality reliability guided multimodal recommendation framework that uniquely learns the modality weights supervised by the modality reliability. Considering that there is no explicit label provided for modality reliability, we resort to automatically identify it through the BPR recommendation objective. In particular, we define a modality reliability vector as the supervision label by the difference between modality-specific user ratings to positive and negative items, where a larger difference indicates a higher reliability of the modality as the BPR objective is better satisfied. Furthermore, to enhance the effectiveness of the supervision, we calculate the confidence level for the modality reliability vector, which dynamically adjusts the supervision strength and eliminates the harmful supervision. Extensive experiments on three real-world datasets show the effectiveness of the proposed method.
FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation
Authors: Chanyeol Choi, Jihoon Kwon, Jaeseon Ha, Hojun Choi, Chaewoon Kim, YongJae lee, Jy-yong Sohn, Alejandro Lopez-Lira
Date: 2025-04-22 00:00:00
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation

In the fast-paced financial domain, accurate and up-to-date information is critical to addressing ever-evolving market conditions. Retrieving this information correctly is essential in financial Question-Answering (QA), since many language models struggle with factual accuracy in this domain. We present FinDER, an expert-generated dataset tailored for Retrieval-Augmented Generation (RAG) in finance. Unlike existing QA datasets that provide predefined contexts and rely on relatively clear and straightforward queries, FinDER focuses on annotating search-relevant evidence by domain experts, offering 5,703 query-evidence-answer triplets derived from real-world financial inquiries. These queries frequently include abbreviations, acronyms, and concise expressions, capturing the brevity and ambiguity common in the realistic search behavior of professionals. By challenging models to retrieve relevant information from large corpora rather than relying on readily determined contexts, FinDER offers a more realistic benchmark for evaluating RAG systems. We further present a comprehensive evaluation of multiple state-of-the-art retrieval models and Large Language Models, showcasing challenges derived from a realistic benchmark to drive future research on truthful and precise RAG in the financial domain.
JFlow: Model-Independent Spherical Jeans Analysis using Equivariant Continuous Normalizing Flows
Authors: Sung Hak Lim, Kohei Hayashi, Shun'ichi Horigome, Shigeki Matsumoto, Mihoko M. Nojiri
Date: 2025-05-01 00:00:00

The kinematics of stars in dwarf spheroidal galaxies have been studied to understand the structure of dark matter halos. However, the kinematic information of these stars is often limited to celestial positions and line-of-sight velocities, making full phase space analysis challenging. Conventional methods rely on projected analytic phase space density models with several parameters and infer dark matter halo structures by solving the spherical Jeans equation. In this paper, we introduce an unsupervised machine learning method for solving the spherical Jeans equation in a model-independent way as a first step toward model-independent analysis of dwarf spheroidal galaxies. Using equivariant continuous normalizing flows, we demonstrate that spherically symmetric stellar phase space densities and velocity dispersions can be estimated without model assumptions. As a proof of concept, we apply our method to Gaia challenge datasets for spherical models and measure dark matter mass densities for given velocity anisotropy profiles. Our method can identify halo structures accurately, even with a small number of tracer stars.
Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model
Authors: Luankang Zhang, Kenan Song, Yi Quan Lee, Wei Guo, Hao Wang, Yawen Li, Huifeng Guo, Yong liu, Defu Lian, Enhong Chen
Date: 2025-04-23 00:00:00
Tasks: Recommendation Systems, Retrieval

In recommendation systems, the traditional multi-stage paradigm, which includes retrieval and ranking, often suffers from information loss between stages and diminishes performance. Recent advances in generative models, inspired by natural language processing, suggest the potential for unifying these stages to mitigate such loss. This paper presents the Unified Generative Recommendation Framework (UniGRF), a novel approach that integrates retrieval and ranking into a single generative model. By treating both stages as sequence generation tasks, UniGRF enables sufficient information sharing without additional computational costs, while remaining model-agnostic. To enhance inter-stage collaboration, UniGRF introduces a ranking-driven enhancer module that leverages the precision of the ranking stage to refine retrieval processes, creating an enhancement loop. Besides, a gradient-guided adaptive weighter is incorporated to dynamically balance the optimization of retrieval and ranking, ensuring synchronized performance improvements. Extensive experiments demonstrate that UniGRF significantly outperforms existing models on benchmark datasets, confirming its effectiveness in facilitating information transfer. Ablation studies and further experiments reveal that UniGRF not only promotes efficient collaboration between stages but also achieves synchronized optimization. UniGRF provides an effective, scalable, and compatible framework for generative recommendation systems.