pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Token Boundaries: Integrating Human Chunking Mechanisms into Multimodal LLMs
Authors: Dongxing Yu
Date: 2025-05-03 00:00:00
Tasks: Chunking, Question Answering, Visual Question Answering

Recent advancements in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing diverse data types, yet significant disparities persist between human cognitive processes and computational approaches to multimodal information integration. This research presents a systematic investigation into the parallels between human cross-modal chunking mechanisms and token representation methodologies in MLLMs. Through empirical studies comparing human performance patterns with model behaviors across visual-linguistic tasks, we demonstrate that conventional static tokenization schemes fundamentally constrain current models' capacity to simulate the dynamic, context-sensitive nature of human information processing. We propose a novel framework for dynamic cross-modal tokenization that incorporates adaptive boundaries, hierarchical representations, and alignment mechanisms grounded in cognitive science principles. Quantitative evaluations demonstrate that our approach yields statistically significant improvements over state-of-the-art models on benchmark tasks (+7.8% on Visual Question Answering, +5.3% on Complex Scene Description) while exhibiting more human-aligned error patterns and attention distributions. These findings contribute to the theoretical understanding of the relationship between human cognition and artificial intelligence, while providing empirical evidence for developing more cognitively plausible AI systems.
Enhancing the Dynamic Range of Quantum Sensing via Quantum Circuit Learning
Authors: Hideaki Kawaguchi, Yuichiro Mori, Takahiko Satoh, Yuichiro Matsuzaki
Date: 2025-05-08 00:00:00

Quantum metrology is a promising application of quantum technologies, enabling the precise measurement of weak external fields at a local scale. In typical quantum sensing protocols, a qubit interacts with an external field, and the amplitude of the field is estimated by analyzing the expectation value of a measured observable. Sensitivity can, in principle, be enhanced by increasing the number of qubits within a fixed volume, thereby maintaining spatial resolution. However, at high qubit densities, inter-qubit interactions induce complex many-body dynamics, resulting in multiple oscillations in the expectation value of the observable even for small field amplitudes. This ambiguity reduces the dynamic range of the sensing protocol. We propose a method to overcome the limitation in quantum metrology by adopting a quantum circuit learning framework using a parameterized quantum circuit to approximate a target function by optimizing the circuit parameters. In our method, after the qubits interact with the external field, we apply a sequence of parameterized quantum gates and measure a suitable observable. By optimizing the gate parameters, the expectation value is trained to exhibit a monotonic response within a target range of field amplitudes, thereby eliminating multiple oscillations and enhancing the dynamic range. This method offers a strategy for improving quantum sensing performance in dense qubit systems.
Artifact Sharing for Information Retrieval Research
Authors: Sean MacAvaney
Date: 2025-05-08 00:00:00
Tasks: Information Retrieval, Retrieval

Sharing artifacts -- such as trained models, pre-built indexes, and the code to use them -- aids in reproducibility efforts by allowing researchers to validate intermediate steps and improves the sustainability of research by allowing multiple groups to build off one another's prior computational work. Although there are de facto consensuses on how to share research code (through a git repository linked to from publications) and trained models (via HuggingFace Hub), there is no consensus for other types of artifacts, such as built indexes. Given the practical utility of using shared indexes, researchers have resorted to self-hosting these resources or performing ad hoc file transfers upon request, ultimately limiting the artifacts' discoverability and reuse. This demonstration introduces a flexible and interoperable way to share artifacts for Information Retrieval research, improving both their accessibility and usability.
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems
Authors: Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio
Date: 2025-05-08 00:00:00
Tasks: Data Poisoning, Language Modeling, Language Modelling, Large Language Model, RAG, Recommendation Systems, Retrieval, Semantic Similarity, Semantic Textual Similarity

We present a systematic study of provider-side data poisoning in retrieval-augmented recommender systems (RAG-based). By modifying only a small fraction of tokens within item descriptions -- for instance, adding emotional keywords or borrowing phrases from semantically related items -- an attacker can significantly promote or demote targeted items. We formalize these attacks under token-edit and semantic-similarity constraints, and we examine their effectiveness in both promotion (long-tail items) and demotion (short-head items) scenarios. Our experiments on MovieLens, using two large language model (LLM) retrieval modules, show that even subtle attacks shift final rankings and item exposures while eluding naive detection. The results underscore the vulnerability of RAG-based pipelines to small-scale metadata rewrites and emphasize the need for robust textual consistency checks and provenance tracking to thwart stealthy provider-side poisoning.
Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R
Authors: Kevin Innerebner, Dominik Kowald, Markus Schedl, Elisabeth Lex
Date: 2025-05-08 00:00:00
Tasks: Decision Making, Recommendation Systems

Recommender systems often rely on sub-symbolic machine learning approaches that operate as opaque black boxes. These approaches typically fail to account for the cognitive processes that shape user preferences and decision-making. In this vision paper, we propose a hybrid user modeling framework based on the cognitive architecture ACT-R that integrates symbolic and sub-symbolic representations of human memory. Our goal is to combine ACT-R's declarative memory, which is responsible for storing symbolic chunks along sub-symbolic activations, with its procedural memory, which contains symbolic production rules. This integration will help simulate how users retrieve past experiences and apply decision-making strategies. With this approach, we aim to provide more transparent recommendations, enable rule-based explanations, and facilitate the modeling of cognitive biases. We argue that our approach has the potential to inform the design of a new generation of human-centered, psychology-informed recommender systems.
Divide-and-Conquer: Cold-Start Bundle Recommendation via Mixture of Diffusion Experts
Authors: Ming Li, Lin Li, Xiaohui Tao, Dong Zhang, Jimmy Xiangji Huang
Date: 2025-05-08 00:00:00
Tasks: Mixture-of-Experts

Cold-start bundle recommendation focuses on modeling new bundles with insufficient information to provide recommendations. Advanced bundle recommendation models usually learn bundle representations from multiple views (e.g., interaction view) at both the bundle and item levels. Consequently, the cold-start problem for bundles is more challenging than that for traditional items due to the dual-level multi-view complexity. In this paper, we propose a novel Mixture of Diffusion Experts (MoDiffE) framework, which employs a divide-and-conquer strategy for cold-start bundle recommendation and follows three steps:(1) Divide: The bundle cold-start problem is divided into independent but similar sub-problems sequentially by level and view, which can be summarized as the poor representation of feature-missing bundles in prior-embedding models. (2) Conquer: Beyond prior-embedding models that fundamentally provide the embedded representations, we introduce a diffusion-based method to solve all sub-problems in a unified way, which directly generates diffusion representations using diffusion models without depending on specific features. (3) Combine: A cold-aware hierarchical Mixture of Experts (MoE) is employed to combine results of the sub-problems for final recommendations, where the two models for each view serve as experts and are adaptively fused for different bundles in a multi-layer manner. Additionally, MoDiffE adopts a multi-stage decoupled training pipeline and introduces a cold-start gating augmentation method to enable the training of gating for cold bundles. Through extensive experiments on three real-world datasets, we demonstrate that MoDiffE significantly outperforms existing solutions in handling cold-start bundle recommendation. It achieves up to a 0.1027 absolute gain in Recall@20 in cold-start scenarios and up to a 47.43\% relative improvement in all-bundle scenarios.
LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device Collaboration
Authors: Yingyi Zhang, Pengyue Jia, Xianneng Li, Derong Xu, Maolin Wang, Yichao Wang, Zhaocheng Du, Huifeng Guo, Yong liu, Ruiming Tang, Xiangyu Zhao
Date: 2025-05-08 00:00:00
Tasks: Privacy Preserving, RAG, Retrieval, Retrieval-augmented Generation

Cloud-device collaboration leverages on-cloud Large Language Models (LLMs) for handling public user queries and on-device Small Language Models (SLMs) for processing private user data, collectively forming a powerful and privacy-preserving solution. However, existing approaches often fail to fully leverage the scalable problem-solving capabilities of on-cloud LLMs while underutilizing the advantage of on-device SLMs in accessing and processing personalized data. This leads to two interconnected issues: 1) Limited utilization of the problem-solving capabilities of on-cloud LLMs, which fail to align with personalized user-task needs, and 2) Inadequate integration of user data into on-device SLM responses, resulting in mismatches in contextual user information. In this paper, we propose a Leader-Subordinate Retrieval framework for Privacy-preserving cloud-device collaboration (LSRP), a novel solution that bridges these gaps by: 1) enhancing on-cloud LLM guidance to on-device SLM through a dynamic selection of task-specific leader strategies named as user-to-user retrieval-augmented generation (U-U-RAG), and 2) integrating the data advantages of on-device SLMs through small model feedback Direct Preference Optimization (SMFB-DPO) for aligning the on-cloud LLM with the on-device SLM. Experiments on two datasets demonstrate that LSRP consistently outperforms state-of-the-art baselines, significantly improving question-answer relevance and personalization, while preserving user privacy through efficient on-device retrieval. Our code is available at: https://github.com/Zhang-Yingyi/LSRP.
Learning Item Representations Directly from Multimodal Features for Effective Recommendation
Authors: Xin Zhou, Xiaoxiong Zhang, Dusit Niyato, Zhiqi Shen
Date: 2025-05-08 00:00:00
Tasks: Recommendation Systems

Conventional multimodal recommender systems predominantly leverage Bayesian Personalized Ranking (BPR) optimization to learn item representations by amalgamating item identity (ID) embeddings with multimodal features. Nevertheless, our empirical and theoretical findings unequivocally demonstrate a pronounced optimization gradient bias in favor of acquiring representations from multimodal features over item ID embeddings. As a consequence, item ID embeddings frequently exhibit suboptimal characteristics despite the convergence of multimodal feature parameters. Given the rich informational content inherent in multimodal features, in this paper, we propose a novel model (i.e., LIRDRec) that learns item representations directly from these features to augment recommendation performance. Recognizing that features derived from each modality may capture disparate yet correlated aspects of items, we propose a multimodal transformation mechanism, integrated with modality-specific encoders, to effectively fuse features from all modalities. Moreover, to differentiate the influence of diverse modality types, we devise a progressive weight copying fusion module within LIRDRec. This module incrementally learns the weight assigned to each modality in synthesizing the final user or item representations. Finally, we utilize the powerful visual understanding of Multimodal Large Language Models (MLLMs) to convert the item images into texts and extract semantics embeddings upon the texts via LLMs. Empirical evaluations conducted on five real-world datasets validate the superiority of our approach relative to competing baselines. It is worth noting the proposed model, equipped with embeddings extracted from MLLMs and LLMs, can further improve the recommendation accuracy of NDCG@20 by an average of 4.21% compared to the original embeddings.
Retrieval Augmented Generation Evaluation for Health Documents
Authors: Mario Ceresa, Lorenzo Bertolini, Valentin Comte, Nicholas Spadaro, Barbara Raffael, Brigitte Toussaint, Sergio Consoli, Amalia Muñoz Piñeiro, Alex Patak, Maddalena Querci, Tobias Wiesenthal
Date: 2025-05-07 00:00:00
Tasks: Moment Retrieval, RAG, Retrieval, Retrieval-augmented Generation

Safe and trustworthy use of Large Language Models (LLM) in the processing of healthcare documents and scientific papers could substantially help clinicians, scientists and policymakers in overcoming information overload and focusing on the most relevant information at a given moment. Retrieval Augmented Generation (RAG) is a promising method to leverage the potential of LLMs while enhancing the accuracy of their outcomes. This report assesses the potentials and shortcomings of such approaches in the automatic knowledge synthesis of different types of documents in the health domain. To this end, it describes: (1) an internally developed proof of concept pipeline that employs state-of-the-art practices to deliver safe and trustable analysis for healthcare documents and scientific papers called RAGEv (Retrieval Augmented Generation Evaluation); (2) a set of evaluation tools for LLM-based document retrieval and generation; (3) a benchmark dataset to verify the accuracy and veracity of the results called RAGEv-Bench. It concludes that careful implementations of RAG techniques could minimize most of the common problems in the use of LLMs for document processing in the health domain, obtaining very high scores both on short yes/no answers and long answers. There is a high potential for incorporating it into the day-to-day work of policy support tasks, but additional efforts are required to obtain a consistent and trustworthy tool.
CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design
Authors: Weitao Feng, Hang Zhou, Jing Liao, Li Cheng, Wenbo Zhou
Date: 2025-04-28 00:00:00
Proceeding: CVPR 2025 1
Tasks: Indoor Scene Synthesis, Scene Generation

We present a novel approach for indoor scene synthesis, which learns to arrange decomposed cuboid primitives to represent 3D objects within a scene. Unlike conventional methods that use bounding boxes to determine the placement and scale of 3D objects, our approach leverages cuboids as a straightforward yet highly effective alternative for modeling objects. This allows for compact scene generation while minimizing object intersections. Our approach, coined CasaGPT for Cuboid Arrangement and Scene Assembly, employs an autoregressive model to sequentially arrange cuboids, producing physically plausible scenes. By applying rejection sampling during the fine-tuning stage to filter out scenes with object collisions, our model further reduces intersections and enhances scene quality. Additionally, we introduce a refined dataset, 3DFRONT-NC, which eliminates significant noise presented in the original dataset, 3D-FRONT. Extensive experiments on the 3D-FRONT dataset as well as our dataset demonstrate that our approach consistently outperforms the state-of-the-art methods, enhancing the realism of generated scenes, and providing a promising direction for 3D scene synthesis.
Joint User Association and Bandwidth Assignment for Digital Twin-Assisted Multi-RAT Networks
Authors: Manobendu Sarker, Md. Zoheb Hassan, Georges Kaddoum
Date: 2025-05-07 00:00:00
Tasks: Computational Efficiency

In this paper, we investigate user equipment (UE)-radio access technology (RAT) association and bandwidth assignment to maximize sum-rates in a multi-RAT network. To this end, we formulate an optimization problem that jointly addresses UE association and bandwidth allocation, adhering to practical constraints. Because of the NP-hard nature of this problem, finding a globally optimal solution is computationally infeasible. To address this challenge, we propose a centralized and computationally efficient heuristic algorithm that aims to maximize sum-rates while enhancing quality of service (QoS). Yet, the proposed approach requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale multi-RAT networks. To alleviate this burden, we use a digital twin (DT) of the multi-RAT network, leveraging its context-awareness to acquire global CSI with reduced overhead. Our numerical results reveal that our approach improves sum-rates by up to 43% over baseline method, with less than a 5% deviation from the theoretical optimal solution, while achieving up to a 43% improvement in QoS. Further analysis reveals that our method not only surpasses the optimal solution in terms of QoS enhancement, but also ensures significant computational efficiency.
Mitigating Adversarial Attacks on ECG Classification in Federated Learning via Adversarial Training
Authors: Eyüpcan Çelik, Mehmet Kemal Güllü
Date: 2025-05-01 00:00:00
Proceeding: Artificial Intelligence Theory and Applications 2025 5
Tasks: ECG Classification, Electrocardiography (ECG), Federated Learning

Federated Learning (FL) has become an important research area in recent years, particularly when dealing with sensitive data such as healthcare information. Since healthcare data contains critical and personal information, FL provides a major advantage by enabling training on local devices without requiring data to be collected on a central server. In the analysis of healthcare data, such as electrocardiography (ECG), FL enables local processing of data while preserving privacy. However, despite its privacy benefits, FL can be vulnerable to attacks. Malicious inputs aim to degrade model accuracy, known as adversarial attacks (AA), can pose a major threat. Adversarial Training (AT) offers a defense mechanism by increasing model’s robustness against such attacks. Federated Adversarial Training (FAT) extends AT into the FL environment, combining privacy advantages with enhanced resistance to adversarial inputs. In this work, we propose the use of FAT to improve both privacy and security when classifying ECG signals, ensuring robustness against AAs. This approach involves applying AT at the client level by augmenting clean ECG data with adversarial examples generated using the Projected Gradient Descent (PGD) method. A Convolutional Neural Network (CNN) architecture was employed for local training. Experiments are conducted on the MIT-BIH Arrhythmia Database (MIT-DB). For comparison, we also trained an FL model without incorporating FAT. Both models were tested on the original test data as well as on adversarially attacked versions generated using PGD, Fast Gradient Sign Method (FGSM), Carlini & Wagner (CW), and Basic Iterative Method (BIM). The results show that the FL system with FAT significantly outperforms the system without FAT in resisting AAs, with a slight compromise in performance on the original test data, thus highlighting the effectiveness of FAT in enhancing model robustness against AAs for ECG classification tasks. Code is available at https://github.com/Skyress1/ECG-FAT-Code.
MicarVLMoE: A Modern Gated Cross-Aligned Vision-Language Mixture of Experts Model for Medical Image Captioning and Report Generation
Authors: Amaan Izhar, Nurul Japar, Norisma Idris, Ting Dang
Date: 2025-04-29 00:00:00
Tasks: cross-modal alignment, Decoder, Image Captioning, Mixture-of-Experts

Medical image reporting (MIR) aims to generate structured clinical descriptions from radiological images. Existing methods struggle with fine-grained feature extraction, multimodal alignment, and generalization across diverse imaging types, often relying on vanilla transformers and focusing primarily on chest X-rays. We propose MicarVLMoE, a vision-language mixture-of-experts model with gated cross-aligned fusion, designed to address these limitations. Our architecture includes: (i) a multiscale vision encoder (MSVE) for capturing anatomical details at varying resolutions, (ii) a multihead dual-branch latent attention (MDLA) module for vision-language alignment through latent bottleneck representations, and (iii) a modulated mixture-of-experts (MoE) decoder for adaptive expert specialization. We extend MIR to CT scans, retinal imaging, MRI scans, and gross pathology images, reporting state-of-the-art results on COVCTR, MMR, PGROSS, and ROCO datasets. Extensive experiments and ablations confirm improved clinical accuracy, cross-modal alignment, and model interpretability. Code is available at https://github.com/AI-14/micar-vl-moe.
High-throughput 3DRA segmentation of brain vasculature and aneurysms using deep learning
Authors: Fengming Lin, Yan Xia, Shuang Song, Nishant Ravikumar, Alejandro F Frangi
Date: 2023-03-01 00:00:00
Proceeding: Computer Methods and Programs in Biomedicine 2023 3
Tasks: Segmentation

Background and Objectives:Automatic segmentation of the cerebral vasculature and aneurysms facilitates incidental detection of aneurysms. The assessment of aneurysm rupture risk assists with pre-operative treatment planning and enables in-silico investigation of cerebral hemodynamics within and in the vicinity of aneurysms. However, ensuring precise and robust segmentation of cerebral vessels and aneurysms in neuroimaging modalities such as three-dimensional rotational angiography (3DRA) is challenging. The vasculature constitutes a small proportion of the image volume, resulting in a large class imbalance (relative to surrounding brain tissue). Additionally, aneurysms and vessels have similar image/appearance characteristics, making it challenging to distinguish the aneurysm sac from the vessel lumen.
Methods:We propose a novel multi-class convolutional neural network to tackle these challenges and facilitate the automatic segmentation of cerebral vessels and aneurysms in 3DRA images. The proposed model is trained and evaluated on an internal multi-center dataset and an external publicly available challenge dataset.
Results:On the internal clinical dataset, our method consistently outperformed several state-of-the-art approaches for vessel and aneurysm segmentation, achieving an average Dice score of 0.81 (0.15 higher than nnUNet) and an average surface-to-surface error of 0.20 mm (less than the in-plane resolution (0.35 mm/pixel)) for aneurysm segmentation; and an average Dice score of 0.91 and average surface-to-surface error of 0.25 mm for vessel segmentation. In 223 cases of a clinical dataset, our method accurately segmented 190 aneurysm cases.
Conclusions:The proposed approach can help address class imbalance problems and inter-class interference problems in multi-class segmentation. Besides, this method performs consistently on clinical datasets from four different sources and the generated results are qualified for hemodynamic simulation. Code available at https://github.com/fmlinks/vessel-aneurysm-segmentation.