pwc-archive/papers-with-abstracts huggingface dataset:
Retrieval-Confused Generation is a Good Defender for Privacy Violation Attack of Large Language Models
Authors: Wanli Peng, Xin Chen, Hang Fu, Xinyu He, Xue Yiming, Juan Wen
Date: 2025-06-24 00:00:00
Tasks: Retrieval

Recent advances in large language models (LLMs) have made a profound impact on our society and also raised new security concerns. Particularly, due to the remarkable inference ability of LLMs, the privacy violation attack (PVA), revealed by Staab et al., introduces serious personal privacy issues. Existing defense methods mainly leverage LLMs to anonymize the input query, which requires costly inference time and cannot gain satisfactory defense performance. Moreover, directly rejecting the PVA query seems like an effective defense method, while the defense method is exposed, promoting the evolution of PVA. In this paper, we propose a novel defense paradigm based on retrieval-confused generation (RCG) of LLMs, which can efficiently and covertly defend the PVA. We first design a paraphrasing prompt to induce the LLM to rewrite the "user comments" of the attack query to construct a disturbed database. Then, we propose the most irrelevant retrieval strategy to retrieve the desired user data from the disturbed database. Finally, the "data comments" are replaced with the retrieved user data to form a defended query, leading to responding to the adversary with some wrong personal attributes, i.e., the attack fails. Extensive experiments are conducted on two datasets and eight popular LLMs to comprehensively evaluate the feasibility and the superiority of the proposed defense method.
Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction
Authors: Ziru Zhang, Jiadong Yu, Danny H. K. Tsang
Date: 2025-06-24 00:00:00
Tasks: Causal Inference, CPU, Fairness, Reinforcement Learning (RL)

The optimization of quality of experience (QoE) in multi-user virtual reality (VR) interactions demands a delicate balance between ultra-low latency, high-fidelity motion synchronization, and equitable resource allocation. While adaptive keyframe extraction mitigates transmission overhead, existing approaches often overlook the causal relationships among allocated bandwidth, CPU frequency, and user perception, limiting QoE gains. This paper proposes an intelligent framework to maximize QoE by integrating adaptive keyframe extraction with causal-aware reinforcement learning (RL). First, a novel QoE metric is formulated using the Weber-Fechner Law, combining perceptual sensitivity, attention-driven priorities, and motion reconstruction accuracy. The QoE optimization problem is then modeled as a mixed integer programming (MIP) task, jointly optimizing keyframe ratios, bandwidth, and computational resources under horizon-fairness constraints. We propose Partial State Causal Deep Deterministic Policy Gradient (PS-CDDPG), which integrates the Deep Deterministic Policy Gradient (DDPG) method with causal influence detection. By leveraging causal information regarding how QoE is influenced and determined by various actions, we explore actions guided by weights calculated from causal inference (CI), which in turn improves training efficiency. Experiments conducted with the CMU Motion Capture Database demonstrate that our framework significantly reduces interactive latency, enhances QoE, and maintains fairness, achieving superior performance compared to benchmark methods.
Orthogonal Soft Pruning for Efficient Class Unlearning
Authors: Qinghui Gong, Xue Yang, Xiaohu Tang
Date: 2025-06-24 00:00:00
Tasks: Inference Attack, Machine Unlearning, Membership Inference Attack

Machine unlearning aims to selectively remove class-specific knowledge from pretrained neural networks to satisfy privacy regulations such as the GDPR. Existing methods typically face a trade-off between unlearning speed and preservation of predictive accuracy, often incurring either high computational overhead or significant performance degradation on retained classes. In this paper, we propose a novel class-aware soft pruning framework leveraging orthogonal convolutional kernel regularization to achieve rapid and precise forgetting with millisecond-level response times. By enforcing orthogonality constraints during training, our method decorrelates convolutional filters and disentangles feature representations, while efficiently identifying class-specific channels through activation difference analysis. Extensive evaluations across multiple architectures and datasets demonstrate stable pruning with near-instant execution, complete forgetting of targeted classes, and minimal accuracy loss on retained data. Experiments on CIFAR-10, CIFAR-100, and TinyImageNet confirm that our approach substantially reduces membership inference attack risks and accelerates unlearning by orders of magnitude compared to state-of-the-art baselines. This framework provides an efficient, practical solution for real-time machine unlearning in Machine Learning as a Service (MLaaS) scenarios.
RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
Authors: Isaac Marroqui Penalva, Enrique Tomás Martínez Beltrán, Manuel Gil Pérez, Alberto Huertas Celdrán
Date: 2025-06-24 00:00:00
Tasks: Federated Learning, Model Poisoning

Decentralized Federated Learning (DFL) enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects peers for model aggregation. Malicious nodes may exploit this autonomy by sending corrupted models (model poisoning), delaying model submissions (delay attack), or flooding the network with excessive messages, negatively affecting system performance. Existing solutions often depend on rigid configurations or additional infrastructures such as blockchain, leading to computational overhead, scalability issues, or limited adaptability. To overcome these limitations, this paper proposes RepuNet, a decentralized reputation system that categorizes threats in DFL and dynamically evaluates node behavior using metrics like model similarity, parameter changes, message latency, and communication volume. Nodes' influence in model aggregation is adjusted based on their reputation scores. RepuNet was integrated into the Nebula DFL platform and experimentally evaluated with MNIST and CIFAR-10 datasets under non-IID distributions, using federations of up to 25 nodes in both fully connected and random topologies. Different attack intensities, frequencies, and activation intervals were tested. Results demonstrated that RepuNet effectively detects and mitigates malicious behavior, achieving F1 scores above 95% for MNIST scenarios and approximately 76% for CIFAR-10 cases. These outcomes highlight RepuNet's adaptability, robustness, and practical potential for mitigating threats in decentralized federated learning environments.
Distillation-Enabled Knowledge Alignment for Generative Semantic Communications in AIGC Provisioning Tasks
Authors: Jingzhi Hu, Geoffrey Ye Li
Date: 2025-06-24 00:00:00
Tasks: Knowledge Distillation, Semantic Communication

Due to the surging amount of AI-generated content (AIGC), its provisioning to edges and mobile users from the cloud incurs substantial traffic on networks. Generative semantic communication (GSC) offers a promising solution by transmitting highly compact information, i.e., prompt text and latent representations, instead of high-dimensional AIGC data. However, GSC relies on the alignment between the knowledge in the cloud generative AI (GAI) and that possessed by the edges and users, and between the knowledge for wireless transmission and that of actual channels, which remains challenging. In this paper, we propose DeKA-g, a distillation-enabled knowledge alignment algorithm for GSC systems. The core idea is to distill the generation knowledge from the cloud-GAI into low-rank matrices, which can be incorporated by the edge and used to adapt the transmission knowledge to diverse wireless channel conditions. DeKA-g comprises two novel methods: metaword-aided knowledge distillation (MAKD) and variable-rate grouped SNR adaptation (VGSA). For MAKD, an optimized metaword is employed to enhance the efficiency of knowledge distillation, while VGSA enables efficient adaptation to diverse compression rates and SNR ranges. From simulation results, DeKA-g improves the alignment between the edge-generated images and the cloud-generated ones by 44%. Moreover, it adapts to compression rates with 116% higher efficiency than the baseline and enhances the performance in low-SNR conditions by 28%.
Explaining deep neural network models for electricity price forecasting with XAI
Authors: Antoine Pesenti, Aidan OSullivan
Date: 2025-06-24 00:00:00
Tasks: Novel Concepts

Electricity markets are highly complex, involving lots of interactions and complex dependencies that make it hard to understand the inner workings of the market and what is driving prices. Econometric methods have been developed for this, white-box models, however, they are not as powerful as deep neural network models (DNN). In this paper, we use a DNN to forecast the price and then use XAI methods to understand the factors driving the price dynamics in the market. The objective is to increase our understanding of how different electricity markets work. To do that, we apply explainable methods such as SHAP and Gradient, combined with visual techniques like heatmaps (saliency maps) to analyse the behaviour and contributions of various features across five electricity markets. We introduce the novel concepts of SSHAP values and SSHAP lines to enhance the complex representation of high-dimensional tabular models.
A Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers
Authors: Miguel N. Font, José L. Jorro-Aragoneses, Carlos M. Alaíz
Date: 2025-06-24 00:00:00
Tasks: Autonomous Driving, Medical Diagnosis, Uncertainty Quantification

Neural Networks have high accuracy in solving problems where it is difficult to detect patterns or create a logical model. However, these algorithms sometimes return wrong solutions, which become problematic in high-risk domains like medical diagnosis or autonomous driving. One strategy to detect and mitigate these errors is the measurement of the uncertainty over neural network decisions. In this paper, we present a novel post-hoc framework for measuring the uncertainty of a decision based on retrieved training cases that have a similar activation vector to the query for each layer. Based on these retrieved cases, we propose two new metrics: Decision Change and Layer Uncertainty, which capture changes in nearest-neighbor class distributions across layers. We evaluated our approach in a classification model for two datasets: CIFAR-10 and MNIST. The results show that these metrics enhance uncertainty estimation, especially in challenging classification tasks, outperforming softmax-based confidence.
Can LLMs Replace Humans During Code Chunking?
Authors: Christopher Glasz, Emily Escamilla, Eric O. Scott, Anand Patel, Jacob Zimmer, Colin Diggs, Michael Doyle, Scott Rosen, Nitin Naik, Justin F. Brunelle, Samruddhi Thaker, Parthav Poudel, Arun Sridharan, Amit Madan, Doug Wendt, William Macke, Thomas Schill
Date: 2025-06-24 00:00:00
Tasks: Chunking

Large language models (LLMs) have become essential tools in computer science, especially for tasks involving code understanding and generation. However, existing work does not address many of the unique challenges presented by code written for government applications. In particular, government enterprise software is often written in legacy languages like MUMPS or assembly language code (ALC) and the overall token lengths of these systems exceed the context window size for current commercially available LLMs. Additionally, LLMs are primarily trained on modern software languages and have undergone limited testing with legacy languages, making their ability to understand legacy languages unknown and, hence, an area for empirical study. This paper examines the application of LLMs in the modernization of legacy government code written in ALC and MUMPS, addressing the challenges of input limitations. We investigate various code-chunking methods to optimize the generation of summary module comments for legacy code files, evaluating the impact of code-chunking methods on the quality of documentation produced by different LLMs, including GPT-4o, Claude 3 Sonnet, Mixtral, and Llama 3. Our results indicate that LLMs can select partition points closely aligned with human expert partitioning. We also find that chunking approaches have significant impact on downstream tasks such as documentation generation. LLM-created partitions produce comments that are up to 20% more factual and up to 10% more useful than when humans create partitions. Therefore, we conclude that LLMs can be used as suitable replacements for human partitioning of large codebases during LLM-aided modernization.
CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation
Authors: Deepon Halder, Thanmay Jayakumar, Raj Dabre
Date: 2025-06-24 00:00:00
Tasks: Machine Translation, Translation

Large language models (LLMs), despite their ability to perform few-shot machine translation (MT), often lag behind dedicated MT systems trained on parallel corpora, which are crucial for high quality machine translation (MT). However, parallel corpora are often scarce or non-existent for low-resource languages. In this paper, we propose CycleDistill, a bootstrapping approach leveraging LLMs and few-shot translation to obtain high-quality MT systems. CycleDistill involves iteratively generating synthetic parallel corpora from monolingual corpora via zero- or few-shot MT, which is then used to fine-tune the model that was used for generating said data for MT. CycleDistill does not need parallel corpora beyond 1 to 4 few-shot examples, and in our experiments focusing on three Indian languages, by relying solely on monolingual corpora, it can achieve high-quality machine translation, improving upon a few-shot baseline model by over 20-30 chrF points on average in the first iteration. We also study the effect of leveraging softmax activations during the distillation process and observe mild improvements in translation quality.
An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
Authors: Adam Foster, Zeno Schätzle, P. Bernát Szabó, Lixue Cheng, Jonas Köhler, Gino Cassella, Nicholas Gao, Jiawei Li, Frank Noé, Jan Hermann
Date: 2025-06-24 00:00:00

Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireferential character of the electronic structure in dissociating species. Multireferential methods in particular suffer from large computational cost, which under the normal paradigm has to be paid anew for each system at a full price, ignoring commonalities in electronic structure across molecules. Quantum Monte Carlo with deep neural networks (deep QMC) uniquely offers to exploit such commonalities by pretraining transferable wavefunction models, but all such attempts were so far limited in scope. Here, we bring this new paradigm to fruition with Orbformer, a novel transferable wavefunction model pretrained on 22,000 equilibrium and dissociating structures that can be fine-tuned on unseen molecules reaching an accuracy-cost ratio rivalling classical multireferential methods. On established benchmarks as well as more challenging bond dissociations and Diels-Alder reactions, Orbformer is the only method that consistently converges to chemical accuracy (1 kcal/mol). This work turns the idea of amortizing the cost of solving the Schr\"odinger equation over many molecules into a practical approach in quantum chemistry.
Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs
Authors: Travis Thompson, Seung-Hwan Lim, Paul Liu, Ruoying He, Dongkuan Xu
Date: 2025-06-24 00:00:00
Tasks: Information Retrieval, Knowledge Graphs, Multi-hop Question Answering, Question Answering, RAG, Retrieval-augmented Generation

Large Language Models (LLMs) have achieved impressive capabilities in language understanding and generation, yet they continue to underperform on knowledge-intensive reasoning tasks due to limited access to structured context and multi-hop information. Retrieval-Augmented Generation (RAG) partially mitigates this by grounding generation in retrieved context, but conventional RAG and GraphRAG methods often fail to capture relational structure across nodes in knowledge graphs. We introduce Inference-Scaled GraphRAG, a novel framework that enhances LLM-based graph reasoning by applying inference-time compute scaling. Our method combines sequential scaling with deep chain-of-thought graph traversal, and parallel scaling with majority voting over sampled trajectories within an interleaved reasoning-execution loop. Experiments on the GRBench benchmark demonstrate that our approach significantly improves multi-hop question answering performance, achieving substantial gains over both traditional GraphRAG and prior graph traversal baselines. These findings suggest that inference-time scaling is a practical and architecture-agnostic solution for structured knowledge reasoning with LLMs
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies
Authors: Vojtěch Novák, Ivan Zelinka, Lenka Přibylová, Lubomír Martínek
Date: 2025-06-24 00:00:00
Tasks: Causal Inference, Selection bias, Survival Analysis

This study investigates the application of quantum neural networks (QNNs) for propensity score estimation to address selection bias in comparing survival outcomes between laparoscopic and open surgical techniques in a cohort of 1177 colorectal carcinoma patients treated at University Hospital Ostrava (2001-2009). Using a dataset with 77 variables, including patient demographics and tumor characteristics, we developed QNN-based propensity score models focusing on four key covariates (Age, Sex, Stage, BMI). The QNN architecture employed a linear ZFeatureMap for data encoding, a SummedPaulis operator for predictions, and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for robust, gradient-free optimization in noisy quantum environments. Variance regularization was integrated to mitigate quantum measurement noise, with simulations conducted under exact, sampling (1024 shots), and noisy hardware (FakeManhattanV2) conditions. QNNs, particularly with simulated hardware noise, outperformed classical logistic regression and gradient boosted machines in small samples (AUC up to 0.750 for n=100), with noise modeling enhancing predictive stability. Propensity score matching and weighting, optimized via genetic matching and matching weights, achieved covariate balance with standardized mean differences of 0.0849 and 0.0869, respectively. Survival analyses using Kaplan-Meier estimation, Cox proportional hazards, and Aalen additive regression revealed no significant survival differences post-adjustment (p-values 0.287-0.851), indicating confounding bias in unadjusted outcomes. These results highlight QNNs' potential, enhanced by CMA-ES and noise-aware strategies, to improve causal inference in biomedical research, particularly for small-sample, high-dimensional datasets.
VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT Registration
Authors: Hang Zhang, Yuxi Zhang, Jiazheng Wang, Xiang Chen, Renjiu Hu, Xin Tian, Gaolei Li, Min Liu
Date: 2025-06-24 00:00:00
Tasks: Contrastive Learning, Image Registration

Recent developments in neural networks have improved deformable image registration (DIR) by amortizing iterative optimization, enabling fast and accurate DIR results. However, learning-based methods often face challenges with limited training data, large deformations, and tend to underperform compared to iterative approaches when label supervision is unavailable. While iterative methods can achieve higher accuracy in such scenarios, they are considerably slower than learning-based methods. To address these limitations, we propose VoxelOpt, a discrete optimization-based DIR framework that combines the strengths of learning-based and iterative methods to achieve a better balance between registration accuracy and runtime. VoxelOpt uses displacement entropy from local cost volumes to measure displacement signal strength at each voxel, which differs from earlier approaches in three key aspects. First, it introduces voxel-wise adaptive message passing, where voxels with lower entropy receives less influence from their neighbors. Second, it employs a multi-level image pyramid with 27-neighbor cost volumes at each level, avoiding exponential complexity growth. Third, it replaces hand-crafted features or contrastive learning with a pretrained foundational segmentation model for feature extraction. In abdominal CT registration, these changes allow VoxelOpt to outperform leading iterative in both efficiency and accuracy, while matching state-of-the-art learning-based methods trained with label supervision. The source code will be available at https://github.com/tinymilky/VoxelOpt
HERCULES: Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization
Authors: Gabor Petnehazi, Bernadett Aradi
Date: 2025-06-24 00:00:00
Tasks: Clustering

The explosive growth of complex datasets across various modalities necessitates advanced analytical tools that not only group data effectively but also provide human-understandable insights into the discovered structures. We introduce HERCULES (Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization), a novel algorithm and Python package designed for hierarchical k-means clustering of diverse data types, including text, images, and numeric data (processed one modality per run). HERCULES constructs a cluster hierarchy by recursively applying k-means clustering, starting from individual data points at level 0. A key innovation is its deep integration of Large Language Models (LLMs) to generate semantically rich titles and descriptions for clusters at each level of the hierarchy, significantly enhancing interpretability. The algorithm supports two main representation modes: `direct' mode, which clusters based on original data embeddings or scaled numeric features, and `description' mode, which clusters based on embeddings derived from LLM-generated summaries. Users can provide a `topic\_seed' to guide LLM-generated summaries towards specific themes. An interactive visualization tool facilitates thorough analysis and understanding of the clustering results. We demonstrate HERCULES's capabilities and discuss its potential for extracting meaningful, hierarchical knowledge from complex datasets.