pwc-archive/papers-with-abstracts huggingface dataset:
SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models
Authors: Jiawei Zhang, Xuan Yang, Taiqi Wang, Yu Yao, Aleksandr Petiushko, Bo Li
Date: 2025-02-28 00:00:00
Tasks: Attribute, Autonomous Driving, RAG, Retrieval-augmented Generation

Traditional autonomous driving systems often struggle to connect high-level reasoning with low-level control, leading to suboptimal and sometimes unsafe behaviors. Recent advances in multimodal large language models (MLLMs), which process both visual and textual data, offer an opportunity to unify perception and reasoning. However, effectively embedding precise safety knowledge into MLLMs for autonomous driving remains a significant challenge. To address this, we propose SafeAuto, a framework that enhances MLLM-based autonomous driving by incorporating both unstructured and structured knowledge. First, we introduce a Position-Dependent Cross-Entropy (PDCE) loss to improve low-level control signal predictions when values are represented as text. Second, to explicitly integrate safety knowledge, we develop a reasoning component that translates traffic rules into first-order logic (e.g., "red light $\implies$ stop") and embeds them into a probabilistic graphical model (e.g., Markov Logic Network) to verify predicted actions using recognized environmental attributes. Additionally, our Multimodal Retrieval-Augmented Generation (RAG) model leverages video, control signals, and environmental attributes to learn from past driving experiences. Integrating PDCE, MLN, and Multimodal RAG, SafeAuto outperforms existing baselines across multiple datasets, enabling more accurate, reliable, and safer autonomous driving. The code is available at https://github.com/AI-secure/SafeAuto.
Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps
Authors: Lukasz Sztukiewicz, Ignacy Stępka, Michał Wiliński, Jerzy Stefanowski
Date: 2025-02-28 00:00:00
Tasks: Decision Making, Fairness

The widespread adoption of machine learning systems has raised critical concerns about fairness and bias, making mitigating harmful biases essential for AI development. In this paper, we investigate the relationship between debiasing and removing artifacts in neural networks for computer vision tasks. First, we introduce a set of novel XAI-based metrics that analyze saliency maps to assess shifts in a model's decision-making process. Then, we demonstrate that successful debiasing methods systematically redirect model focus away from protected attributes. Finally, we show that techniques originally developed for artifact removal can be effectively repurposed for improving fairness. These findings provide evidence for the existence of a bidirectional connection between ensuring fairness and removing artifacts corresponding to protected attributes.
Decoupling Content and Expression: Two-Dimensional Detection of AI-Generated Text
Authors: Guangsheng Bao, Lihua Rong, Yanbin Zhao, Qiji Zhou, Yue Zhang
Date: 2025-03-01 00:00:00

The wide usage of LLMs raises critical requirements on detecting AI participation in texts. Existing studies investigate these detections in scattered contexts, leaving a systematic and unified approach unexplored. In this paper, we present HART, a hierarchical framework of AI risk levels, each corresponding to a detection task. To address these tasks, we propose a novel 2D Detection Method, decoupling a text into content and language expression. Our findings show that content is resistant to surface-level changes, which can serve as a key feature for detection. Experiments demonstrate that 2D method significantly outperforms existing detectors, achieving an AUROC improvement from 0.705 to 0.849 for level-2 detection and from 0.807 to 0.886 for RAID. We release our data and code at https://github.com/baoguangsheng/truth-mirror.
Input Specific Neural Networks
Authors: Asghar A. Jadoon, D. Thomas Seidl, Reese E. Jones, Jan N. Fuhg
Date: 2025-03-01 00:00:00

The black-box nature of neural networks limits the ability to encode or impose specific structural relationships between inputs and outputs. While various studies have introduced architectures that ensure the network's output adheres to a particular form in relation to certain inputs, the majority of these approaches impose constraints on only a single set of inputs. This paper introduces a novel neural network architecture, termed the Input Specific Neural Network (ISNN), which extends this concept by allowing scalar-valued outputs to be subject to multiple constraints. Specifically, the ISNN can enforce convexity in some inputs, non-decreasing monotonicity combined with convexity with respect to others, and simple non-decreasing monotonicity or arbitrary relationships with additional inputs. The paper presents two distinct ISNN architectures, along with equations for the first and second derivatives of the output with respect to the inputs. These networks are broadly applicable. In this work, we restrict their usage to solving problems in computational mechanics. In particular, we show how they can be effectively applied to fitting data-driven constitutive models. We then embed our trained data-driven constitutive laws into a finite element solver where significant time savings can be achieved by using explicit manual differentiation using the derived equations as opposed to automatic differentiation. We also show how ISNNs can be used to learn structural relationships between inputs and outputs via a binary gating mechanism. Particularly, ISNNs are employed to model an anisotropic free energy potential to get the homogenized macroscopic response in a decoupled multiscale setting, where the network learns whether or not the potential should be modeled as polyconvex, and retains only the relevant layers while using the minimum number of inputs.
Hidden Convexity of Fair PCA and Fast Solver via Eigenvalue Optimization
Authors: Junhui Shen, Aaron J. Davis, Ding Lu, Zhaojun Bai
Date: 2025-03-01 00:00:00
Tasks: Dimensionality Reduction, Fairness

Principal Component Analysis (PCA) is a foundational technique in machine learning for dimensionality reduction of high-dimensional datasets. However, PCA could lead to biased outcomes that disadvantage certain subgroups of the underlying datasets. To address the bias issue, a Fair PCA (FPCA) model was introduced by Samadi et al. (2018) for equalizing the reconstruction loss between subgroups. The semidefinite relaxation (SDR) based approach proposed by Samadi et al. (2018) is computationally expensive even for suboptimal solutions. To improve efficiency, several alternative variants of the FPCA model have been developed. These variants often shift the focus away from equalizing the reconstruction loss. In this paper, we identify a hidden convexity in the FPCA model and introduce an algorithm for convex optimization via eigenvalue optimization. Our approach achieves the desired fairness in reconstruction loss without sacrificing performance. As demonstrated in real-world datasets, the proposed FPCA algorithm runs $8\times$ faster than the SDR-based algorithm, and only at most 85% slower than the standard PCA.
Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM
Authors: Yuxin Yang, Haoyang Wu, Tao Wang, Jia Yang, Hao Ma, Guojie Luo
Date: 2025-03-01 00:00:00
Tasks: Information Retrieval, RAG, Retrieval, Retrieval-augmented Generation

The advent of Large Language Models (LLMs) has revolutionized natural language processing. However, these models face challenges in retrieving precise information from vast datasets. Retrieval-Augmented Generation (RAG) was developed to combining LLMs with external information retrieval systems to enhance the accuracy and context of responses. Despite improvements, RAG still struggles with comprehensive retrieval in high-volume, low-information-density databases and lacks relational awareness, leading to fragmented answers. To address this, this paper introduces the Pseudo-Knowledge Graph (PKG) framework, designed to overcome these limitations by integrating Meta-path Retrieval, In-graph Text and Vector Retrieval into LLMs. By preserving natural language text and leveraging various retrieval techniques, the PKG offers a richer knowledge representation and improves accuracy in information retrieval. Extensive evaluations using Open Compass and MultiHop-RAG benchmarks demonstrate the framework's effectiveness in managing large volumes of data and complex relationships.
Shifting Power: Leveraging LLMs to Simulate Human Aversion in ABMs of Bilateral Financial Exchanges, A bond market study
Authors: Alicia Vidler, Toby Walsh
Date: 2025-03-01 00:00:00
Tasks: Decision Making, Large Language Model, Sensitivity

Bilateral markets, such as those for government bonds, involve decentralized and opaque transactions between market makers (MMs) and clients, posing significant challenges for traditional modeling approaches. To address these complexities, we introduce TRIBE an agent-based model augmented with a large language model (LLM) to simulate human-like decision-making in trading environments. TRIBE leverages publicly available data and stylized facts to capture realistic trading dynamics, integrating human biases like risk aversion and ambiguity sensitivity into the decision-making processes of agents. Our research yields three key contributions: first, we demonstrate that integrating LLMs into agent-based models to enhance client agency is feasible and enriches the simulation of agent behaviors in complex markets; second, we find that even slight trade aversion encoded within the LLM leads to a complete cessation of trading activity, highlighting the sensitivity of market dynamics to agents' risk profiles; third, we show that incorporating human-like variability shifts power dynamics towards clients and can disproportionately affect the entire system, often resulting in systemic agent collapse across simulations. These findings underscore the emergent properties that arise when introducing stochastic, human-like decision processes, revealing new system behaviors that enhance the realism and complexity of artificial societies.
T-REX: A 68-567 μs/token, 0.41-3.95 μJ/token Transformer Accelerator with Reduced External Memory Access and Enhanced Hardware Utilization in 16nm FinFET
Authors: Seunghyun Moon, Mao Li, Gregory Chen, Phil Knag, Ram Krishnamurthy, Mingoo Seok
Date: 2025-03-01 00:00:00

This work introduces novel training and post-training compression schemes to reduce external memory access during transformer model inference. Additionally, a new control flow mechanism, called dynamic batching, and a novel buffer architecture, termed a two-direction accessible register file, further reduce external memory access while improving hardware utilization.
FLStore: Efficient Federated Learning Storage for non-training workloads
Authors: Ahmad Faraz Khan, Samuel Fountain, Ahmed M. Abdelmoniem, Ali R. Butt, Ali Anwar
Date: 2025-03-01 00:00:00
Tasks: Federated Learning, Privacy Preserving, Scheduling

Federated Learning (FL) is an approach for privacy-preserving Machine Learning (ML), enabling model training across multiple clients without centralized data collection. With an aggregator server coordinating training, aggregating model updates, and storing metadata across rounds. In addition to training, a substantial part of FL systems are the non-training workloads such as scheduling, personalization, clustering, debugging, and incentivization. Most existing systems rely on the aggregator to handle non-training workloads and use cloud services for data storage. This results in high latency and increased costs as non-training workloads rely on large volumes of metadata, including weight parameters from client updates, hyperparameters, and aggregated updates across rounds, making the situation even worse. We propose FLStore, a serverless framework for efficient FL non-training workloads and storage. FLStore unifies the data and compute planes on a serverless cache, enabling locality-aware execution via tailored caching policies to reduce latency and costs. Per our evaluations, compared to cloud object store based aggregator server FLStore reduces per request average latency by 71% and costs by 92.45%, with peak improvements of 99.7% and 98.8%, respectively. Compared to an in-memory cloud cache based aggregator server, FLStore reduces average latency by 64.6% and costs by 98.83%, with peak improvements of 98.8% and 99.6%, respectively. FLStore integrates seamlessly with existing FL frameworks with minimal modifications, while also being fault-tolerant and highly scalable.
PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security
Authors: Hajar Kazemi Naeini, Roya Shomali, Abolhassan Pishahang, Hamidreza Hasanzadeh, Mahdieh Mohammadi, Saeed Asadi, Abbas Varmaghani, Ahmad Gholizadeh Lonbar
Date: 2025-03-01 00:00:00
Tasks: Deep Reinforcement Learning, energy management

The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement Learning (DRL) agents trained using data from Digital Twins (DTs) to optimize energy consumption in real time, (2) Physics-Informed Neural Networks (PINNs) to seamlessly embed physical laws within the optimization process, ensuring model accuracy and interpretability, and (3) Blockchain (BC) technology to facilitate secure and transparent communication across the smart grid infrastructure. The model was trained and validated using comprehensive datasets, including smart meter energy consumption data, renewable energy outputs, dynamic pricing, and user preferences collected from IoT devices. The proposed framework achieved superior predictive performance with a Mean Absolute Error (MAE) of 0.237 kWh, Root Mean Square Error (RMSE) of 0.298 kWh, and an R-squared (R2) value of 0.978, indicating a 97.8% explanation of data variance. Classification metrics further demonstrated the model's robustness, achieving 97.7% accuracy, 97.8% precision, 97.6% recall, and an F1 Score of 97.7%. Comparative analysis with traditional models like Linear Regression, Random Forest, SVM, LSTM, and XGBoost revealed the superior accuracy and real-time adaptability of the proposed method. In addition to enhancing energy efficiency, the model reduced energy costs by 35%, maintained a 96% user comfort index, and increased renewable energy utilization to 40%. This study demonstrates the transformative potential of integrating PINNs, DT, and Blockchain technologies to optimize energy consumption in smart grids, paving the way for sustainable, secure, and efficient energy management systems.
Investigating the contribution of terrain-following coordinates and conservation schemes in AI-driven precipitation forecasts
Authors: Yingkai Sha, John S. Schreck, William Chapman, David John Gagne II
Date: 2025-03-01 00:00:00

Artificial Intelligence (AI) weather prediction (AIWP) models often produce "blurry" precipitation forecasts that overestimate drizzle and underestimate extremes. This study provides a novel solution to tackle this problem -- integrating terrain-following coordinates with global mass and energy conservation schemes into AIWP models. Forecast experiments are conducted to evaluate the effectiveness of this solution using FuXi, an example AIWP model, adapted to 1.0-degree grid spacing data. Verification results show large performance gains. The conservation schemes are found to reduce drizzle bias, whereas using terrain-following coordinates improves the estimation of extreme events and precipitation intensity spectra. Furthermore, a case study reveals that terrain-following coordinates capture near-surface winds better over mountains, offering AIWP models more accurate information on understanding the dynamics of precipitation processes. The proposed solution of this study can benefit a wide range of AIWP models and bring insights into how atmospheric domain knowledge can support the development of AIWP models.
CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid
Authors: Smruti P. Dash, Kedar V. Khandeparkar, Nipun Agrawal
Date: 2025-03-01 00:00:00
Tasks: Cyber Attack Detection, Intrusion Detection

The modern power grids are integrated with digital technologies and automation systems. The inclusion of digital technologies has made the smart grids vulnerable to cyber-attacks. Cyberattacks on smart grids can compromise data integrity and jeopardize the reliability of the power supply. Traditional intrusion detection systems often need help to effectively detect novel and sophisticated attacks due to their reliance on labeled training data, which may only encompass part of the spectrum of potential threats. This work proposes a semi-supervised method for cyber-attack detection in smart grids by leveraging the labeled and unlabeled measurement data. We implement consistency regularization and pseudo-labeling to identify deviations from expected behavior and predict the attack classes. We use a curriculum learning approach to improve pseudo-labeling performance, capturing the model uncertainty. We demonstrate the efficiency of the proposed method in detecting different types of cyberattacks, minimizing the false positives by implementing them on publicly available datasets. The method proposes a promising solution by improving the detection accuracy to 99% in the presence of unknown samples and significantly reducing false positives.
AI-Augmented Thyroid Scintigraphy for Robust Classification
Authors: Maziar Sabouri, Ghasem Hajianfar, Alireza Rafiei Sardouei, Milad Yazdani, Azin Asadzadeh, Soroush Bagheri, Mohsen Arabi, Seyed Rasoul Zakavi, Emran Askari, Atena Aghaee, Dena Shahriari, Habib Zaidi, Arman Rahmim
Date: 2025-03-01 00:00:00
Tasks: Classification, Data Augmentation, image-classification, Image Classification, Medical Image Classification, Robust classification

Thyroid scintigraphy is a key imaging modality for diagnosing thyroid disorders. Deep learning models for thyroid scintigraphy classification often face challenges due to limited and imbalanced datasets, leading to suboptimal generalization. In this study, we investigate the effectiveness of different data augmentation techniques including Stable Diffusion (SD), Flow Matching (FM), and Conventional Augmentation (CA) to enhance the performance of a ResNet18 classifier for thyroid condition classification. Our results showed that FM-based augmentation consistently outperforms SD-based approaches, particularly when combined with original (O) data and CA (O+FM+CA), achieving both high accuracy and fair classification across Diffuse Goiter (DG), Nodular Goiter (NG), Normal (NL), and Thyroiditis (TI) cases. The Wilcoxon statistical analysis further validated the superiority of O+FM and its variants (O+FM+CA) over SD-based augmentations in most scenarios. These findings highlight the potential of FM-based augmentation as a superior approach for generating high-quality synthetic thyroid scintigraphy images and improving model generalization in medical image classification.
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning
Authors: Yugu Li, Zehong Cao, Jianglin Qiao, Siyi Hu
Date: 2025-03-01 00:00:00
Tasks: Multi-agent Reinforcement Learning, Q-Learning, Starcraft

In cooperative multi-agent reinforcement learning (MARL), agents typically form a single grand coalition based on credit assignment to tackle a composite task, often resulting in suboptimal performance. This paper proposed a nucleolus-based credit assignment grounded in cooperative game theory, enabling the autonomous partitioning of agents into multiple small coalitions that can effectively identify and complete subtasks within a larger composite task. Specifically, our designed nucleolus Q-learning could assign fair credits to each agent, and the nucleolus Q-operator provides theoretical guarantees with interpretability for both learning convergence and the stability of the formed small coalitions. Through experiments on Predator-Prey and StarCraft scenarios across varying difficulty levels, our approach demonstrated the emergence of multiple effective coalitions during MARL training, leading to faster learning and superior performance in terms of win rate and cumulative rewards especially in hard and super-hard environments, compared to four baseline methods. Our nucleolus-based credit assignment showed the promise for complex composite tasks requiring effective subteams of agents.
Conditioning on Local Statistics for Scalable Heterogeneous Federated Learning
Authors: Rickard Brännvall
Date: 2025-03-01 00:00:00
Tasks: Federated Learning

Federated learning is a distributed machine learning approach where multiple clients collaboratively train a model without sharing their local data, which contributes to preserving privacy. A challenge in federated learning is managing heterogeneous data distributions across clients, which can hinder model convergence and performance due to the need for the global model to generalize well across diverse local datasets. We propose to use local characteristic statistics, by which we mean some statistical properties calculated independently by each client using only their local training dataset. These statistics, such as means, covariances, and higher moments, are used to capture the characteristics of the local data distribution. They are not shared with other clients or a central node. During training, these local statistics help the model learn how to condition on the local data distribution, and during inference, they guide the client's predictions. Our experiments show that this approach allows for efficient handling of heterogeneous data across the federation, has favorable scaling compared to approaches that directly try to identify peer nodes that share distribution characteristics, and maintains privacy as no additional information needs to be communicated.
Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems
Authors: Song Xia, Yi Yu, Wenhan Yang, Meiwen Ding, Zhuo Chen, Ling-Yu Duan, Alex C. Kot, Xudong Jiang
Date: 2025-03-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Collaborative Inference, Representation Learning

By locally encoding raw data into intermediate features, collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data to cloud servers. However, recent studies have revealed that these intermediate features may not sufficiently preserve privacy, as information can be leaked and raw data can be reconstructed via model inversion attacks (MIAs). Obfuscation-based methods, such as noise corruption, adversarial representation learning, and information filters, enhance the inversion robustness by obfuscating the task-irrelevant redundancy empirically. However, methods for quantifying such redundancy remain elusive, and the explicit mathematical relation between this redundancy minimization and inversion robustness enhancement has not yet been established. To address that, this work first theoretically proves that the conditional entropy of inputs given intermediate features provides a guaranteed lower bound on the reconstruction mean square error (MSE) under any MIA. Then, we derive a differentiable and solvable measure for bounding this conditional entropy based on the Gaussian mixture estimation and propose a conditional entropy maximization (CEM) algorithm to enhance the inversion robustness. Experimental results on four datasets demonstrate the effectiveness and adaptability of our proposed CEM; without compromising feature utility and computing efficiency, plugging the proposed CEM into obfuscation-based defense mechanisms consistently boosts their inversion robustness, achieving average gains ranging from 12.9\% to 48.2\%. Code is available at \href{https://github.com/xiasong0501/CEM}{https://github.com/xiasong0501/CEM}.