pwc-archive/papers-with-abstracts huggingface dataset:
Private GPTs for LLM-driven testing in software development and machine learning
Authors: Jakub Jagielski, Markus Abel
Date: 2025-06-06 00:00:00

In this contribution, we examine the capability of private GPTs to automatically generate executable test code based on requirements. More specifically, we use acceptance criteria as input, formulated as part of epics, or stories, which are typically used in modern development processes. This gives product owners, or business intelligence, respectively, a way to directly produce testable criteria through the use of LLMs. We explore the quality of the so-produced tests in two ways: i) directly by letting the LLM generate code from requirements, ii) through an intermediate step using Gherkin syntax. As a result, it turns out that the two-step procedure yields better results -where we define better in terms of human readability and best coding practices, i.e. lines of code and use of additional libraries typically used in testing. Concretely, we evaluate prompt effectiveness across two scenarios: a simple "Hello World" program and a digit classification model, showing that structured prompts lead to higher-quality test outputs.
DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Authors: Jingyu Xiao, Ming Wang, Man Ho Lam, Yuxuan Wan, Junliang Liu, Yintong Huo, Michael R. Lyu
Date: 2025-06-06 00:00:00
Tasks: Code Generation

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in automated front-end engineering, e.g., generating UI code from visual designs. However, existing front-end UI code generation benchmarks have the following limitations: (1) While framework-based development becomes predominant in modern front-end programming, current benchmarks fail to incorporate mainstream development frameworks. (2) Existing evaluations focus solely on the UI code generation task, whereas practical UI development involves several iterations, including refining editing, and repairing issues. (3) Current benchmarks employ unidimensional evaluation, lacking investigation into influencing factors like task difficulty, input context variations, and in-depth code-level analysis. To bridge these gaps, we introduce DesignBench, a multi-framework, multi-task evaluation benchmark for assessing MLLMs' capabilities in automated front-end engineering. DesignBench encompasses three widely-used UI frameworks (React, Vue, and Angular) alongside vanilla HTML/CSS, and evaluates on three essential front-end tasks (generation, edit, and repair) in real-world development workflows. DesignBench contains 900 webpage samples spanning over 11 topics, 9 edit types, and 6 issue categories, enabling detailed analysis of MLLM performance across multiple dimensions. Our systematic evaluation reveals critical insights into MLLMs' framework-specific limitations, task-related bottlenecks, and performance variations under different conditions, providing guidance for future research in automated front-end development. Our code and data are available at https://github.com/WebPAI/DesignBench.
Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Authors: Stefan Dascalescu, Adrian Marius Dumitran, Mihai Alexandru Vasiluta
Date: 2025-06-06 00:00:00

Competitive programming contests play a crucial role in cultivating computational thinking and algorithmic skills among learners. However, generating comprehensive test cases to effectively assess programming solutions remains resource-intensive and challenging for educators. This paper introduces an innovative NLP-driven method leveraging generative AI (large language models) to automate the creation of high-quality test cases for competitive programming assessments. We extensively evaluated our approach on diverse datasets, including 25 years of Romanian Informatics Olympiad (OJI) data for 5th graders, recent competitions hosted on the Kilonova.ro platform, and the International Informatics Olympiad in Teams (IIOT). Our results demonstrate that AI-generated test cases substantially enhanced assessments, notably identifying previously undetected errors in 67% of the OJI 5th grade programming problems. These improvements underscore the complementary educational value of our technique in formative assessment contexts. By openly sharing our prompts, translated datasets, and methodologies, we offer practical NLP-based tools that educators and contest organizers can readily integrate to enhance assessment quality, reduce workload, and deepen insights into learner performance.
Deployability-Centric Infrastructure-as-Code Generation: An LLM-based Iterative Framework
Authors: Tianyi Zhang, Shidong Pan, Zejun Zhang, Zhenchang Xing, Xiaoyu Sun
Date: 2025-06-05 00:00:00
Tasks: Code Generation

Infrastructure-as-Code (IaC) generation holds significant promise for automating cloud infrastructure provisioning. Recent advances in Large Language Models (LLMs) present a promising opportunity to democratize IaC development by generating deployable infrastructure templates from natural language descriptions, but current evaluation focuses on syntactic correctness while ignoring deployability, the fatal measure of IaC template utility. We address this gap through two contributions: (1) IaCGen, an LLM-based deployability-centric framework that uses iterative feedback mechanism to generate IaC templates, and (2) DPIaC-Eval, a deployability-centric IaC template benchmark consists of 153 real-world scenarios that can evaluate syntax, deployment, user intent, and security. Our evaluation reveals that state-of-the-art LLMs initially performed poorly, with Claude-3.5 and Claude-3.7 achieving only 30.2% and 26.8% deployment success on the first attempt respectively. However, IaCGen transforms this performance dramatically: all evaluated models reach over 90% passItr@25, with Claude-3.5 and Claude-3.7 achieving 98% success rate. Despite these improvements, critical challenges remain in user intent alignment (25.2% accuracy) and security compliance (8.4% pass rate), highlighting areas requiring continued research. Our work provides the first comprehensive assessment of deployability-centric IaC template generation and establishes a foundation for future research.
Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety
Authors: Seongmin Lee, Aeree Cho, Grace C. Kim, Shengyun Peng, Mansi Phute, Duen Horng Chau
Date: 2025-06-05 00:00:00
Tasks: Navigate, Survey

As large language models (LLMs) see wider real-world use, understanding and mitigating their unsafe behaviors is critical. Interpretation techniques can reveal causes of unsafe outputs and guide safety, but such connections with safety are often overlooked in prior surveys. We present the first survey that bridges this gap, introducing a unified framework that connects safety-focused interpretation methods, the safety enhancements they inform, and the tools that operationalize them. Our novel taxonomy, organized by LLM workflow stages, summarizes nearly 70 works at their intersections. We conclude with open challenges and future directions. This timely survey helps researchers and practitioners navigate key advancements for safer, more interpretable LLMs.
Alice and the Caterpillar: A more descriptive null model for assessing data mining results
Authors: Giulia Preti, Gianmarco De Francisci Morales, Matteo Riondato
Date: 2025-06-11 00:00:00
Tasks: Descriptive

We introduce novel null models for assessing the results obtained from observed binary transactional and sequence datasets, using statistical hypothesis testing. Our null models maintain more properties of the observed dataset than existing ones. Specifically, they preserve the Bipartite Joint Degree Matrix of the bipartite (multi-)graph corresponding to the dataset, which ensures that the number of caterpillars, i.e., paths of length three, is preserved, in addition to other properties considered by other models. We describe Alice, a suite of Markov chain Monte Carlo algorithms for sampling datasets from our null models, based on a carefully defined set of states and efficient operations to move between them. The results of our experimental evaluation show that Alice mixes fast and scales well, and that our null model finds different significant results than ones previously considered in the literature.
Graph signal aware decomposition of dynamic networks via latent graphs
Authors: Bishwadeep Das, Andrei Buciulea, Antonio G. Marques, Elvin Isufi
Date: 2025-06-10 00:00:00
Tasks: Tensor Decomposition

Dynamics on and of networks refer to changes in topology and node-associated signals, respectively and are pervasive in many socio-technological systems, including social, biological, and infrastructure networks. Due to practical constraints, privacy concerns, or malfunctions, we often observe only a fraction of the topological evolution and associated signal, which not only hinders downstream tasks but also restricts our analysis of network evolution. Such aspects could be mitigated by moving our attention at the underlying latent driving factors of the network evolution, which can be naturally uncovered via low-rank tensor decomposition. Tensor-based methods provide a powerful means of uncovering the underlying factors of network evolution through low-rank decompositions. However, the extracted embeddings typically lack a relational structure and are obtained independently from the node signals. This disconnect reduces the interpretability of the embeddings and overlooks the coupling between topology and signals. To address these limitations, we propose a novel two-way decomposition to represent a dynamic graph topology, where the structural evolution is captured by a linear combination of latent graph adjacency matrices reflecting the overall joint evolution of both the topology and the signal. Using spatio-temporal data, we estimate the latent adjacency matrices and their temporal scaling signatures via alternating minimization, and prove that our approach converges to a stationary point. Numerical results show that the proposed method recovers individually and collectively expressive latent graphs, outperforming both standard tensor-based decompositions and signal-based topology identification methods in reconstructing the missing network especially when observations are limited.
PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels
Authors: Peyman Rostami, Vahid Rahimzadeh, Ali Adibi, Azadeh Shakery
Date: 2025-06-09 00:00:00
Tasks: Information Retrieval, Stance Detection

Stance detection identifies the viewpoint expressed in text toward a specific target, such as a political figure. While previous datasets have focused primarily on tweet-level stances from established platforms, user-level stance resources, especially on emerging platforms like Bluesky remain scarce. User-level stance detection provides a more holistic view by considering a user's complete posting history rather than isolated posts. We present the first stance detection dataset for the 2024 U.S. presidential election, collected from Bluesky and centered on Kamala Harris and Donald Trump. The dataset comprises 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories. PolitiSky24 was created using a carefully evaluated pipeline combining advanced information retrieval and large language models, which generates stance labels with supporting rationales and text spans for transparency. The labeling approach achieves 81\% accuracy with scalable LLMs. This resource addresses gaps in political stance analysis through its timeliness, open-data nature, and user-level perspective. The dataset is available at https://doi.org/10.5281/zenodo.15616911
Fast Geometric Embedding for Node Influence Maximization
Authors: Alexander Kolpakov, Igor Rivin
Date: 2025-06-09 00:00:00

Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs. In this work, we introduce an efficient force layout algorithm that embeds a graph into a low-dimensional space, where the radial distance from the origin serves as a proxy for various centrality measures. We evaluate our method on multiple graph families and demonstrate strong correlations with degree, PageRank, and paths-based centralities. As an application, it turns out that the proposed embedding allows to find high-influence nodes in a network, and provides a fast and scalable alternative to the standard greedy algorithm.
Masked Language Models are Good Heterogeneous Graph Generalizers
Authors: Jinyu Yang, Cheng Yang, Shanyuan Cui, Zeyuan Guo, Liangwei Yang, Muhan Zhang, Chuan Shi
Date: 2025-06-06 00:00:00
Tasks: Graph Learning, Language Modeling, Language Modelling, Masked Language Modeling

Heterogeneous graph neural networks (HGNNs) excel at capturing structural and semantic information in heterogeneous graphs (HGs), while struggling to generalize across domains and tasks. Recently, some researchers have turned to integrating HGNNs with large language models (LLMs) for more generalizable heterogeneous graph learning. However, these approaches typically extract structural information via HGNNs as HG tokens, and disparities in embedding spaces between HGNNs and LLMs have been shown to bias the LLM's comprehension of HGs. Moreover, as these HG tokens are often derived from node-level tasks, the model's ability to generalize across tasks remains limited. To this end, we propose a simple yet effective Masked Language Modeling-based method, called MLM4HG. MLM4HG introduces metapath-based textual sequences instead of HG tokens to extract structural and semantic information inherent in HGs, and designs customized textual templates to unify different graph tasks into a coherent cloze-style "mask" token prediction paradigm. Specifically, MLM4HG first converts HGs from various domains to texts based on metapaths, and subsequently combines them with the unified task texts to form a HG-based corpus. Moreover, the corpus is fed into a pretrained LM for fine-tuning with a constrained target vocabulary, enabling the fine-tuned LM to generalize to unseen target HGs. Extensive cross-domain and multi-task experiments on four real-world datasets demonstrate the superior generalization performance of MLM4HG over state-of-the-art methods in both few-shot and zero-shot scenarios. Our code is available at https://github.com/BUPT-GAMMA/MLM4HG.
Combating Misinformation in the Arab World: Challenges & Opportunities
Authors: Azza Abouzied, Firoj Alam, Raian Ali, Paolo Papotti
Date: 2025-06-05 00:00:00
Tasks: Diversity, Fact Checking, Misinformation

Misinformation and disinformation pose significant risks globally, with the Arab region facing unique vulnerabilities due to geopolitical instabilities, linguistic diversity, and cultural nuances. We explore these challenges through the key facets of combating misinformation: detection, tracking, mitigation and community-engagement. We shed light on how connecting with grass-roots fact-checking organizations, understanding cultural norms, promoting social correction, and creating strong collaborative information networks can create opportunities for a more resilient information ecosystem in the Arab world.
Data-Driven Model Reduction by Moment Matching for Linear and Nonlinear Parametric Systems
Authors: Hanqing Zhang, Junyu Mao, Mohammad Fahim Shakib, Giordano Scarciotti
Date: 2025-06-12 00:00:00

Theory and methods to obtain parametric reduced-order models by moment matching are presented. The definition of the parametric moment is introduced, and methods (model-based and data-driven) for the approximation of the parametric moment of linear and nonlinear parametric systems are proposed. These approximations are exploited to construct families of parametric reduced-order models that match the approximate parametric moment of the system to be reduced and preserve key system properties such as asymptotic stability and dissipativity. The use of the model reduction methods is illustrated by means of a parametric benchmark model for the linear case and a large-scale wind farm model for the nonlinear case. In the illustration, a comparison of the proposed approximation methods is drawn and their advantages/disadvantages are discussed.
General Reference Frame Identification and Transformation in Unbalanced Power Systems
Authors: Francisco G. Montoya, Santiago Sánchez Acevedo
Date: 2025-06-12 00:00:00
Tasks: valid

Various domains such as power system stability analysis, electric machine modeling, and control of power electronic converters have significantly benefited from the application of coordinate transformations. One of the main benefits is the dimensional reduction, which reduces the complexity of the problems. This paper introduces a novel general transformation based on a geometric framework that directly identifies the plane containing the locus for unbalanced quantities through bivector analysis using Geometric Algebra. The proposed method provides a direct transformation valid for any degree of unbalance in $n$-phase, $(n+1)$-wire sinusoidal systems. The transformation requires only two measurements (voltage or current) taken at different time instants, making it computationally efficient. Moreover, we demonstrate through pure geometric reasoning that our approach is general and encompasses other techniques, such as the classical Clarke transformation. Numerical simulations and experimental validation using a real-time digital simulator and a physical laboratory setup demonstrate the effectiveness of the proposed method. This generalization to multi-dimensional systems, combined with the reduced measurement requirements, represents a significant advancement over existing approaches that are typically restricted to three-phase applications or suffer from computational limitations.
A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation
Authors: Sebastián Rojas-Innocenti, Enrique Baeyens, Alejandro Martín-Crespo, Sergio Saludes-Rodil, Fernando Frechoso Escudero
Date: 2025-06-12 00:00:00
Tasks: Scheduling

This paper presents a scenario based robust optimization framework for short term energy scheduling in electricity intensive industrial plants, explicitly addressing uncertainty in planning decisions. The model is formulated as a two-stage Mixed Integer Linear Program (MILP) and integrates a hybrid scenario generation method capable of representing uncertain inputs such as electricity prices, renewable generation, and internal demand. A convex objective function combining expected and worst case operational costs allows for tunable risk aversion, enabling planners to balance economic performance and robustness. The resulting schedule ensures feasibility across all scenarios and supports coordinated use of industrial flexibility assets, including battery energy storage and shiftable production. To isolate the effects of market volatility, the framework is applied to a real world cement manufacturing case study considering only day-ahead electricity price uncertainty, with all other inputs treated deterministically. Results show improved resilience to forecast deviations, reduced cost variability, and more consistent operations. The proposed method offers a scalable and risk-aware approach for industrial flexibility planning under uncertainty.
Joint Beamforming with Extremely Large Scale RIS: A Sequential Multi-Agent A2C Approach
Authors: Zhi Chai, Jiajie Xu, Justin P Coon, Mohamed-Slim Alouini
Date: 2025-06-12 00:00:00
Tasks: Deep Reinforcement Learning

It is a challenging problem to jointly optimize the base station (BS) precoding matrix and the reconfigurable intelligent surface (RIS) phases simultaneously in a RIS-assisted multiple-user multiple-input-multiple-output (MU-MIMO) scenario when the size of the RIS becomes extremely large. In this paper, we propose a deep reinforcement learning algorithm called sequential multi-agent advantage actor-critic (A2C) to solve this problem. In addition, the discrete phase of RISs, imperfect channel state information (CSI), and channel correlations between users are taken into consideration. The computational complexity is also analyzed, and the performance of the proposed algorithm is compared with the zero-forcing (ZF) beamformer in terms of the sum spectral efficiency (SE). It is noted that the computational complexity of the proposed algorithm is lower than the benchmark, while the performance is better than the benchmark. Throughout simulations, it is also found that the proposed algorithm is robust to medium channel estimation error.
Sampling-Based Planning Under STL Specifications: A Forward Invariance Approach
Authors: Gregorio Marchesini, Siyuan Liu, Lars Lindemann, Dimos V. Dimarogonas
Date: 2025-06-12 00:00:00

We propose a variant of the Rapidly Exploring Random Tree Star (RRT$^{\star}$) algorithm to synthesize trajectories satisfying a given spatio-temporal specification expressed in a fragment of Signal Temporal Logic (STL) for linear systems. Previous approaches for planning trajectories under STL specifications using sampling-based methods leverage either mixed-integer or non-smooth optimization techniques, with poor scalability in the horizon and complexity of the task. We adopt instead a control-theoretic perspective on the problem, based on the notion of set forward invariance. Specifically, from a given STL task defined over polyhedral predicates, we develop a novel algorithmic framework by which the task is efficiently encoded into a time-varying set via linear programming, such that trajectories evolving within the set also satisfy the task. Forward invariance properties of the resulting set with respect to the system dynamics and input limitations are then proved via non-smooth analysis. We then present a modified RRT$^{\star}$ algorithm to synthesize asymptotically optimal and dynamically feasible trajectories satisfying a given STL specification, by sampling a tree of trajectories within the previously constructed time-varying set. We showcase two use cases of our approach involving an autonomous inspection of the International Space Station and room-servicing task requiring timed revisit of a charging station.
Transient performance of MPC for tracking without terminal constraints
Authors: Nadine Ehmann, Matthias Köhler, Frank Allgöwer
Date: 2025-06-12 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) for tracking is a recently introduced approach, which extends standard MPC formulations by incorporating an artificial reference as an additional optimization variable, in order to track external and potentially time-varying references. In this work, we analyze the performance of such an MPC for tracking scheme without a terminal cost and terminal constraints. We derive a transient performance estimate, i.e. a bound on the closed-loop performance over an arbitrary time interval, yielding insights on how to select the scheme's parameters for performance. Furthermore, we show that in the asymptotic case, where the prediction horizon and observed time interval tend to infinity, the closed-loop solution of MPC for tracking recovers the infinite horizon optimal solution.