pwc-archive/papers-with-abstracts huggingface dataset:
HarmonE: A Self-Adaptive Approach to Architecting Sustainable MLOps
Authors: Hiya Bhatt, Shaunak Biswas, Srinivasan Rakhunathan, Karthik Vaidhyanathan
Date: 2025-05-19 00:00:00

Machine Learning Enabled Systems (MLS) are becoming integral to real-world applications, but ensuring their sustainable performance over time remains a significant challenge. These systems operate in dynamic environments and face runtime uncertainties like data drift and model degradation, which affect the sustainability of MLS across multiple dimensions: technical, economical, environmental, and social. While Machine Learning Operations (MLOps) addresses the technical dimension by streamlining the ML model lifecycle, it overlooks other dimensions. Furthermore, some traditional practices, such as frequent retraining, incur substantial energy and computational overhead, thus amplifying sustainability concerns. To address them, we introduce HarmonE, an architectural approach that enables self-adaptive capabilities in MLOps pipelines using the MAPE-K loop. HarmonE allows system architects to define explicit sustainability goals and adaptation thresholds at design time, and performs runtime monitoring of key metrics, such as prediction accuracy, energy consumption, and data distribution shifts, to trigger appropriate adaptation strategies. We validate our approach using a Digital Twin (DT) of an Intelligent Transportation System (ITS), focusing on traffic flow prediction as our primary use case. The DT employs time series ML models to simulate real-time traffic and assess various flow scenarios. Our results show that HarmonE adapts effectively to evolving conditions while maintaining accuracy and meeting sustainability goals.
Guided Search Strategies in Non-Serializable Environments with Applications to Software Engineering Agents
Authors: Karina Zainullina, Alexander Golubev, Maria Trofimova, Sergei Polezhaev, Ibragim Badertdinov, Daria Litvintseva, Simon Karasik, Filipp Fisin, Sergei Skvortsov, Maksim Nekrashevich, Anton Shevtsov, Boris Yangel
Date: 2025-05-19 00:00:00
Tasks: Mathematical Reasoning

Large language models (LLMs) have recently achieved remarkable results in complex multi-step tasks, such as mathematical reasoning and agentic software engineering. However, they often struggle to maintain consistent performance across multiple solution attempts. One effective approach to narrow the gap between average-case and best-case performance is guided test-time search, which explores multiple solution paths to identify the most promising one. Unfortunately, effective search techniques (e.g. MCTS) are often unsuitable for non-serializable RL environments, such as Docker containers, where intermediate environment states cannot be easily saved and restored. We investigate two complementary search strategies applicable to such environments: 1-step lookahead and trajectory selection, both guided by a learned action-value function estimator. On the SWE-bench Verified benchmark, a key testbed for agentic software engineering, we find these methods to double the average success rate of a fine-tuned Qwen-72B model, achieving 40.8%, the new state-of-the-art for open-weights models. Additionally, we show that these techniques are transferable to more advanced closed models, yielding similar improvements with GPT-4o.
Selective Code Generation for Functional Guarantees
Authors: Jaewoo Jeong, Taesoo Kim, Sangdon Park
Date: 2025-05-19 00:00:00
Tasks: Code Generation, Hallucination, Mathematical Reasoning

Large language models (LLMs) show human-level performance and their specialized descendants, code generation models, play core roles in solving complex tasks, including mathematical reasoning and software development. On the downside, the hallucination of LLMs mainly hinders their applicability to systems requiring higher safety standards, thus drawing the attention of the AI community. However, the hallucination of code generation models is rarely considered. One critical bottleneck in considering code hallucination is the intricate property of code to identify whether generated code has the intended functionality due to its un-natural form, different to natural languages. Handful of unit tests have been considered to address this issue, but scaling-up its size is extremely expensive. We address this core bottleneck by automatically generating unit tests using dynamic code analysis tools, which leverages the \emph{executable nature} of code. Given generated unit tests from true code for measuring functional correctness of generated code, we propose to learn a \emph{selective code generator}, which abstains from answering for unsure generation, to control the rate of code hallucination among non-abstaining answers in terms of a false discovery rate. This learning algorithm provides a controllability guarantee, providing trustworthiness of code generation. Finally, we propose to use generated unit tests in evaluation as well as in learning for precise code evaluation, calling this evaluation paradigm \emph{FuzzEval}. We demonstrate the efficacy of our selective code generator over open and closed code generators, showing clear benefit of leveraging generated unit tests along with the controllability of code hallucination and reasonable selection efficiency via our selective code generator.
Enhancing Code Quality with Generative AI: Boosting Developer Warning Compliance
Authors: Hansen Chang, Christian DeLozier
Date: 2025-05-16 00:00:00

Programmers have long ignored warnings, especially those generated by static analysis tools, due to the potential for false-positives. In some cases, warnings may be indicative of larger issues, but programmers may not understand how a seemingly unimportant warning can grow into a vulnerability. Because these messages tend to be long and confusing, programmers tend to ignore them if they do not cause readily identifiable issues. Large language models can simplify these warnings, explain the gravity of important warnings, and suggest potential fixes to increase developer compliance with fixing warnings.
FLOW-BENCH: Towards Conversational Generation of Enterprise Workflows
Authors: Evelyn Duesterwald, Siyu Huo, Vatche Isahagian, K. R. Jayaram, Ritesh Kumar, Vinod Muthusamy, Punleuk Oum, Debashish Saha, Gegi Thomas, Praveen Venkateswaran
Date: 2025-05-16 00:00:00

Business process automation (BPA) that leverages Large Language Models (LLMs) to convert natural language (NL) instructions into structured business process artifacts is becoming a hot research topic. This paper makes two technical contributions -- (i) FLOW-BENCH, a high quality dataset of paired natural language instructions and structured business process definitions to evaluate NL-based BPA tools, and support bourgeoning research in this area, and (ii) FLOW-GEN, our approach to utilize LLMs to translate natural language into an intermediate representation with Python syntax that facilitates final conversion into widely adopted business process definition languages, such as BPMN and DMN. We bootstrap FLOW-BENCH by demonstrating how it can be used to evaluate the components of FLOW-GEN across eight LLMs of varying sizes. We hope that FLOW-GEN and FLOW-BENCH catalyze further research in BPA making it more accessible to novice and expert users.
Compendium Manager: a tool for coordination of workflow management instances for bulk data processing in Python
Authors: Richard J. Abdill, Ran Blekhman
Date: 2025-05-16 00:00:00
Tasks: Management

Compendium Manager is a command-line tool written in Python to automate the provisioning, launch, and evaluation of bioinformatics pipelines. Although workflow management tools such as Snakemake and Nextflow enable users to automate the processing of samples within a single sequencing project, integrating many datasets in bulk requires launching and monitoring hundreds or thousands of pipelines. We present the Compendium Manager, a lightweight command-line tool to enable launching and monitoring analysis pipelines at scale. The tool can gauge progress through a list of projects, load results into a shared database, and record detailed processing metrics for later evaluation and reproducibility.
Counter-Inferential Behavior in Natural and Artificial Cognitive Systems
Authors: Serge Dolgikh
Date: 2025-05-19 00:00:00

This study explores the emergence of counter-inferential behavior in natural and artificial cognitive systems, that is, patterns in which agents misattribute empirical success or suppress adaptation, leading to epistemic rigidity or maladaptive stability. We analyze archetypal scenarios in which such behavior arises: reinforcement of stability through reward imbalance, meta-cognitive attribution of success to internal superiority, and protective reframing under perceived model fragility. Rather than arising from noise or flawed design, these behaviors emerge through structured interactions between internal information models, empirical feedback, and higher-order evaluation mechanisms. Drawing on evidence from artificial systems, biological cognition, human psychology, and social dynamics, we identify counter-inferential behavior as a general cognitive vulnerability that can manifest even in otherwise well-adapted systems. The findings highlight the importance of preserving minimal adaptive activation under stable conditions and suggest design principles for cognitive architectures that can resist rigidity under informational stress.
Transmission Neural Networks: Approximation and Optimal Control
Authors: Shuang Gao, Peter E. Caines
Date: 2025-05-19 00:00:00

Transmission Neural Networks (TransNNs) introduced by Gao and Caines (2022) connect virus spread models over networks and neural networks with tuneable activation functions. This paper presents the approximation technique and the underlying assumptions employed by TransNNs in relation to the corresponding Markovian Susceptible-Infected-Susceptible (SIS) model with 2^n states, where n is the number of nodes in the network. The underlying infection paths are assumed to be stochastic with heterogeneous and time-varying transmission probabilities. We obtain the conditional probability of infection in the stochastic 2^n-state SIS epidemic model corresponding to each state configuration under mild assumptions, which enables control solutions based on Markov decision processes (MDP). Finally, MDP control with 2^n-state SIS epidemic models and optimal control with TransNNs are compared in terms of mitigating virus spread over networks through vaccination, and it is shown that TranNNs enable the generation of control laws with significant computational savings, albeit with more conservative control actions.
Analysis and Resilience of the U.S. Flight Network
Authors: Sushrit Kafle, Shreejan Pandey
Date: 2025-05-16 00:00:00

Air travel is one of the most widely used transportation services in the United States. This paper analyzes the U.S. Flight Network (USFN) using complex network theory by exploring how the network's topology contributes to its efficiency and vulnerability. This is done by examining the structural properties, degree distributions, and community structures in the network. USFN was observed to follow power-law distribution and falls under the anomalous regime, suggesting that the network is hub dominant. Compared to null networks, USFN has a higher clustering coefficient and modularity. Various percolation test revealed that USFN is vulnerable to targeted attacks and is susceptible to complete cascading failure if one of the major hubs fails. The overall results suggest that while the USFN is designed for efficiency, it is highly vulnerable to disruptions. Protecting key hub airports is important to make the network more robust and prevent large-scale failures.
Sequential QCQP for Bilevel Optimization with Line Search
Authors: Sina Sharifi, Erfan Yazdandoost Hamedani, Mahyar Fazlyab
Date: 2025-05-20 00:00:00
Tasks: Bilevel Optimization

Bilevel optimization involves a hierarchical structure where one problem is nested within another, leading to complex interdependencies between levels. We propose a single-loop, tuning-free algorithm that guarantees anytime feasibility, i.e., approximate satisfaction of the lower-level optimality condition, while ensuring descent of the upper-level objective. At each iteration, a convex quadratically-constrained quadratic program (QCQP) with a closed-form solution yields the search direction, followed by a backtracking line search inspired by control barrier functions to ensure safe, uniformly positive step sizes. The resulting method is scalable, requires no hyperparameter tuning, and converges under mild local regularity assumptions. We establish an O(1/k) ergodic convergence rate and demonstrate the algorithm's effectiveness on representative bilevel tasks.
Development of a Scaled Setup for Experimental Study of the Effect of Lateral Dynamics on Energy Consumption in Electric Vehicles: An Extension
Authors: Simran Kumari, Anand Ronald K., Siddhartha Mukhopadhyay, Ashish R. Hota
Date: 2025-05-20 00:00:00

Most of the existing state-of-the-art approaches for energy consumption analysis do not account for the effect of lateral dynamics on energy consumption in electric vehicles (EVs) during vehicle maneuvers. This paper aims to validate this effect through an experimental study. We develop a scaled model using a radio-controlled (RC) car, modified to achieve dynamic similitude with on-road vehicles, to conduct scaled experiments. The experimental results confirm the impact of lateral dynamics on both energy demand and driving range in electric vehicles, aligning with our previous findings [1], and emphasize the need to incorporate these factors into energy consumption models. This is an extended version of a paper accepted at IEEE ITEC 2025. It includes additional results and analysis.
Comparison of Data-Driven Modeling Approaches for Control Optimization of Floating Offshore Wind Turbines
Authors: Athul K. Sundarrajan, Daniel R. Herber
Date: 2025-05-20 00:00:00

Models that balance accuracy against computational costs are advantageous when designing wind turbines with optimization studies, as several hundred predictive function evaluations might be necessary to identify the optimal solution. We explore different approaches to construct low-fidelity models that can be used to approximate dynamic quantities and be used as surrogates for design optimization studies and other use cases. In particular, low-fidelity modeling approaches using classical systems identification and deep learning approaches are considered against derivative function surrogate models ({DFSMs}), or approximate models of the state derivative function. This work proposes a novel method that utilizes a linear parameter varying (LPV) modeling scheme to construct the DFSM. We compare the trade-offs between these different models and explore the efficacy of the proposed DFSM approach in approximating wind turbine performance and design optimization studies for controllers. Results show that the proposed DFSM approach balances computational time and model accuracy better than the system identification and deep learning based models. Additionally, the DFSM provides nearly a fifty times speed-up compared to the high-fidelity model, while balancing accuracy.
Security of Distributed Gradient Descent Against Byzantine Agents
Authors: Sribalaji C. Anand, Nicola Bastianello
Date: 2025-05-20 00:00:00

This paper investigates the security of Decentralized Gradient Descent (DGD) algorithms on undirected graphs. Specifically, we consider Byzantine agents that inject stealthy attacks to degrade the performance of the DGD algorithm in terms of its optimal value. To mitigate the effect of stealthy attacks, some agents are allocated to monitor the evolution of their gradient. We then propose a method to quantify the maximum deviation caused by the Byzantine agent in the optimal value of the DGD. Our approach serves as a security metric to evaluate the robustness of graph structures against Byzantine attacks. We validate our findings through numerical simulations.
Efficient Configuration-Constrained Tube MPC via Variables Restriction and Template Selection
Authors: Filippo Badalamenti, Sampath Kumar Mulagaleti, Mario Eduardo Villanueva, Boris Houska, Alberto Bemporad
Date: 2025-05-20 00:00:00
Tasks: Model Predictive Control

Configuration-Constrained Tube Model Predictive Control (CCTMPC) offers flexibility by using a polytopic parameterization of invariant sets and the optimization of an associated vertex control law. This flexibility, however, often demands computational trade-offs between set parameterization accuracy and optimization complexity. This paper proposes two innovations that help the user tackle this trade-off. First, a structured framework is proposed, which strategically limits optimization degrees of freedom, significantly reducing online computation time while retaining stability guarantees. This framework aligns with Homothetic Tube MPC (HTMPC) under maximal constraints. Second, a template refinement algorithm that iteratively solves quadratic programs is introduced to balance polytope complexity and conservatism. Simulation studies on an illustrative benchmark problem as well as a high-dimensional ten-state system demonstrate the approach's efficiency, achieving robust performance with minimal computational overhead. The results validate a practical pathway to leveraging CCTMPC's adaptability without sacrificing real-time viability.
A system identification approach to clustering vector autoregressive time series
Authors: Zuogong Yue, Xinyi Wang, Victor Solo
Date: 2025-05-20 00:00:00
Tasks: Clustering, Time Series, Time Series Clustering

Clustering of time series based on their underlying dynamics is keeping attracting researchers due to its impacts on assisting complex system modelling. Most current time series clustering methods handle only scalar time series, treat them as white noise, or rely on domain knowledge for high-quality feature construction, where the autocorrelation pattern/feature is mostly ignored. Instead of relying on heuristic feature/metric construction, the system identification approach allows treating vector time series clustering by explicitly considering their underlying autoregressive dynamics. We first derive a clustering algorithm based on a mixture autoregressive model. Unfortunately it turns out to have significant computational problems. We then derive a `small-noise' limiting version of the algorithm, which we call k-LMVAR (Limiting Mixture Vector AutoRegression), that is computationally manageable. We develop an associated BIC criterion for choosing the number of clusters and model order. The algorithm performs very well in comparative simulations and also scales well computationally.
A Data-Driven Method to Identify IBRs with Dominant Participation in Sub-Synchronous Oscillations
Authors: Youhong Chen, Debraj Bhattacharjee, Balarko Chaudhuri
Date: 2025-05-20 00:00:00
Tasks: Benchmarking

This paper introduces a data-driven (i.e., model-free) approach to identify which inverter-based resources (IBRs) have dominant participation in poorly damped sub-synchronous oscillations (SSO), to get to the root cause for effective mitigation. An Enhanced Dynamic Mode Decomposition (eDMD) method is proposed that incorporates an appropriate set of observables. Based on time-synchronized data (either simulated or real) from IBR connection points, eDMD directly computes data-driven eigenvectors and participation factors to reveal the role of each IBR in poorly damped SSO. We show the improved accuracy of eDMD over conventional Dynamic Mode Decomposition (DMD) by benchmarking both against actual model-based analysis. We demonstrate this first through a synthetic example and then a case study on the IEEE 39-bus test system with 100% IBR. This data-driven, model-free method offers a powerful tool to foresee and mitigate the risk of IBR-induced SSO in planning (simulated data) and post-event analysis (real data) of SSO events.
Functional Controllability, Functional Stabilisability, and the Generalised Separation Principle
Authors: Tyrone Fernando, Mohamed Darouach
Date: 2025-05-20 00:00:00

This paper introduces the new concepts of Functional Controllability and Functional Stabilisability, and establishes their duality with Functional Observability and Functional Detectability, respectively. We further present a Generalised Separation Principle, demonstrating that the classical Separation Principle emerges as a special case. Conditions for the existence of functional controllers of a specified order are derived. Importantly, the design framework does not require full controllability. Furthermore, we develop a functional observer-based controller design applicable to systems that are both uncontrollable and unobservable. The results presented generalise the classical full-state feedback control paradigm.
Statistically Optimal Structured Additive MIMO Continuous-time System Identification
Authors: Rodrigo A. González, Maarten van der Hulst, Koen Classens, Tom Oomen
Date: 2025-05-20 00:00:00

Many applications in mechanical, acoustic, and electronic engineering require estimating complex dynamical models, often represented as additive multi-input multi-output (MIMO) transfer functions with structural constraints. This paper introduces a two-stage procedure for estimating structured additive MIMO models, where structural constraints are enforced through a weighted nonlinear least-squares projection of the parameter vector initially estimated using a recently developed refined instrumental variables algorithm. The proposed approach is shown to be consistent and asymptotically efficient in open-loop scenarios. In closed-loop settings, it remains consistent despite potential noise model misspecification and achieves minimum covariance among all instrumental variable estimators. Extensive simulations are performed to validate the theoretical findings, and to show the efficacy of the proposed approach.
Gaming Strategies in European Imbalance Settlement Mechanisms
Authors: Seyed Soroush Karimi Madahi, Kenneth Bruninx, Bert Claessens, Chris Develder
Date: 2025-05-20 00:00:00

Transmission System Operators (TSOs) rely on balancing energy provided by Balancing Service Providers (BSPs) to maintain the supply-demand balance in real time. Balance Responsible Parties (BRPs) can simultaneously deviate from their day-ahead schedules in response to imbalance prices, e.g., by controlling flexible assets such as batteries. According to the European Electricity Balancing Guideline, these imbalance prices should incentivize BRPs performing such implicit or passive balancing to aid the TSO in restoring the energy balance. In this paper, we demonstrate that BRPs are unintentionally offered the opportunity to exploit gaming strategies in European imbalance settlement mechanisms. This is enabled by a disconnect between sub-quarter-hourly dynamics that determine the imbalance prices and the financial settlement on a quarter-hourly basis. We illustrate this behavior in a case study of the imbalance settlement mechanisms in Belgium and the Netherlands. Our results reveal that, in both countries, BRPs can, in theory, exploit the imbalance mechanism by increasing the instantaneous system imbalance during minutes within the quarter-hour that determine the imbalance price while still contributing to restoring the system balance for the rest of the quarter-hour.