pwc-archive/papers-with-abstracts huggingface dataset:
Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs
Authors: Yao Fehlis, Paul Mandel, Charles Crain, Betty Liu, David Fuller
Date: 2025-04-01 00:00:00
Tasks: Decision Making, Drug Discovery, Scheduling

Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation, but they face challenges in orchestrating complex workflows, integrating diverse instruments and AI models, and managing data efficiently. Artificial addresses these issues with a comprehensive orchestration and scheduling system that unifies lab operations, automates workflows, and integrates AI-driven decision-making. By incorporating AI/ML models like NVIDIA BioNeMo - which facilitates molecular interaction prediction and biomolecular analysis - Artificial enhances drug discovery and accelerates data-driven research. Through real-time coordination of instruments, robots, and personnel, the platform streamlines experiments, enhances reproducibility, and advances drug discovery.
SRLCG: Self-Rectified Large-Scale Code Generation with Multidimensional Chain-of-Thought and Dynamic Backtracking
Authors: Hongru Ma, Yanjie Liang, Jiasheng Si, Weiyu Zhang, Hongjiao Guan, Chaoqun Zheng, Bing Xu, Wenpeng Lu
Date: 2025-04-01 00:00:00
Tasks: Code Generation

Large language models (LLMs) have revolutionized code generation, significantly enhancing developer productivity. However, for a vast number of users with minimal coding knowledge, LLMs provide little support, as they primarily generate isolated code snippets rather than complete, large-scale project code. Without coding expertise, these users struggle to interpret, modify, and iteratively refine the outputs of LLMs, making it impossible to assemble a complete project. To address this issue, we propose Self-Rectified Large-Scale Code Generator (SRLCG), a framework that generates complete multi-file project code from a single prompt. SRLCG employs a novel multidimensional chain-of-thought (CoT) and self-rectification to guide LLMs in generating correct and robust code files, then integrates them into a complete and coherent project using our proposed dynamic backtracking algorithm. Experimental results show that SRLCG generates code 15x longer than DeepSeek-V3, 16x longer than GPT-4, and at least 10x longer than other leading CoT-based baselines. Furthermore, they confirm its improved correctness, robustness, and performance compared to baselines in large-scale code generation.
Automated detection of atomicity violations in large-scale systems
Authors: Hang He, Yixing Luo, Chengcheng Wan, Ting Su, Haiying Sun, Geguang Pu
Date: 2025-04-01 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Atomicity violations in interrupt-driven programs pose a significant threat to software safety in critical systems. These violations occur when the execution sequence of operations on shared resources is disrupted by asynchronous interrupts. Detecting atomicity violations is challenging due to the vast program state space, application-level code dependencies, and complex domain-specific knowledge. We propose Clover, a hybrid framework that integrates static analysis with large language model (LLM) agents to detect atomicity violations in real-world programs. Clover first performs static analysis to extract critical code snippets and operation information. It then initiates a multi-agent process, where the expert agent leverages domain-specific knowledge to detect atomicity violations, which are subsequently validated by the judge agent. Evaluations on RaceBench 2.1, SV-COMP, and RWIP demonstrate that Clover achieves a precision/recall of 92.3%/86.6%, outperforming existing approaches by 27.4-118.2% on F1-score.
Leveraging LLMs for User Stories in AI Systems: UStAI Dataset
Authors: Asma Yamani, Malak Baslyman, Moataz Ahmed
Date: 2025-04-01 00:00:00

AI systems are gaining widespread adoption across various sectors and domains. Creating high-quality AI system requirements is crucial for aligning the AI system with business goals and consumer values and for social responsibility. However, with the uncertain nature of AI systems and the heavy reliance on sensitive data, more research is needed to address the elicitation and analysis of AI systems requirements. With the proprietary nature of many AI systems, there is a lack of open-source requirements artifacts and technical requirements documents for AI systems, limiting broader research and investigation. With Large Language Models (LLMs) emerging as a promising alternative to human-generated text, this paper investigates the potential use of LLMs to generate user stories for AI systems based on abstracts from scholarly papers. We conducted an empirical evaluation using three LLMs and generated $1260$ user stories from $42$ abstracts from $26$ domains. We assess their quality using the Quality User Story (QUS) framework. Moreover, we identify relevant non-functional requirements (NFRs) and ethical principles. Our analysis demonstrates that the investigated LLMs can generate user stories inspired by the needs of various stakeholders, offering a promising approach for generating user stories for research purposes and for aiding in the early requirements elicitation phase of AI systems. We have compiled and curated a collection of stories generated by various LLMs into a dataset (UStAI), which is now publicly available for use.
GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks
Authors: Lahiru Akmeemana, Chamodya Attanayake, Husni Faiz, Sandareka Wickramanayake
Date: 2025-03-31 00:00:00
Tasks: Anomaly Detection, Graph Attention

The transition to microservices has revolutionized software architectures, offering enhanced scalability and modularity. However, the distributed and dynamic nature of microservices introduces complexities in ensuring system reliability, making anomaly detection crucial for maintaining performance and functionality. Anomalies stemming from network and performance issues must be swiftly identified and addressed. Existing anomaly detection techniques often rely on statistical models or machine learning methods that struggle with the high-dimensional, interdependent data inherent in microservice applications. Current techniques and available datasets predominantly focus on system traces and logs, limiting their ability to support advanced detection models. This paper addresses these gaps by introducing the RS-Anomic dataset generated using the open-source RobotShop microservice application. The dataset captures multivariate performance metrics and response times under normal and anomalous conditions, encompassing ten types of anomalies. We propose a novel anomaly detection model called Graph Attention and LSTM-based Microservice Anomaly Detection (GAL-MAD), leveraging Graph Attention and Long Short-Term Memory architectures to capture spatial and temporal dependencies in microservices. We utilize SHAP values to localize anomalous services and identify root causes to enhance explainability. Experimental results demonstrate that GAL-MAD outperforms state-of-the-art models on the RS-Anomic dataset, achieving higher accuracy and recall across varying anomaly rates. The explanations provide actionable insights into service anomalies, which benefits system administrators.
Generating Structured Plan Representation of Procedures with LLMs
Authors: Deepeka Garg, Sihan Zeng, Sumitra Ganesh, Leo Ardon
Date: 2025-03-28 00:00:00

In this paper, we address the challenges of managing Standard Operating Procedures (SOPs), which often suffer from inconsistencies in language, format, and execution, leading to operational inefficiencies. Traditional process modeling demands significant manual effort, domain expertise, and familiarity with complex languages like Business Process Modeling Notation (BPMN), creating barriers for non-techincal users. We introduce SOP Structuring (SOPStruct), a novel approach that leverages Large Language Models (LLMs) to transform SOPs into decision-tree-based structured representations. SOPStruct produces a standardized representation of SOPs across different domains, reduces cognitive load, and improves user comprehension by effectively capturing task dependencies and ensuring sequential integrity. Our approach enables leveraging the structured information to automate workflows as well as empower the human users. By organizing procedures into logical graphs, SOPStruct facilitates backtracking and error correction, offering a scalable solution for process optimization. We employ a novel evaluation framework, combining deterministic methods with the Planning Domain Definition Language (PDDL) to verify graph soundness, and non-deterministic assessment by an LLM to ensure completeness. We empirically validate the robustness of our LLM-based structured SOP representation methodology across SOPs from different domains and varying levels of complexity. Despite the current lack of automation readiness in many organizations, our research highlights the transformative potential of LLMs to streamline process modeling, paving the way for future advancements in automated procedure optimization.
Tunable Thresholds and Frequency Encoding in a Spiking NOD Controller
Authors: Ian Xul Belaustegui, Alessio Franci, Naomi Ehrich Leonard
Date: 2025-04-02 00:00:00
Tasks: Decision Making

Spiking Nonlinear Opinion Dynamics (S-NOD) is an excitable decision-making model inspired by the spiking dynamics of neurons. S-NOD enables the design of agile decision-making that can rapidly switch between decision options in response to a changing environment. In S-NOD, decisions are represented by continuous time, yet discrete, opinion spikes. Here, we extend previous analysis of S-NOD and explore its potential as a nonlinear controller with a tunable balance between robustness and responsiveness. We identify and provide necessary conditions for the bifurcation that determines the onset of periodic opinion spiking. We leverage this analysis to characterize the tunability of the input-output threshold for opinion spiking as a function of the model basal sensitivity and the modulation of opinion spiking frequency as a function of input magnitude past threshold. We conclude with a discussion on S-NOD as a new neuromorphic control block and its extension to distributed spiking controllers.
Non-collocated vibration absorption using delayed resonator for spectral and spacial tuning -- analysis and experimental validation
Authors: Matěj Kuře, Adam Peichl, Jaroslav Bušek, Nejat Olgac, Tomáš Vyhlídal
Date: 2025-04-02 00:00:00

Non-collocated vibration absorption (NCVA) concept using delayed resonator for in-situ tuning is analyzed and experimentally validated. There are two critical contributions of this work. One is on the scalable analytical pathway for verifying the concept of resonant substructure as the basis of the ideal vibration absorption. The second is to experimentally validate the spatial and spectral tunability of NCVA structures for the first time. For both novelties arbitrarily large dimensions of interconnected mass-spring-damper chains are considered. Following the state of the art on NCVA, control synthesis is performed over the resonant substructure comprising the delayed resonator and a part of the primary structure involved in the vibration absorption. The experimental validation of the proposed NCVA concept is performed on a mechatronic setup with three interconnected cart-bodies. Based on the spectral analysis, an excitation frequency is selected for which a stable vibration suppression can be achieved sequentially for all the three bodies, one collocated and two non-collocated. The experimental results closely match the simulations for complete vibration suppression at the targeted bodies, and thus validating the crucial spatial tunability characteristic as well as the traditional spectral tuning.
Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference
Authors: Robert Lefringhausen, Sami Leon Noel Aziz Hanna, Elias August, Sandra Hirche
Date: 2025-04-02 00:00:00
Tasks: Bayesian Inference, Uncertainty Quantification, valid

Certifying safety in dynamical systems is crucial, but barrier certificates - widely used to verify that system trajectories remain within a safe region - typically require explicit system models. When dynamics are unknown, data-driven methods can be used instead, yet obtaining a valid certificate requires rigorous uncertainty quantification. For this purpose, existing methods usually rely on full-state measurements, limiting their applicability. This paper proposes a novel approach for synthesizing barrier certificates for unknown systems with latent states and polynomial dynamics. A Bayesian framework is employed, where a prior in state-space representation is updated using input-output data via a targeted marginal Metropolis-Hastings sampler. The resulting samples are used to construct a candidate barrier certificate through a sum-of-squares program. It is shown that if the candidate satisfies the required conditions on a test set of additional samples, it is also valid for the true, unknown system with high probability. The approach and its probabilistic guarantees are illustrated through a numerical simulation.
Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error
Authors: Anne Somalwar, Bruce D. Lee, George J. Pappas, Nikolai Matni
Date: 2025-04-02 00:00:00
Tasks: Imitation Learning, Model-based Reinforcement Learning

Compounding error, where small prediction mistakes accumulate over time, presents a major challenge in learning-based control. For example, this issue often limits the performance of model-based reinforcement learning and imitation learning. One common approach to mitigate compounding error is to train multi-step predictors directly, rather than relying on autoregressive rollout of a single-step model. However, it is not well understood when the benefits of multi-step prediction outweigh the added complexity of learning a more complicated model. In this work, we provide a rigorous analysis of this trade-off in the context of linear dynamical systems. We show that when the model class is well-specified and accurately captures the system dynamics, single-step models achieve lower asymptotic prediction error. On the other hand, when the model class is misspecified due to partial observability, direct multi-step predictors can significantly reduce bias and thus outperform single-step approaches. These theoretical results are supported by numerical experiments, wherein we also (a) empirically evaluate an intermediate strategy which trains a single-step model using a multi-step loss and (b) evaluate performance of single step and multi-step predictors in a closed loop control setting.
Alpha-Beta HMM: Hidden Markov Model Filtering with Equal Exit Probabilities and a Step-Size Parameter
Authors: Dongyan Sui, Haotian Pu, Siyang Leng, Stefan Vlaski
Date: 2025-04-02 00:00:00

The hidden Markov model (HMM) provides a powerful framework for inference in time-varying environments, where the underlying state evolves according to a Markov chain. To address the optimal filtering problem in general dynamic settings, we propose the $\alpha\beta$-HMM algorithm, which simplifies the state transition model to a Markov chain with equal exit probabilities and introduces a step-size parameter to balance the influence of observational data and the model. By analyzing the algorithm's dynamics in stationary environments, we uncover a fundamental trade-off between inference accuracy and adaptation capability, highlighting how key parameters and observation quality impact performance. A comprehensive theoretical analysis of the nonlinear dynamical system governing the evolution of the log-belief ratio, along with supporting numerical experiments, demonstrates that the proposed approach effectively balances adaptability and inference performance in dynamic environments.
Convex Computations for Controlled Safety Invariant Sets of Black-box Discrete-time Dynamical Systems
Authors: Taoran Wu, Yiling Xue, Jingduo Pan, Dejin Ren, Arvind Easwaran, Bai Xue
Date: 2025-04-02 00:00:00

Identifying controlled safety invariant sets (CSISs) is essential in safety-critical applications. This paper tackles the problem of identifying CSISs for black-box discrete-time systems, where the model is unknown and only limited simulation data is accessible. Traditionally, a CSIS is defined as a subset of a safe set, encompassing initial states for which a control input exists that keeps the system within the set at the next time step-this is referred to as the one-step invariance property. However, the requirement for one-step invariance can be equivalently translated into a stricter condition of ``always-invariance'', meaning that there exist control inputs capable of keeping the system within this set indefinitely. Such a condition may prove overly stringent or impractical for black-box systems, where predictions can become unreliable beyond a single time step or a limited number of finite time steps. To overcome the challenges posed by black-box systems, we reformulate the one-step invariance property in a ``Probably Approximately Correct'' (PAC) sense. This approach allows us to assess the probability that a control input exists to keep the system within the CSIS at the next time step, with a predefined level of confidence. If the system successfully remains within the set at the next time step, we can then reapply the invariance evaluation to the new state, thereby facilitating a recursive assurance of invariance. Our method employs barrier functions and scenario optimization, resulting in a linear programming method to estimate PAC CSISs. Finally, the effectiveness of our approach is demonstrated on several examples.
Frequency-Domain Bounds for the Multiconductor Telegrapher's Equation
Authors: Daniel Selvaratnam, Alessio Moreschini, Amritam Das, Thomas Parisini, Henrik Sandberg
Date: 2025-04-02 00:00:00
Tasks: valid

We establish mathematical bounds on the chain, ABCD and immittance matrices of a multiconductor transmission line, based on the Telegrapher's equation. Closed-form expressions for those matrices are also presented. Existing results that hold on the imaginary axis are extended to the complex plane, without reliance on a simultaneous diagonalizability assumption that is ubiquitous in the literature. Therefore, the results remain valid even when line symmetry breaks down, as in the case of electrical faults. The system-theoretic properties established here are of general relevance to control, power systems, and signal processing involving multiconductor transmission lines.
Nonlinear Bandwidth and Bode Diagrams based on Scaled Relative Graphs
Authors: Julius P. J. Krebbekx, Roland Tóth, Amritam Das
Date: 2025-04-02 00:00:00

Scaled Relative Graphs (SRGs) provide a novel graphical frequency domain method for the analysis of nonlinear systems. In this paper, we use the restriction of the SRG to particular input spaces to compute frequency-dependent gain bounds for incrementally stable nonlinear systems. This leads to a nonlinear (NL) generalization of the Bode diagram, where the sinusoidal, harmonic, and subharmonic inputs are considered separately. When applied to the analysis of the NL loop transfer and sensitivity, we define a notion of bandwidth for both the open-loop and closed-loop, compatible with the LTI definitions. We illustrate the power of our method on the analysis of a DC motor with a parasitic nonlinearity, verifying our results in simulations.
Behavioral Inequalities
Authors: Soutrik Bandyopadhyay, Debasattam Pal, Shubhendu Bhasin
Date: 2025-04-02 00:00:00

We introduce behavioral inequalities as a way to model dynamical systems defined by inequalities among their variables of interest. We claim that such a formulation enables the representation of safety-aware dynamical systems, systems with bounds on disturbances, practical design limits and operational boundaries, etc. We develop a necessary and sufficient condition for the existence of solutions to such behavioral inequalities and provide a parametrization of solutions when they exist. Finally, we show the efficacy of the proposed method in two practical examples.
Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach
Authors: Yichen Sun, Chenggang Cui, Chuanlin Zhang, Chunyang Gong
Date: 2025-04-02 00:00:00
Tasks: Decision Making, Distributed Optimization, Management

This paper presents an enhanced electric vehicle demand response system based on large language models, aimed at optimizing the application of vehicle-to-grid technology. By leveraging an large language models-driven multi-agent framework to construct user digital twins integrated with multidimensional user profile features, it enables deep simulation and precise prediction of users' charging and discharging decision-making patterns. Additionally, a data- and knowledge-driven dynamic incentive mechanism is proposed, combining a distributed optimization model under network constraints to optimize the grid-user interaction while ensuring both economic viability and security. Simulation results demonstrate that the approach significantly improves load peak-valley regulation and charging/discharging strategies. Experimental validation highlights the system's substantial advantages in load balancing, user satisfaction and grid stability, providing decision-makers with a scalable V2G management tool that promotes the sustainable, synergistic development of vehicle-grid integration.
Navigating the Uncharted Waters: A Gradual Approach to the Certification and Integration of Maritime Autonomous Surface Ships (MASS) in Global Maritime Operations
Authors: Hany M. Arnaoot
Date: 2025-04-02 00:00:00

The integration of Maritime Autonomous Surface Ships (MASS) into global maritime operations represents a transformative shift in the shipping industry, promising enhanced safety, efficiency, and cost-effectiveness. However, the widespread adoption of autonomous ships necessitates a robust regulatory framework and rigorous certification processes to address the unique challenges posed by these advanced technologies. This paper proposes a gradual, multi-stage approach to the certification and integration of MASS, beginning with small-scale trials in controlled environments and progressing to large-scale international operations. Key considerations include the development of reliable control systems, cybersecurity measures, sensor technologies, and redundancy mechanisms to ensure safe and efficient navigation. Additionally, the paper explores the economic and environmental implications of autonomous shipping, as well as the evolving legal frameworks for liability and compensation in the event of collisions. By adopting a cautious and methodical approach, the maritime industry can mitigate risks and pave the way for the safe and sustainable integration of autonomous ships into global trade.