pwc-archive/papers-with-abstracts huggingface dataset:
Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making
Authors: Zelun Tony Zhang, Leon Reicherts
Date: 2025-04-04 00:00:00
Tasks: Decision Making

How can we use generative AI to design tools that augment rather than replace human cognition? In this position paper, we review our own research on AI-assisted decision-making for lessons to learn. We observe that in both AI-assisted decision-making and generative AI, a popular approach is to suggest AI-generated end-to-end solutions to users, which users can then accept, reject, or edit. Alternatively, AI tools could offer more incremental support to help users solve tasks themselves, which we call process-oriented support. We describe findings on the challenges of end-to-end solutions, and how process-oriented support can address them. We also discuss the applicability of these findings to generative AI based on a recent study in which we compared both approaches to assist users in a complex decision-making task with LLMs.
A Human Digital Twin Architecture for Knowledge-based Interactions and Context-Aware Conversations
Authors: Abdul Mannan Mohammed, Azhar Ali Mohammad, Jason A. Ortiz, Carsten Neumann, Grace Bochenek, Dirk Reiners, Carolina Cruz-Neira
Date: 2025-04-04 00:00:00
Tasks: speech-recognition, Speech Recognition

Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) are creating new opportunities for Human-Autonomy Teaming (HAT) in tasks, missions, and continuous coordinated activities. A major challenge is enabling humans to maintain awareness and control over autonomous assets, while also building trust and supporting shared contextual understanding. To address this, we present a real-time Human Digital Twin (HDT) architecture that integrates Large Language Models (LLMs) for knowledge reporting, answering, and recommendation, embodied in a visual interface. The system applies a metacognitive approach to enable personalized, context-aware responses aligned with the human teammate's expectations. The HDT acts as a visually and behaviorally realistic team member, integrated throughout the mission lifecycle, from training to deployment to after-action review. Our architecture includes speech recognition, context processing, AI-driven dialogue, emotion modeling, lip-syncing, and multimodal feedback. We describe the system design, performance metrics, and future development directions for more adaptive and realistic HAT systems.
Design of AI-Powered Tool for Self-Regulation Support in Programming Education
Authors: Huiyong Li, Boxuan Ma
Date: 2025-04-03 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Large Language Model (LLM) tools have demonstrated their potential to deliver high-quality assistance by providing instant, personalized feedback that is crucial for effective programming education. However, many of these tools operate independently from institutional Learning Management Systems, which creates a significant disconnect. This isolation limits the ability to leverage learning materials and exercise context for generating tailored, context-aware feedback. Furthermore, previous research on self-regulated learning and LLM support mainly focused on knowledge acquisition, not the development of important self-regulation skills. To address these challenges, we developed CodeRunner Agent, an LLM-based programming assistant that integrates the CodeRunner, a student-submitted code executing and automated grading plugin in Moodle. CodeRunner Agent empowers educators to customize AI-generated feedback by incorporating detailed context from lecture materials, programming questions, student answers, and execution results. Additionally, it enhances students' self-regulated learning by providing strategy-based AI responses. This integrated, context-aware, and skill-focused approach offers promising avenues for data-driven improvements in programming education.
Ontologies in Design: How Imagining a Tree Reveals Possibilites and Assumptions in Large Language Models
Authors: Nava Haghighi, Sunny Yu, James Landay, Daniela Rosner
Date: 2025-04-03 00:00:00

Amid the recent uptake of Generative AI, sociotechnical scholars and critics have traced a multitude of resulting harms, with analyses largely focused on values and axiology (e.g., bias). While value-based analyses are crucial, we argue that ontologies -- concerning what we allow ourselves to think or talk about -- is a vital but under-recognized dimension in analyzing these systems. Proposing a need for a practice-based engagement with ontologies, we offer four orientations for considering ontologies in design: pluralism, groundedness, liveliness, and enactment. We share examples of potentialities that are opened up through these orientations across the entire LLM development pipeline by conducting two ontological analyses: examining the responses of four LLM-based chatbots in a prompting exercise, and analyzing the architecture of an LLM-based agent simulation. We conclude by sharing opportunities and limitations of working with ontologies in the design and development of sociotechnical systems.
Processes Matter: How ML/GAI Approaches Could Support Open Qualitative Coding of Online Discourse Datasets
Authors: John Chen, Alexandros Lotsos, Grace Wang, Lexie Zhao, Bruce Sherin, Uri Wilensky, Michael Horn
Date: 2025-04-02 00:00:00

Open coding, a key inductive step in qualitative research, discovers and constructs concepts from human datasets. However, capturing extensive and nuanced aspects or "coding moments" can be challenging, especially with large discourse datasets. While some studies explore machine learning (ML)/Generative AI (GAI)'s potential for open coding, few evaluation studies exist. We compare open coding results by five recently published ML/GAI approaches and four human coders, using a dataset of online chat messages around a mobile learning software. Our systematic analysis reveals ML/GAI approaches' strengths and weaknesses, uncovering the complementary potential between humans and AI. Line-by-line AI approaches effectively identify content-based codes, while humans excel in interpreting conversational dynamics. We discussed how embedded analytical processes could shape the results of ML/GAI approaches. Instead of replacing humans in open coding, researchers should integrate AI with and according to their analytical processes, e.g., as parallel co-coders.
Verification of Autonomous Neural Car Control with KeYmaera X
Authors: Enguerrand Prebet, Samuel Teuber, André Platzer
Date: 2025-04-04 00:00:00

This article presents a formal model and formal safety proofs for the ABZ'25 case study in differential dynamic logic (dL). The case study considers an autonomous car driving on a highway avoiding collisions with neighbouring cars. Using KeYmaera X's dL implementation, we prove absence of collision on an infinite time horizon which ensures that safety is preserved independently of trip length. The safety guarantees hold for time-varying reaction time and brake force. Our dL model considers the single lane scenario with cars ahead or behind. We demonstrate that dL with its tools is a rigorous foundation for runtime monitoring, shielding, and neural network verification. Doing so sheds light on inconsistencies between the provided specification and simulation environment highway-env of the ABZ'25 study. We attempt to fix these inconsistencies and uncover numerous counterexamples which also indicate issues in the provided reinforcement learning environment.
Distributionally Robust Predictive Runtime Verification under Spatio-Temporal Logic Specifications
Authors: Yiqi Zhao, Emily Zhu, Bardh Hoxha, Georgios Fainekos, Jyotirmoy V. Deshmukh, Lars Lindemann
Date: 2025-04-03 00:00:00
Tasks: Conformal Prediction

Cyber-physical systems designed in simulators, often consisting of multiple interacting agents, behave differently in the real-world. We would like to verify these systems during runtime when they are deployed. Thus, we propose robust predictive runtime verification (RPRV) algorithms for: (1) general stochastic CPS under signal temporal logic (STL) tasks, and (2) stochastic multi-agent systems (MAS) under spatio-temporal logic tasks. The RPRV problem presents the following challenges: (1) there may not be sufficient data on the behavior of the deployed CPS, (2) predictive models based on design phase system trajectories may encounter distribution shift during real-world deployment, and (3) the algorithms need to scale to the complexity of MAS and be applicable to spatio-temporal logic tasks. To address these challenges, we assume knowledge of an upper bound on the statistical distance (in terms of an f-divergence) between the trajectory distributions of the system at deployment and design time. We are motivated by our prior work [1, 2] where we proposed an accurate and an interpretable RPRV algorithm for general CPS, which we here extend to the MAS setting and spatio-temporal logic tasks. Specifically, we use a learned predictive model to estimate the system behavior at runtime and robust conformal prediction to obtain probabilistic guarantees by accounting for distribution shifts. Building on [1], we perform robust conformal prediction over the robust semantics of spatio-temporal reach and escape logic (STREL) to obtain centralized RPRV algorithms for MAS. We empirically validate our results in a drone swarm simulator, where we show the scalability of our RPRV algorithms to MAS and analyze the impact of different trajectory predictors on the verification result. To the best of our knowledge, these are the first statistically valid algorithms for MAS under distribution shift.
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement
Authors: Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
Date: 2025-04-04 00:00:00
Tasks: Navigate

In the interaction between agents and their environments, agents expand their capabilities by planning and executing actions. However, LLM-based agents face substantial challenges when deployed in novel environments or required to navigate unconventional action spaces. To empower agents to autonomously explore environments, optimize workflows, and enhance their understanding of actions, we propose SynWorld, a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search (MCTS) exploration to effectively refine their action knowledge in the current environment. Our experiments demonstrate that SynWorld is an effective and general approach to learning action knowledge in new environments. Code is available at https://github.com/zjunlp/SynWorld.
Agentic Knowledgeable Self-awareness
Authors: Shuofei Qiao, Zhisong Qiu, Baochang Ren, Xiaobin Wang, Xiangyuan Ru, Ningyu Zhang, Xiang Chen, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
Date: 2025-04-04 00:00:00
Tasks: Decision Making

Large Language Models (LLMs) have achieved considerable performance across various agentic planning tasks. However, traditional agent planning approaches adopt a "flood irrigation" methodology that indiscriminately injects gold trajectories, external feedback, and domain knowledge into agent models. This practice overlooks the fundamental human cognitive principle of situational self-awareness during decision-making-the ability to dynamically assess situational demands and strategically employ resources during decision-making. We propose agentic knowledgeable self-awareness to address this gap, a novel paradigm enabling LLM-based agents to autonomously regulate knowledge utilization. Specifically, we propose KnowSelf, a data-centric approach that applies agents with knowledgeable self-awareness like humans. Concretely, we devise a heuristic situation judgement criterion to mark special tokens on the agent's self-explored trajectories for collecting training data. Through a two-stage training process, the agent model can switch between different situations by generating specific special tokens, achieving optimal planning effects with minimal costs. Our experiments demonstrate that KnowSelf can outperform various strong baselines on different tasks and models with minimal use of external knowledge. Code is available at https://github.com/zjunlp/KnowSelf.
Decentralized Collective World Model for Emergent Communication and Coordination
Authors: Kentaro Nomura, Tatsuya Aoki, Tadahiro Taniguchi, Takato Horii
Date: 2025-04-04 00:00:00
Tasks: Contrastive Learning

We propose a fully decentralized multi-agent world model that enables both symbol emergence for communication and coordinated behavior through temporal extension of collective predictive coding. Unlike previous research that focuses on either communication or coordination separately, our approach achieves both simultaneously. Our method integrates world models with communication channels, enabling agents to predict environmental dynamics, estimate states from partial observations, and share critical information through bidirectional message exchange with contrastive learning for message alignment. Using a two-agent trajectory drawing task, we demonstrate that our communication-based approach outperforms non-communicative models when agents have divergent perceptual capabilities, achieving the second-best coordination after centralized models. Importantly, our decentralized approach with constraints preventing direct access to other agents' internal states facilitates the emergence of more meaningful symbol systems that accurately reflect environmental states. These findings demonstrate the effectiveness of decentralized communication for supporting coordination while developing shared representations of the environment.
Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations
Authors: Maik Larooij, Petter Törnberg
Date: 2025-04-04 00:00:00

Recent advancements in AI have reinvigorated Agent-Based Models (ABMs), as the integration of Large Language Models (LLMs) has led to the emergence of ``generative ABMs'' as a novel approach to simulating social systems. While ABMs offer means to bridge micro-level interactions with macro-level patterns, they have long faced criticisms from social scientists, pointing to e.g., lack of realism, computational complexity, and challenges of calibrating and validating against empirical data. This paper reviews the generative ABM literature to assess how this new approach adequately addresses these long-standing criticisms. Our findings show that studies show limited awareness of historical debates. Validation remains poorly addressed, with many studies relying solely on subjective assessments of model `believability', and even the most rigorous validation failing to adequately evidence operational validity. We argue that there are reasons to believe that LLMs will exacerbate rather than resolve the long-standing challenges of ABMs. The black-box nature of LLMs moreover limit their usefulness for disentangling complex emergent causal mechanisms. While generative ABMs are still in a stage of early experimentation, these findings question of whether and how the field can transition to the type of rigorous modeling needed to contribute to social scientific theory.
Distributed Linear Quadratic Gaussian for Multi-Robot Coordination with Localization Uncertainty
Authors: Tohid Kargar Tasooji, Sakineh Khodadadi
Date: 2025-03-26 00:00:00
Tasks: Decision Making

This paper addresses the problem of distributed coordination control for multi-robot systems (MRSs) in the presence of localization uncertainty using a Linear Quadratic Gaussian (LQG) approach. We introduce a stochastic LQG control strategy that ensures the coordination of mobile robots while optimizing a performance criterion. The proposed control framework accounts for the inherent uncertainty in localization measurements, enabling robust decision-making and coordination. We analyze the stability of the system under the proposed control protocol, deriving conditions for the convergence of the multi-robot network. The effectiveness of the proposed approach is demonstrated through experimental validation using Robotrium simulation experiments, showcasing the practical applicability of the control strategy in real-world scenarios with localization uncertainty.
Numerical simulation of atmospheric transport and dispersion of Phakopsora pachyrhizi urediniospores in South America using the state of Paraná-Brazil as a model
Authors: Eduardo Oliveira Belinelli, Lucas Henrique Fantin, Marcelo Giovanetti Canteri, Karla Braga, Eliandro Rodrigues Cirilo, Neyva Maria Lopes Romeiro, Paulo Laerte Natti
Date: 2025-04-04 00:00:00

Phakopsora pachyrhizi is a biotrophic fungus that requires living plant tissue to survive in the wild for extended periods. The fungus causes Asian rust and costs billions of US dollars every year for its control in South American soy production. Despite the regulatory measure that prohibits the cultivation of soybeans in some months of the year (soybean-free period) in Brazil, the presence of soybean production areas in neighboring countries, such as Paraguay and Bolivia, can help the survival of the pathogen between crop seasons. It is known that P. pachyrhizi urediniospores can be spread/transported thousands of kilometres by the wind. In this context, the objective of this work was to develop a mathematical model to simulate the atmospheric transport of P. pachyrhizi urediniospores from the west, Paraguay/Bolivia to Paran\'a Brazil, through storms coming from cold fronts. The transport of urediniospores was modeled by a diffusive-convective-reactive equation. Wind direction and the velocity of cold fronts that crossed Paran\'a state between October 2018 and February 2019 were used. For validation, real cases of rust occurrence in the Paran\'a state informed by the Anti-rust Consortium Portal in the season 2018/19 were used. The results confirm mathematically that urediniospores from infected areas located in a country on the west can be transported and deposited on the east, in the state of Paran\'a. The first case of soybean rust in Paran\'a state/Brazil was registered 10 days after the first cold front, suggesting that the transported and deposited urediniospores were still viable for host infection. This work reinforces the importance of the establishment of the soybean-free period in other soybean-producing countries. It will also provide a better understanding of the fungus dispersion system, potentially enabling the correct use of fungicides.
Mathematical Modeling of Option Pricing with an Extended Black-Scholes Framework
Authors: Nikhil Shivakumar Nayak
Date: 2025-04-04 00:00:00
Tasks: Computational Efficiency

This study investigates enhancing option pricing by extending the Black-Scholes model to include stochastic volatility and interest rate variability within the Partial Differential Equation (PDE). The PDE is solved using the finite difference method. The extended Black-Scholes model and a machine learning-based LSTM model are developed and evaluated for pricing Google stock options. Both models were backtested using historical market data. While the LSTM model exhibited higher predictive accuracy, the finite difference method demonstrated superior computational efficiency. This work provides insights into model performance under varying market conditions and emphasizes the potential of hybrid approaches for robust financial modeling.
Properties of Fixed Points of Generalised Extra Gradient Methods Applied to Min-Max Problems
Authors: Amir Ali Farzin, Yuen-Man Pun, Philipp Braun, Iman Shames
Date: 2025-04-03 00:00:00

This paper studies properties of fixed points of generalised Extra-gradient (GEG) algorithms applied to min-max problems. We discuss connections between saddle points of the objective function of the min-max problem and GEG fixed points. We show that, under appropriate step-size selections, the set of saddle points (Nash equilibria) is a subset of stable fixed points of GEG. Convergence properties of the GEG algorithm are obtained through a stability analysis of a discrete-time dynamical system. The results and benefits when compared to existing methods are illustrated through numerical examples.
Optimistic Learning for Communication Networks
Authors: George Iosifidis, Naram Mhaisen, Douglas J. Leith
Date: 2025-04-04 00:00:00
Tasks: Edge-computing, Management

AI/ML-based tools are at the forefront of resource management solutions for communication networks. Deep learning, in particular, is highly effective in facilitating fast and high-performing decision-making whenever representative training data is available to build offline accurate models. Conversely, online learning solutions do not require training and enable adaptive decisions based on runtime observations, alas are often overly conservative. This extensive tutorial proposes the use of optimistic learning (OpL) as a decision engine for resource management frameworks in modern communication systems. When properly designed, such solutions can achieve fast and high-performing decisions -- comparable to offline-trained models -- while preserving the robustness and performance guarantees of the respective online learning approaches. We introduce the fundamental concepts, algorithms and results of OpL, discuss the roots of this theory and present different approaches to defining and achieving optimism. We proceed to showcase how OpL can enhance resource management in communication networks for several key problems such as caching, edge computing, network slicing, and workload assignment in decentralized O-RAN platforms. Finally, we discuss the open challenges that must be addressed to unlock the full potential of this new resource management approach.
Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin for Real-World Robot Policy Evaluation
Authors: Jad Abou-Chakra, Lingfeng Sun, Krishan Rana, Brandon May, Karl Schmeckpeper, Maria Vittoria Minniti, Laura Herlant
Date: 2025-04-04 00:00:00

Recent advancements in behavior cloning have enabled robots to perform complex manipulation tasks. However, accurately assessing training performance remains challenging, particularly for real-world applications, as behavior cloning losses often correlate poorly with actual task success. Consequently, researchers resort to success rate metrics derived from costly and time-consuming real-world evaluations, making the identification of optimal policies and detection of overfitting or underfitting impractical. To address these issues, we propose real-is-sim, a novel behavior cloning framework that incorporates a dynamic digital twin (based on Embodied Gaussians) throughout the entire policy development pipeline: data collection, training, and deployment. By continuously aligning the simulated world with the physical world, demonstrations can be collected in the real world with states extracted from the simulator. The simulator enables flexible state representations by rendering image inputs from any viewpoint or extracting low-level state information from objects embodied within the scene. During training, policies can be directly evaluated within the simulator in an offline and highly parallelizable manner. Finally, during deployment, policies are run within the simulator where the real robot directly tracks the simulated robot's joints, effectively decoupling policy execution from real hardware and mitigating traditional domain-transfer challenges. We validate real-is-sim on the PushT manipulation task, demonstrating strong correlation between success rates obtained in the simulator and real-world evaluations. Videos of our system can be found at https://realissim.rai-inst.com.