Artificial Intelligence and Robotics Commons^™

Debate, Reflect, And Distill: Multi-Agent Feedback With Tree-Structured Preference Optimization For Efficient Language Model Enhancement, Xiaofeng Zhou, Heyan Huang, Lizi Liao

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) continue to set new standards in knowledge-intensive and complex reasoning tasks, yet their high computational demands limit widespread adoption. While distilling large models into smaller ones offers a sustainable solution, current techniques—such as static knowledge distillation, resource-intensive reinforcement learning from human feedback, or limited self-reflection—struggle to yield substantial and lasting performance gains. In this paper, we present a novel Debate and Reflect (D&R) framework that orchestrates multi-turn debates between smaller models and stronger teacher models, eliciting actionable feedback (e.g., error analysis, corrective strategies) to guide student models. Further, we introduce Tree-structured Direct Preference Optimization (T-DPO) to …

Memotune: A Measure And Moment-Driven Fine-Tuning Framework For Quantized Large Language Models, Yun Zhang, Xue Geng, Lizi Liao, Jintong Sun, Minghe Yu, Ge Yu

Research Collection School Of Computing and Information Systems

Quantizing large language models (LLMs) is essential for reducing memory and computational costs in natural language processing. Existing methods combine quantization with parameter-efficient fine-tuning but often fail to meet practical performance requirements. This paper introduces MeMoTune, a novel fine-tuning framework for quantized LLMs. By employing a measure and moment approach within a low-rank approximation framework in probability measure space, MeMoTune optimizes the objective function for superior fine-tuning results. The update process is further refined through scaled gradient, enhancing convergence efficiency and noise robustness. Experiments on tasks like text generation, summarization, and understanding show MeMoTune significantly outperforms state-of-the-art methods, e.g. fine-tuning …

R2dqg: A Quality Meets Diversity Framework For Question Generation Over Knowledge Bases, Yimeng Ren, Yanhua Yu, Lizi Liao, Yuhu Shang, Kangkang Lu, Mingliang Yan

Research Collection School Of Computing and Information Systems

The task of Knowledge-Based Question Generation (KBQG) involves generating natural language questions from structured knowledge sources, posing unique challenges in balancing linguistic diversity and semantic relevance. Existing models often focus on maximizing surface-level similarity to ground-truth questions, neglecting the need for diverse syntactic forms and leading to semantic drift during generation. To overcome these challenges, we propose Refine-Reinforced Diverse Question Generation (R2DQG), a two-phase framework leveraging a generation-then-refinement paradigm. The Generator first constructs a diverse set of expressive templates using dependency parse tree similarity, capturing a wide range of syntactic patterns and styles. These templates guide the creation of question …

Consistent Client Simulation For Motivational Interviewing-Based Counseling, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Phey Ling Kit, Jenny Xiuhui Giam, John Pinto, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Simulating human clients in mental health counseling is crucial for training and evaluating counselors (both human or simulated) in a scalable manner. Nevertheless, past research on client simulation did not focus on complex conversation tasks such as mental health counseling. In these tasks, the challenge is to ensure that the client’s actions (i.e., interactions with the counselor) are consistent with with its stipulated profiles and negative behavior settings. In this paper, we propose a novel framework that supports consistent client simulation for mental health counseling. Our framework tracks the mental state of a simulated client, controls its state transitions, and …

Evaluating Immersion And Agency In Ai-Assisted Live Murder Mystery Games, Seraphina C. Courtney

LSU New Orleans Theses and Dissertations

This thesis explores the impact of AI-assisted narrative generation on player immersion and agency in a live-action roleplaying (LARP) experience. A live-action murder mystery game was designed and run in two formats: a static version with GPT-4 generated characters, dialogue, and stage directions, and a dynamic version where players created their own characters and improvised freely, guided by AI-generated narrative beats, a story element that moves the plot forward [21], that provided a narrative scaffolding for the players. The dynamic version employed a distributed computer vision system that tracks the movement of key items in the play-space so their relevance …

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

How To Do Things With Little Talking Tubes: Nonideal Speech Acts In The Digital Age, Anthony Holdier

Graduate Theses and Dissertations

In this work, I develop a view about what it means to share a social or conversational context with others, as well as what is normatively entailed by doing so. Working from a broadly Austinian perspective about the moral foundations of language use and how we use words to position ourselves within social space, as well as from a generally Stalnakerian social ontology (demarcating groups by dint of aligned or overlapping sets of commitments), I present three papers demonstrating how nonidealized, ordinary language philosophy can make sense of complex, real-world phenomena. Through analyses of heretical utterances, context collapse, and chatbot …

Intersecting Realities And Evolving Landscapes: Mapping Generative Ai Within The Framework Of Digital Rhetoric, Joshua Troy Nieubuurt

English Theses & Dissertations

The increased usage of [Generative] AI technologies (GenAI) in the 21st century has called into the question the rhetorical agency of these digital things. [Gen]AI has historically been framed within a Heideggerian “readiness-to-hand” dynamic in which it has been unilaterally conceived as a tool to be used by humans. This dissertation proposes that the GenAI assemblage is capable of being a co-actor in rhetorical spaces. To provide evidence for this stance This dissertation utilizes Actor Network Theory to map the actants within a GenAI assemblage. In doing so it allows for an understanding of the stakeholders (both human and non-human) …

Human Activity Recognition And Identification Driven Automated Deep Learning For Time-Series Classification, Justin Alan Gamble

Engineering Management & Systems Engineering Theses & Dissertations

The growing emphasis on Digital Engineering (DE) within the U.S. Department of Defense (DoD) demands advanced methods for leveraging vast time-series data generated by sensor-rich environments. Deep learning models offer promising solutions for complex timeseries classification tasks, however their design and optimization remain highly resource intensive, requiring specialized expertise. This dissertation addresses this challenge by developing and evaluating an Automated Machine Learning (AutoML) framework specifically tailored for the time-series classification task of Human Activity Recognition and Identification (HARI).

A systematic investigation was conducted using the Design Science Research Methodology (DSRM) comparing traditional search strategies of grid search and random search …

Unfolding Particle Detector Effects And Solving Qcd Inverse Problem With Generative Ai, Tareq Saeed Alghamdi

Computer Science Theses & Dissertations

Advancements in artificial intelligence (AI) have revolutionized high-energy physics by enabling generative models to address key detector-related Challenges. This work explores the generative model to mitigate smearing, acceptance, and inefficiency in particle detectors, enhancing experimental precision.

We present a generative model-based framework to model and correct detector distortions. Using the Jefferson Lab CLAS g11 experiment as a case study, our approach successfully unfolds detector effects in multi-particle final states while preserving multidimensional correlations despite complex reaction mechanisms. A key focus is addressing the acceptance problem—accurately modeling detector acceptance without computationally expensive simulations. By training generative model-based framework on simulated detector …

Network Intelligence For Next-Generation Wireless Networks: Advancing Distribution And Coordination, Yonatan Melese Worku

Electrical and Computer Engineering ETDs

Next-generation wireless networks, encompassing 6G and beyond, face rigorous demands for ultra-low latency, ubiquitous connectivity, exceptionally high data rates, and robust security, necessitating innovative approaches to resource optimization and network protection. This dissertation proposes a pioneering framework that synergizes advanced methodologies—deep reinforcement learning, deep learning, blockchain, and multi-agent systems—to address these challenges. Distributed architectures, underpinned by AI-driven multi-agent systems, form the backbone of this framework, enabling seamless integration and intelligent orchestration across diverse domains. The research advances IoT-based systems leveraging machine learning for resource efficiency in healthcare applications, develops reinforcement learning-driven frameworks to optimize energy and coverage for Unmanned Aerial …

Reviving The Lost Art: Historical Foundations And Future Pathways For Bespoke Service In Luxury Retail, Andrew Burnstine

Faculty and Staff Publications & Presentations

The mid-20th century witnessed a zenith of deeply personalized, bespoke service within iconic luxury specialty retailers. This paper critically analyzes the foundational principles of this historical bespoke service model, extending Service-Dominant (S-D) Logic and retail evolution theory, to propose a robust framework for its modern revival. Employing a rigorous qualitative, multiple-case study approach, grounded in extensive historical and media analysis of four archetypal American and British luxury boutiques, and uniquely informed by an insider-ethnographic perspective on the central case exemplar ("Martha's"), the study distills six core principles: Profound Client Knowledge, Visionary Curation & Styling, Anticipatory & Proactive Service, The Exclusive …

Combination Of Irreversible Electroporation And Clostridium Novyi-Nt Bacterial Therapy For Colorectal Liver Metastasis, Zigeng Zhang, Guangbo Yu, Qiaoming Hou, Farideh Amirrad, Sha Webster, Surya M. Nauli, Jianhua Yu, Vahid Yaghmai, Aydin Eresen, Zhuoli Zhang

Pharmacy Faculty Articles and Research

Colorectal liver metastasis (CRLM) poses a significant challenge in oncology due to its high incidence and poor prognosis in unresectable cases. Current treatments, including surgical resection, systemic chemotherapy, and liver-directed therapies, often fail to effectively target hypoxic tumor regions, which are inherently more resistant to these interventions. This review examines the potential of a novel therapeutic strategy combining irreversible electroporation (IRE) ablation and Clostridium novyi-nontoxic (C. novyi-NT) bacterial therapy. IRE is a non-thermal tumor ablation technique that uses high-voltage electric pulses to create permanent nanopores in cell membranes, leading to cell death while preserving surrounding structures, and …

More Everything Forever: Ai Overlords, Space Empires, And Silicon Valley’S Crusade To Control The Fate Of Humanity, Joseph Kirby

Consensus

Becker, A. (2025). More everything forever : AI overlords, space empires, and Silicon Valley’s crusade to control the fate of humanity. Basic Books. ISBN: 9781541619593

Teaching Ai Ethics And Skepticism: The Impact Of Instruction On Ethical Usage On Students’ Perceptions, Jenna D. Justin

Journal of Practitioner Research

This study investigates the ethical implications of artificial intelligence (AI) in K-12 education, focusing on how explicit instruction influences students' perceptions of AI tools like ChatGPT. Conducted with 80 sixth-grade students at A.D. Henderson University School and FAU High School, the research tracks changes in student understanding and skepticism of AI following a World History unit. Pre- and post-instruction surveys revealed a decline in comfort with AI as students became more aware of its ethical concerns, including plagiarism, bias, and misinformation. The findings suggest integrating AI ethics into the middle school curriculum to foster responsible AI usage among students.

Securing Ai-Generated Code, Andreas E. Nelson

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

The increasing use of AI for code generation presents significant security challenges, as these tools often lack inherent security awareness and can produce vulnerable code. This paper investigates these security risks, outlining common types of vulnerabilities (such as injection flaws and improper resource handling) found in AI-generated code. It further explores and evaluates mitigation techniques aimed at im-proving code security, including model fine-tuning and adversarial strategies like Security Verifier Enhanced Neural Steering (SVEN). Findings indicate that while current methods offer promising ways to reduce vulnerabilities, ongoing research and development are crucial for the secure and responsible deployment of AI in …

Research On Policy Representation In Deep Reinforcement Learning, Zhen Chen, Zhuoyi Wu, Lin Zhang

Journal of System Simulation

Abstract: Deep reinforcement learning (DRL) has achieved remarkable success in various domains. Nevertheless, existing policy networks in DRL still face significant challenges in areas such as generalizability, multi-task adaptability, and sample efficiency. Policy representation, as a crucial research direction for enhancing DRL capabilities, aims to improve an agent's adaptability to environmental changes and novel tasks by constructing more efficient and generalizable forms of policy expression. This paper provided a concise overview of key research advances in the field of policy representation. It introduced diverse policy architectures, ranging from traditional multi-layer perceptron (MLP) -based policies to those based on pointer networks, …

Ai Project Facilitation Guidance For Research Computing And Data (Rcd) Professionals, Anna Alber, Laura Briggs, Paul Brunk, Manasvita Joshi, Atish P. Kamble, Amira Kefi, Timothy Middelkoop, Semir Sarajlic, Ana Maria Sokovic, Jeffrey N. Valdez, Ying Zhang

Administration and Staff Articles and Research

The role of Artificial Intelligence (AI) in research and education continues to rapidly grow, resulting in increased collaboration between researchers in AI and Research Computing and Data (RCD) professionals to meet the research and teaching demands. RCD professionals bridge the gap between research and technology by guiding and collaborating with researchers and educators through the process of selecting the hardware, software, and services best suited for executing their AI projects. This includes ensuring compliance with funding and regulatory requirements across the entire lifecycle of the project. In this paper, we present an overview of the AI project lifecycle and how …

A Web Application For Generating Argument Maps For Essays Using Llms, Alexis R. Chalmers

Theses

Argumentative writing is a critical skill that strengthens students’ reasoning, communication, and analytical abilities. However, maintaining a clear and organized argument structure while writing can be challenging. Argument maps — visual diagrams which explicitly show an argument’s structure — have been shown to improve students’ writing, but are rarely used outside of the planning stage of an essay due to the time and effort required to create them. Automatically generating argument maps from student essays helps students to evaluate the structure of their argument as they write and makes identifying unsupported claims visible. To evaluate whether large language models (LLMs) …

Thinking On Simulation Science And Engineering In The Era Of Artificial Intelligence, Wenhui Fan, Yuan Jiang

Journal of System Simulation

Abstract: With the rapid advancement of artificial intelligence and computing technologies, simulation technologies have leapfrogged, propelling the discipline of simulation toward greater maturity. Research progress in computer simulation technologies both in China and abroad was reviewed, and the definition and connotation of simulation were clarified. It was proposed that the Chinese terms "仿真" "仿效"and " 模拟" be unified under a single term " 仿真" with corresponding "Simulation" "Emulation" and "Analog" in English translated uniformly as "Simulation". Simulation science and engineering discipline was delineated, which was grounded in three core theoretical foundations: analogical theory,computational theory, and model validation theory. The first-level …

Research Review Of Intelligent Navigation Simulation Technology And Its Applications, Tao Liu, Hanxi Li, Yong Yin, Jialun Liu

Journal of System Simulation

Abstract:Navigation simulation develops models of ship navigation environments and behavior to simulate ship responses under various scenarios, enabling the prediction of ship behavior under complex and disturbing conditions. With the development of computer graphics, virtual reality, and artificial intelligence technologies in recent years, especially the development of unmanned ship technology, new research topics and applications have emerged in navigation simulation technology. This paper introduced the current research status and development trends of navigation simulation technology and reviewed it from three aspects: typical scenarios, key technologies, and development trends. The development trends and key points of multi-dimensional electronic navigation charts, …

Current Situation Of Simulation Course Offerings In Colleges And Universities In China And Abroad, Xiaogang Qiu, Zhengqiu Zhu, Kai Xu, Guanghong Gong

Journal of System Simulation

Abstract: As fundamental teaching units, courses serve as vital carriers of disciplinary knowledge transmission and critical bridges for converting research outcomes into educational content. The construction of simulation courses plays a pivotal role in developing the simulation discipline. The inaugural "Intelligence+ " symposium on simulation discipline and specialty construction focused on exploring the current state of simulation courses and pedagogy in China while examining future development directions. This report presented the key findings and discussions from the symposium regarding simulation courses and pedagogy. The analysis covered four parts: first, an overview of simulation course offerings and characteristics at European and …

Research On Requirements And Methods For Intelligent Assessment Of Simulation Credibility, Bingheng Wang, Tingrui Liu, Fan Yang, Huan Zhang, Wei Li, Ping Ma, Ming Yang

Journal of System Simulation

Abstract: The accuracy of simulations in representing real-world systems is a critical concern for users. Simulation credibility assessment ensures trustworthiness by evaluating the correctness and effectiveness of simulations to meet application requirements. As simulation technologies are widely adopted, and new simulation paradigms emerge, traditional assessment methods are increasingly showing limitations in their dependence on experts, data processing capabilities, and assessment efficiency. This paper systematically reviewed the research demands, current progress, new technologies, and future trends of intelligent simulation credibility assessment. Based on the simulation credibility assessment process and problem analysis, the requirements for intelligent credibility assessment were discussed. Intelligent technologies …

Optimization And Simulation Of Adaptive Production Scheduling Based On Hybrid Decision-Making Mechanism, Zhicheng Ji, Zhen Quan, Yan Wang

Journal of System Simulation

Abstract: To optimize flexible production scheduling with the objectives of the longest makespan, mean tardiness, and bottleneck machine processing load rate, a hybrid decision-making mechanism scheduling algorithm was proposed based on the decision complexity and constraint characteristics of machine assignment and task sequencing. The algorithm adopted a two-dimensional chromosome to encode machine assignment and a heuristic rule to evaluate task sequencing priority, enhancing the adaptability of the method to decision-making optimization. In order to further improve the performance of the proposed scheduling method, an adaptive rule strategy was designed based on the distribution of processing time required for waiting scheduling …

Research On Simulation Training Technology For Special Vehicle Command Based On Gesture Behavior Cognition Interaction, Xiangyang Li, Zhili Zhang, Rui Wang, Xiao Wang, Cheng Chi

Journal of System Simulation

Abstract: To overcome the practical shortcomings in the driving command training of special vehicles, a simulation training technology framework based on gesture behavior cognition and command interaction was proposed. A somatosensory interactive motion capture device and human skeleton model were used to conduct the real-time dynamic recognition and spatial coordinate transformation of the gesture actions of the command training personnel. The median filtering method was applied to eliminate the abrupt data and random noise. The spatial coordinates were processed consistently by using the human skeleton centralization and normalization method. Based on the command gesture action behavior model library and the …

Dynamic Data Driven Simulation Based On Macro-Microscopic Hierarchical Simulation Models, Xu Xie, Yuqing Ma

Journal of System Simulation

Abstract:This paper proposed a dynamic data driven simulation approach based on macro-microscopic hierarchical simulation models. This approach enabled the measurement data from the real system to affect the macroscopic simulation and microscopic simulation in sequence and made the two simulations evolve together so that it could provide decision makers with the state evolution prediction of the real system at the macroscopic level to assist decision making and provide a microscopic testbed similar to the real system, on which decision makers could deduce and evaluate their strategies. This paper established a formal description of the approach and designed a data …

Research On High-Performance Optimization Methods For Fastdds In Heterogeneous Real-Time Simulation, Congping Liu, Wei Song, Jian Fang, Fei Liu

Journal of System Simulation

Abstract:FastDDS faces limitations under high-frequency data streams, such as lock contention, performance overhead from frequent context switching, and configuration complexity of multiple nodes under strict real-time constraints, which affect the experimental efficiency. This paper proposed a performance optimization method based on a batch-scalable circular queue (BSCQ). The approach replaced the traditional mutex mechanism with a lock-free algorithm to reduce lock contention and avoid deadlocks, while batch processing improved data locality, cache hit rates, and memory utilization, effectively reducing data transmission delay and improving system throughput. Hazard pointers were introduced to ensure safe memory management during batch processing and eliminate …

Reflections On Innovative Approaches To Autonomous Simulation Software, Feng Tian

Journal of System Simulation

Abstract: Given China's weak foundation in simulation software, simply replicating the development paths of these global leading companies offers limited potential for leapfrog development. Based on an analysis of pitfalls in the independent innovation of domestic simulation software, this paper proposed and elaborated on six strategic approaches: avoiding established paths, aligning with national realities, pursuing extreme performance, leveraging special needs to advance technology, building new systems from the ground up, and embracing artificial intelligence (AI)-native architectures. These strategies aim to provide new perspectives for the independent innovation and development of domestic simulation software.

Reliability Simulation Testing And Verification Technologies For Intelligent Systems: Frontiers, Progress, And Challenges, Zili Wang, Yuntian Gao, Dezhen Yang, Yeyang Liu, Yi Ren

Journal of System Simulation

Abstract: Intelligent systems are extensively deployed in domains such as transportation, energy and water resources, smart healthcare, and aerospace, where their reliability is directly linked to public safety and social stability, thus requiring thorough and scientifically rigorous verification. This article conducted an in-depth exploration of the current state of reliability simulation and verification techniques for intelligent systems. It defined the concept of reliability specific to intelligent systems and identified key challenges they face in areas including mission scenario modeling, characteristic modeling and simulation, evaluation and verification, and simulation platforms. Future development requirements were proposed to guide research toward more trustworthy, …

Modeling And Simulation Of Complex Systems Based On Graph Neural Networks, Jinhu Lü, Hongyi Jiang, Deyuan Liu, Shaolin Tan

Journal of System Simulation

Abstract: Modeling and simulation of complex systems are critical issues for understanding their structural and functional properties. The ability of graph neural networks (GNNs) to learn and represent the internal correlations within data provides a new approach for modeling and simulating complex systems. Currently, there are various types of GNN models involving frequency-domain, spatial-domain, generative, heterogeneous, and spatio-temporal models. These models are widely applied in complex system modeling and simulation research in multiple fields such as industrial internet, social networks, and supply chains based on specific tasks and scenarios. Starting from three representative tasks: network topology representation, dynamic evolution modeling, …