Digital Commons Network^™

Pxaa: A Root-Cause Decision-Support System For Airline Passenger Complaints Using Sentiment Analysis And Complaint Classification, Adélia Acacio Da Silva, Stefani Rabelo De Campos Nunes

ICT

PXAA (Passenger Experience Analytics Assistant) is a machine learning and decision-support prototype developed to analyse airline passenger complaints using sentiment analysis and complaint classification techniques. The project follows the CRISP-DM methodology and uses airline passenger reviews collected from AirlineQuality.com through Python web scraping. Natural language processing (NLP) techniques were applied to clean and preprocess the textual data, while logistic Regression was used to classify passenger sentiment into positive, negative and neutral categories.

The project also implemented rule-based complaint classification to identify common complaint themes such as delays, baggage issues, customer service problems, comfort issues, payments/refund complaints and communication failures. These …

Comparative Economic Impact Modelling Of Floods Vs Drought On A Global Scale, Diogo Lemos Aguiar, Paulo Henrique Oliveira Machado

ICT

Climate change is generating unprecedented economic losses through extreme weather events, yet comparative predictive analysis between different hazard types remains limited. This report presents a Machine Learning–Based Comparative Economic Impact Modelling System designed to predict and compare the economic damages caused by floods and droughts on a global scale. The system integrates historical disaster records from the EM-DAT International Disaster Database with country-level socioeconomic indicators from the World Bank's World Development Indicators, applying supervised regression modelling to a unified dataset of 1,301 disaster events across 133 countries from 2000 to 2025. XGBoost and a Multi-Layer Perceptron Neural Network were evaluated …

When Relevant Isn’T Readable: Evaluating Cognitive Accessibility In Search, Kaija Frierson

2026 Research Poster Competition

According to the World Health Organization, more than one billion people worldwide experience some form of disability. Many of them may affect cognitive processing and reading comprehension rather than vision or mobility, which creates barriers when accessing written information online. Web search is a critical modern literacy skill used for learning, decision-making, and everyday problem solving, yet most search systems are designed with the assumption that all users can process complex written language equally. As a result, people with dyslexia, other cognitive disabilities, low literacy levels, or who are English language learners may struggle to access relevant information even when …

A Bayesian Late-Fusion Supportability Framework For Rare-Disease Severity Prediction In Glut1 Deficiency Syndrome, Jordan M. Rodriguez

Mathematics Dissertations

Glucose transporter type 1 deficiency syndrome (GLUT1-DS) is a rare neurometabolic disorder with heterogeneous neurological and developmental severity. Because patient-level severity is not observed as a single validated outcome, this dissertation develops a Bayesian late-fusion supportability framework for constructing and predicting an ordered latent severity phenotype from clinical, genetic, and EEG-derived evidence. The primary target was constructed in a larger clinical cohort using age-5 symptom burden and learning cognition, then assigned to an aligned multimodal prediction cohort. Target-defining variables were excluded from supervised predictors, and models were evaluated using patient-exclusive cross-validation with training-fold preprocessing and fold-wise EEG PCA.

The primary …

Identifying Mobility Hub Suitability In The Mid-Sized United States Urban Area Using Weighted Overlay And Percentile Based Local Peak Analysis, Eric Asplund

Theses and Dissertations

IDENTIFYING MOBILITY HUB SUITABILITY IN THE MID-SIZED UNITED STATES URBAN AREA USING WEIGHTED OVERLAY AND PERCENTILE‑BASED LOCAL PEAK ANALYSIS

By Eric Asplund

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Urban and Regional Planning at Virginia Commonwealth University.

Virginia Commonwealth University, 2026.

Major Director: Dr. Ivan Suen, Ph. D., Associate Professor, Faculty of Urban and Regional Studies and Planning

Shared mobility hubs are increasingly posited in transportation planning as interventions supporting multimodality, sustainability, and equitable access, but guidance on evaluation of potential hub locations remains uneven, particularly in mid-sized United States cities where …

Artificial Elections: How Artificial Intelligence Can Affect Elections And Decide Our Future, Ryan D. Granger

Honors Undergraduate Theses

This thesis examines the growing role of artificial intelligence (AI) in democratic elections, highlighting both its transformative potential and its associated risks. Drawing on a qualitative analysis of existing literature, the study explores how AI is increasingly integrated into political campaigns, election administration, and voter engagement. Key benefits include enhanced data analysis, personalized political messaging, and improved efficiency in campaign operations. AI also supports real-time fact-checking and more accurate vote tabulation, which can strengthen transparency and trust in electoral processes. However, the thesis emphasizes that these advantages are accompanied by significant challenges. AI technologies enable the rapid creation and dissemination …

Hybrid Patchtst And Physics-Based Framework For Predicting Lithium-Ion Battery State Of Health, Pavan Ravuri

Honors Undergraduate Theses

Accurately forecasting the state of health of lithium-ion batteries is critical for improving performance, reliability and lifetime in energy storage applications. Battery capacity degrades into a nonlinear pattern over cycling due to electrochemical processes where neither purely data driven nor physics-based models can capture alone. This study looks at a hybrid framework combining that PatchTST patch-based transformer architecture with physics-based features derived from the solid electrolyte interphase and pseudo two-dimensional models. Physics inspired proxy features were computed from cycling data and concatenated with electrochemical measurements as added input channels before patch segmentation. There are five model configurations that were evaluated …

The Role Of Resistance Training In Managing Menopause-Related Changes, Alyson K. Strauss

Honors Undergraduate Theses

Resistance training serves as a critical non-pharmacological strategy for mitigating the physiological and psychological declines associated with the menopausal transition. This natural biological phase, marked by declining estrogen levels, typically leads to accelerated bone mineral density loss, sarcopenia, metabolic regulation disturbance, and heightened mood disturbances. To evaluate the efficacy of targeted exercise interventions, a systematic review was conducted following PRISMA guidelines. From an initial pool of 60 relevant articles, 13 studies met the specific inclusion criteria, focusing on structured resistance training protocols for perimenopausal, menopausal, and postmenopausal cohorts.

The synthesis of these studies indicates that resistance training is a highly …

Lie-Galois Theory, Giovanni Reed

Honors Undergraduate Theses

Differential equations are a much-studied topic in the field of mathematics, as well as other sciences, such as engineering, economics, and biology. While much is known concerning these, there is still a large gap in our knowledge about such equations. It is important therefore, both to mathematics and other sciences, that we gain a more complete knowledge of differential equations, in particular the nature of their solutions. In this research, we investigate the solution space of linear ordinary differential equations (ODEs) from the standpoint of differential algebra. Differential algebra allows an ODE to be treated similarly to a polynomial, allowing …

Dominating Hadwiger's Conjecture For 2k2-Free Graphs, Thomas Tibbetts

Honors Undergraduate Theses

A dominating K_t minor in a graph �� is a sequence (��₁,…,��_t) of pairwise disjoint non-empty connected subgraphs of ��, such that for 1≤��< ��≤��, every vertex in ��_j has a neighbor in ��_i. Replacing “every vertex in ��_j” by “some vertex in ��_j” retrieves the standard definition of a ��_t minor. The strengthened notion was introduced by Illingworth and Wood in 2024, who asked whether every graph with chromatic number �� contains a dominating ��_t minor. This is a substantial strengthening of the celebrated Hadwiger’s Conjecture, which asserts that every …

Balancing Evidence Recovery: Hexane’S Effect On Biological And Non-Biological Traces In Sexual Assault Kits, Maya Richards

Honors Undergraduate Theses

This study investigated the effectiveness of solvent – based extraction methods for the recovery of lubricant residues from simulated sexual assault swabs, with emphasis on balancing chemical evidence recovery. Sequential extraction using ultra – pure water followed by organic solvents, hexane, dichloromethane (DCM), and ethyl acetate (EA), was evaluated across water – based, silicone – based, oil – based, and hygiene product matrices. Analysis was performed using Direct Analysis in Real Time – High Resolution Mass Spectrometry (DART – HRMS), and extraction efficiency was assessed through spectral correlation with neat reference samples. Results demonstrated that solvent performance was highly dependent …

From Lap To Map: How Musical Scale, Place, And Play Drive The Interconnected Mario Kart World, Cameron Cummins

Honors Undergraduate Theses

With their deserts, castles, and ghost houses, the environments of Super Mario games are colorful, whimsical, and charming, but why are they so compelling, and what happens when our analysis of these environments extends beyond individual levels to expansive game worlds? Drawing on Cresswell’s theory of place (2014) and recent work on musical place-building in Mario Kart 8 (Heazlewood-Dale, 2024), I propose a spectrum between localized and globalized scale in games. As game environments become increasingly globalized, the music may be similarly altered to account for this shift in scale. Consequently, players may then encounter a broader, less musically congruent …

To Print A Remake: An Analysis Of Hollywood Remakes And Their Cultural Value, Connor F. Seaton

Honors Undergraduate Theses

In recent years, audiences and movie critics have expressed concern that Hollywood’s growing reliance on remakes, sequels, franchises, and similar adaptations has led to a broader worry that originality is fading from modern cinema and that the industry is instead focused on using adaptations to maximize profits. Although adaptation is often seen as a commercially driven framework for reproducing existing intellectual property in a new media format, this thesis argues that it should be recognized as an autonomous cultural category with its own artistic, historical, and social significance and merit. By analyzing adaptation scholarship and reviewing its complex historical development, …

Evaluating The Comparison Of Different Body Locations For Data Collection Using Galvanic Skin Response Of Forensic Chemistry Students When In Lecture And Laboratory Environments, Elisa Guerrero

Honors Undergraduate Theses

Anxiety is the response that humans can have when exposed to a stimulus. It can be caused by a multitude of factors, such as psychological, biological, or physiological stimuli. Anxiety can be presented as a state or as a trait in a person. Trait anxiety is typically seen as a more stable personality trait, whereas state anxiety is a more intense temporary emotional state. In our study, anxiety was regarded as a more state-like characteristic. A galvanic skin response (GSR) Shimmer3+ device can measure skin electro-conductance changes following stimulation. In a situation where a participant is experiencing anxiety, levels of …

Hybrid Model For Phishing Website Detection Using Transfer Learning, Atul Dubal, Mansi Subhedar, Santosh Dhamala, Manasi Patil

ASEAN Journal on Science and Technology for Development

The rapid digitization of human activities has intensified reliance on internet-based platforms, creating fertile ground for cybercriminal exploits such as phishing. Despite advancements in detection mechanisms, phishing attacks continue to evolve, leveraging sophisticated visual mimicry to deceive users. This paper proposes a robust vision-based phishing detection system using ensemble deep learning to analyse webpage screenshots. The framework integrates transfer learning with pre-trained VGG16 and DenseNet121 models, extracting complementary low-level texture features (edges, gradients) and high-level hierarchical patterns (logos, layouts). These features are fused through a custom classifier with dropout regularization to mitigate overfitting. A balanced dataset of 3,000 webpage screenshots …

Generative Ai Peer Tutoring To Support Peer-Reviewed Source Identification And Evaluation, Rae Mair, Michelle Kelley, Taylar Wenzel, Andrea C. Borowczak, Yuqing Li, Mike Borowczak

Faculty Scholarship and Creative Works

This chapter explores the design and evaluation of a generative artificial intelligence peer tutor prompt to support college students in identifying and evaluating peer-reviewed sources for academic research. Grounded in literature on peer tutoring, Socratic dialogue, and AI-supported learning, the authors describe an iterative prompt engineering process designed to transform large language models (LLMs) into Socratic-style peer tutors capable of scaffolding student reasoning without completing tasks for them. Five guiding criteria for an effective peer tutor shaped development and evaluation: cognitive congruence, step-by-step guidance, avoiding giving answers, adaptability to student level, metacognitive transparency, and following assignment directions. Across multiple human-centered …

Visual Analytics For Interpretable Quantum Computing, Shaolun Ruan

Dissertations and Theses Collection (Open Access)

Quantum computing has entered a stage of increasing practicality. Many quantum hardware vendors such as IBM, Rigetti, Honeywell, and IonQ now enable experiments on real devices in the Noisy Intermediate-Scale Quantum (NISQ) era. These platforms show computational advantages in domains such as optimization, machine learning, and materials science. However, they remain limited by hardware noise and the absence of human-interpretable information. Existing visual metaphors, such as the Bloch Sphere for single-qubit states or circuit schematics for algorithm design, struggle to convey multi-qubit entanglement or measurement probabilities in ways accessible to human reasoning. Likewise, the rise of variational quantum circuits and …

Constrained Reinforcement Learning: From Single-Agent Safety To Multi-Agent Coordination, Hao Jiang

Dissertations and Theses Collection (Open Access)

Real-world decision-making systems such as autonomous driving and largescale ride-pooling must operate under strict safety and resource constraints. Traditional Reinforcement Learning (RL) methods, while powerful in simulation, often fail to guarantee such constraints, limiting their real-world deployment. The fundamental challenge lies in integrating constraint satisfaction with long-term reward optimization, especially when outcomes are stochastic and interdependent across multiple agents.

This dissertation advances the field of Constrained Reinforcement Learning (CRL) from both single-agent safety and multi-agent coordination perspectives. In the single-agent setting, we introduce a Reward Penalty framework that augments the state space with cumulative cost and penalizes only trajectories that …

Ai-Powered Lawyering: Ai Reasoning Models, Retrieval Augmented Generation, And The Future Of Legal Practice, Daniel Schwarcz, Sam Manning, J. J. Prescott, Patrick Barry, David R. Cleveland, Beverly Rich

Articles

Generative AI is set to transform the legal profession, though its most promising uses and ultimate effects are still unclear. While AI models like GPT-4 improve efficiency, they can also “hallucinate” and may undermine legal judgment, particularly in complex tasks typically handled by skilled lawyers. This article examines two emerging AI innovations that may mitigate these concerns: Retrieval Augmented Generation (RAG), which grounds AI-powered analysis in legal sources, and AI reasoning models, which structure complex reasoning before generating output. We conduct the first randomized controlled trial assessing these technologies, assigning upper-level law students to complete legal tasks using a RAG-powered …

A Novel Federated Llm Framework For Distributed Traffic Modelling In Intelligent Transportation Systems, Seerat Kaur

Theses and Dissertations (Comprehensive)

Intelligent transportation systems (ITS) depend on accurate traffic prediction to support congestion management, infrastructure planning, and real-time operational decisions. Despite substantial progress in data-driven forecasting, several challenges continue to limit practical deployment: traffic data is distributed across independent regional authorities, making centralized aggregation infeasible, standard federated aggregation strategies ignore traffic-specific characteristics that meaningfully affect model quality, and existing models produce only numerical outputs without interpretable reasoning that urban planners can act upon. This thesis addresses these challenges through four contributions that collectively advance privacy-preserving, explainable, and scalable traffic forecasting.

The first contribution provides a systematic review of 129 peer-reviewed publications, …

Real-Time Isolated Asl Recognition: Evaluating Spatial-Temporal Networks And Multimodal Llms, Raga Mouni Batchu

West Chester University Graduate Theses, Dissertations, and Final Projects

This thesis investigates the deployment of high-accuracy Isolated ASL Recognition (ISLR) in resource-constrained edge environments. We train a lightweight Spatio-Temporal Attention Network (SSTAN,∼2.7 M parameters,∼10 MB) on the WLASL-100 benchmark, achieving 75.25% Top-1 and 88.24% Top-5 accuracy with 139 ms CPU-only inference. A systematic comparison against frontier multimodal LLMs (Gemini 3 Flash, Gemini 3.1 Pro, Qwen 3 VL) shows SSTAN outperforms the best LLM baseline by∼1.85×in accuracy while being 22–230×faster and up to 40×cheaper annually. The LLMs’ core limitation is a lack of fine-grained temporal perception; they impose English-language semantic priors rather than learning the articulatory distinctions that define ASL …

Polysemicolon; Novice Programmers And Java Keywords, Briana C. Bettin

Michigan Tech Publications

Most industrial programming languages leverage the English language for reserved keywords – words which a program compiler recognizes as specific execution commands. The divide between expert and novice programmers showcases an intriguing middle-ground by which the polysemy of many keywords becomes revealed. This essay explores a sampling of the multitude of keyword interpretations that a novice programmer may derive from the Java language’s syntactic style and keywords specifically, and how the polysemy of both “English” and “code” meanings to these terms affects the novice-expert programmer transition.The transition from novice to expert, and the mapping of the career of metaphor to …

Chatgpt In Secondary English Language Learning: Educators' And Students' Literacy, Perceptions, And Experiences, Victoria Brenes, Sierra Outerbridge, Xinyue Ren

STEMPS Faculty Publications

The increasing use of generative artificial intelligence (GenAI) has shown the potential of transforming teaching and learning practices in various educational settings, such as in English language learning (ELL). As English language learners (ELLs) often experience many challenges and barriers in schools in the United States, it is urgent to leverage the educational affordances of GenAI in fostering the effectiveness of ELL. Given the limited research investigating GenAI adoption, especially ChatGPT literacy within K-12 ELL, this convergent mixed methods research aims to investigate students' and teachers’ perceptions of using ChatGPT and their ChatGPT literacy in secondary ELL contexts. We will …

A Quantitative Analysis Of Burrowing And Subterranean Locomotion In The Sand Lance Using A Transparent Sediment, Issei Fujita, Makoto Tomiyasu, Jun Yamamoto, Yasuzumi Fujimori

Journal of Marine Science and Technology–Taiwan

Sand lances (Ammodytes spp.) rely on rapid burrowing into sediment for predator avoidance. Although their sediment grain-size preferences are well documented, the biomechanics underlying burrowing success remain unclear because natural substrates are opaque. Using a transparent-sediment system, we directly visualized and quantified burrowing kinematics to: (1) test the effect of body size (total length, TL) on burrowing success; (2) examine entry mechanisms (e.g., swimming speed, entry angle); and (3) describe locomotion within sediment. Logistic regression on the full dataset (N = 28 fish) identified TL as the primary determinant of success, with larger individuals exhibiting significantly higher success …

Examining The Spatial Variability Of Summer Heat In New York City Using Landsat Land Surface Temperature, Ryan Kabir

Dissertations and Theses

Extreme heat is the leading weather-related cause of death in the United States, and in New York City (NYC) approximately 370 heat-related deaths occur annually on average. The spatial distribution of surface heat exposure across NYC is highly uneven, low-income neighborhoods and communities of color consistently face higher temperatures, yet the intraurban spatial structure of land surface temperature (LST) during heatwave events has not been systematically characterized at high resolution. The New York City Panel on Climate Change Fourth Assessment (NPCC4) identifies this as an explicit research gap, noting that its own projections, based on global climate models downscaled to …

กฎหมายการหมุนเวียนทรัพยากรพลาสติกของญี่ปุ่น : พัฒนาการ กลไกเชิงสถาบัน และบทเรียนเชิงนโยบายต่อประเทศไทย, พรนภัส สุวลักษณ์ วอห์น, สุจิตรา วาสนาดำรงดี

Thai Environment

บทความนี้มีวัตถุประสงค์เพื่อวิเคราะห์พัฒนาการของกฎหมายญี่ปุ่นด้านการจัดการพลาสติกโดยมุ่งเน้นพระราชบัญญัติส่งเสริมการหมุนเวียนทรัพยากรพลาสติก ค.ศ. 2021 (Act on Promotion of Resource Circulation for Plastics) และกฎหมายที่เกี่ยวข้อง การศึกษานี้ใช้การวิเคราะห์เชิงเอกสารภายใต้กรอบแนวคิดเศรษฐกิจหมุนเวียน (Circular economy) หลักลำดับขั้นการจัดการขยะ (Waste hierarchy) และแนวคิดการจัดการตลอดวัฏจักรชีวิตผลิตภัณฑ์ (Life-cycle approach) เพื่ออธิบายโครงสร้างและกลไกทางกฎหมายในการกำกับการหมุนเวียนทรัพยากรพลาสติก

ผลการศึกษาพบว่า ระบบกฎหมายของญี่ปุ่นได้พัฒนาจากการกำกับการจัดการของเสียแบบปลายทางไปสู่การกำกับการหมุนเวียนทรัพยากรตลอดวัฏจักรชีวิตผลิตภัณฑ์ โดยบูรณาการมาตรการด้านการออกแบบผลิตภัณฑ์ การลดการใช้พลาสติกแบบใช้ครั้งเดียว การจำหน่าย การเก็บรวบรวม การคัดแยกและการรีไซเคิลไว้ภายใต้กรอบกฎหมายเดียวกัน จุดเด่นของระบบกฎหมายญี่ปุ่นอยู่ที่การกำหนดบทบาทของรัฐ องค์กรปกครองส่วนท้องถิ่น ผู้ประกอบการ และประชาชนอย่างเป็นระบบในลักษณะการกำกับหลายระดับ รวมทั้งการใช้มาตรการทางกฎหมายควบคู่กับแรงจูงใจทางเศรษฐศาสตร์และการปรับเปลี่ยนพฤติกรรมของผู้บริโภค ซึ่งสะท้อนแนวปฏิบัติที่ดีในการขับเคลื่อนเศรษฐกิจหมุนเวียนด้านพลาสติก อย่างไรก็ตาม ผลการศึกษายังพบข้อจำกัดบางประการของระบบญี่ปุ่น เช่น การพึ่งพาการเผาขยะเพื่อผลิตพลังงานในสัดส่วนสูงและความท้าทายในการลดการใช้พลาสติกอย่างเป็นรูปธรรม

ข้อค้นพบจากการศึกษานำไปสู่ข้อเสนอแนะสำหรับประเทศไทยว่า ควรมีการพัฒนากฎหมายแม่บทด้านเศรษฐกิจหมุนเวียน การพัฒนาระบบความรับผิดชอบที่เพิ่มขึ้นของผู้ผลิต (Extended Producer Responsibility: EPR) การกำหนดมาตรฐานการออกแบบเชิงนิเวศ (Eco-design) การพัฒนาโครงสร้างพื้นฐานด้านการคัดแยกและรีไซเคิลและการจัดทำระบบข้อมูลเพื่อสนับสนุนการกำกับการหมุนเวียนทรัพยากรอย่างยั่งยืน

Automatic Generation Of Introductory Programming Exercises With Large Language Models, Nguyen Binh Duong Ta, Hua Gia Phuc Nguyen, Gottipati Swapna

Research Collection School Of Computing and Information Systems

Despite recent advances in code generation made possible by large language models (LLMs), programming is still an essential skill that computing students need to master now and in the foreseeable future. In learning programming, frequent practices with exercises set at an appropriate difficulty and knowledge level is of crucial importance for students. However, it’s not a trivial task for instructors to create many good quality exercises customized for each student. Programming problems found on Internet sources such as LeetCode are mostly too challenging for novice programmers with no prior coding knowledge. Recent work in AI-enabled education has been leveraging LLMs …

Choice-Based Crowdshipping For Next-Day Delivery Services: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra

Research Collection School Of Computing and Information Systems

This paper studies integrating the crowd workforce into next-day home delivery services. In this setting, both crowd drivers and contract drivers collaborate in making deliveries. Crowd drivers have limited capacity and can choose not to deliver if the presented tasks do not align with their preferences. The central question addressed is: How can the platform minimize the total task fulfilment cost, which includes payouts to crowd drivers and additional payouts to contract drivers for delivering the unselected tasks by customizing task displays to crowd drivers? To tackle this problem, we formulate it as a finite-horizon Stochastic Decision Problem, capturing crowd …

Can We Read Ai’S Mind? A Quest For Transparency, Santhosh Kumar Ravindran, Estera Kot, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Although Artificial Intelligence (AI) systems are playing an increasing role in critical domains such as healthcare, finance, and autonomous systems, their decision-making processes remain largely opaque. This paper examines the challenges of AI transparency, addressing the “black box” problem using Explainable AI (XAI) techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It also examines the ethical, regulatory, and societal implications of AI opacity and proposes a Comprehensive AI Observability (CAO) Framework that integrates deep explainability, provenance tracking, and real-time monitoring to enhance AI accountability. By bridging technical solutions with governance structures, this research emphasizes the …

Actor-Critic For Continuous Action Chunks: A Reinforcement Learning Framework For Long-Horizon Robotic Manipulation With Sparse Reward, Jiarui Yang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Existing reinforcement learning (RL) methods struggle with long-horizon robotic manipulation tasks, particularly those involving sparse rewards. While action chunking is a promising paradigm for robotic manipulation, using RL to directly learn continuous action chunks in a stable and data-efficient manner remains a critical challenge. This paper introduces AC3 (Actor-Critic for Continuous Chunks), a novel RL framework that learns to generate high-dimensional, continuous action sequences. To make this learning process stable and dataefficient, AC3 incorporates targeted stabilization mechanisms for both the actor and the critic. First, to ensure reliable policy improvement, the actor is trained with an asymmetric update rule, learning …