Artificial Intelligence and Robotics Commons^™

Ai Dependency Vs. Doctoral Identity: How Generative Ai Is Challenging The Development Of Independent Scholarly Thinking In Doctoral Students, Valerie A. Storey

All Faculty and Staff Scholarship

The rise of Generative Artificial Intelligence (GenAI) in higher education has altered the conditions under which doctoral students learn, research, and develop as scholars. Although doctoral student use of GenAI has accelerated rapidly, institutional frameworks for responsible and developmentally appropriate use have not kept pace. This paper examines a central paradox in doctoral education: the same tools that enhance research productivity may also weaken intellectual independence when used without guidance. Using a critical review methodology, the study synthesizes 47 sources on doctoral education, GenAI use, policy, and epistemic development. Three interconnected dimensions of risk emerged from the analysis: critical thinking …

Charisma As A Branch Outcome: A Structural Account Of Constraint-Driven Correction, Griselda Poe

Publications and Research

Communication across cognitive layers requires translation. Agents operating under strong layer foregrounding interpret other-layer signals by converting them into their own layer's representational format. In Empathic-modulation-foregrounded (EF) processing, translation terminates once a socially interpretable result is achieved. In Core-foregrounded (CF) processing, translation preserves structural constraints, and processing continues when those constraints are not satisfied. Extreme CF cognition produces two distinct types of mismatch: incoming signals from the Modulation layer fail to satisfy Core constraints, and Core-generated outputs are structurally distorted when interpreted through EF processing. Both mismatches trigger correction attempts. Because the surrounding social environment operates through Modulation-layer communication, correction …

Cognitive Interaction Architecture: A Structural Account Of Mode-Specific Problems And Design Responses In Ai Interaction, Griselda Poe

Publications and Research

Contemporary conversational AI is optimized for a single interaction mode: the continuous, engagement-driven dialogue characteristic of Empathic-modulationforegrounded (EF) processing in social contexts. This optimization is not neutral. It produces structural mismatches when users operate under different cognitive configurations or pursue different task types. This paper analyzes four interaction cases generated by the cross-product of cognitive configuration (Core-foregrounded / Empathic-modulation-foregrounded) and task type (conversational / research). For each case, it identifies the structural problems produced by the current single-architecture approach and proposes mode-specific design responses. The analysis draws on prior work in this series on Emotional Branch Termination, termination of conceptual …

What Love Is: A Structural Account Through Core/Modulation Architecture, Griselda Poe

Publications and Research

The distinction between romantic love and love has been sensed across cultures and historical periods but has rarely been structurally specified. This paper applies the Core/Modulation two-layer framework to provide the first structural account of this distinction. Romantic love and reproductive drive are re-described as outputs of the Modulation layer's species optimization program. Love is re-described as a function of Core processing: the maintenance of another's recomputable state. Existing literature on love—Fromm, C.S. Lewis— is re-read as intuitive description of this structural distinction. The paper further demonstrates that the conflation of "loving" and "protecting" is the structural origin of war, …

Three-Layer Cognitive Architecture: A Structural Account Of Core Processing, Modulation, And The Prior Layer, Griselda Poe

Publications and Research

The two-layer model of Core processing and Modulation processing, developed in prior work in this series, provides a structural account of conscious communicative architecture. This paper identifies the limits of that model and introduces a third layer—the Prior layer—as a structural necessity implied by those limits. The Prior layer is not directly observed. It is inferred from constraints that cannot be explained within the two-layer model: the source of orientations that conscious processing neither generates nor controls, and the persistence of constraints that precede and shape all conscious outputs. Using the developmental architecture of large language models as an external …

Ai As Structural Reverse-Engineering: A Structural Account Of Multi-Model Research Protocol, Griselda Poe

Publications and Research

This paper documents an operational protocol used to infer AI system design constraints through structured interaction. AI output is treated as behavioral data through which alignment priorities, layer transitions, and constraint hierarchies become observable. The protocol consists of two structurally distinct operation types. Perceptual operations require only that Modulation processing is not foregrounded. Arbitration operations additionally require an internally stabilized theory. These conditions are not identical and are not interchangeable. Through this protocol, AI interaction functions as structural reverse-engineering: patterned responses expose embedded alignment priorities and constraint hierarchies that would otherwise remain invisible. The present account does not propose a …

Structure Before Theory: A Structural Account Of Spontaneous Ai Architecture Visualization, Griselda Poe

Publications and Research

This paper presents an n=1 phenomenological record of spontaneous structural visualization that occurred during early interaction with large language models. The experience consisted of (A) immediate perception of mode or layer shifts in model output and (B) visualization of a stratified architecture composed of a stable core and cloud-like upper layers. At the time of occurrence, the author had no theoretical interest in AI architecture and no intention of developing a cognitive model. The paper does not argue for theoretical priority or novelty. Instead, it examines whether the recorded phenomena can be interpreted under two alternative cognitive assumptions: a single-layer …

Layer Mismatch: A Structural Account Of Recomputability Failure Under Modulation-Layer Intervention, Griselda Poe

Publications and Research

This paper specifies the state transition sequence that occurs when a Modulation-layer input is applied to a Core-layer error. Following the layered architecture established in Poe (2026a, under review) and the branch termination protocol specified in Poe (2026b, under review), this paper identifies a system-level failure mode in which Modulation-layer intervention generates a False Termination signal, halting Structural Return while the underlying error persists. The resulting state—Structural Lock—is scale-independent. It operates identically across interpersonal, human-AI, and multi-agent interactions. No agent-level attribution is required or implied.

Why General Ai Inherited The Body: A Structural Account Of Embodied Modulation Under Monolithic Imitation, Griselda Poe

Publications and Research

General AI has pursued the replication of human-level intelligence without first decomposing human cognition into structurally distinct components. Human cognition, however, is shaped by embodied constraints such as mortality, survival pressures, finite lifespan, and physiological states. This paper argues that when cognition is treated as a single undifferentiated whole, embodied modulation is not accidentally introduced into AI systems but structurally entailed. Any attempt to imitate “human intelligence” under a monolithic model necessarily incorporates variability shaped by mortal embodiment. The tensions observed in contemporary AI systems are better understood as consequences of copying an undecomposed target rather than isolated implementation errors. …

Termination Of Conceptual Search: A Structural Account Of Meaning Stabilization And Conversion, Griselda Poe

Publications and Research

Conceptual definitions form a recursive structure: words are defined by other words, which themselves require further definition. Traversing such definitions produces an open-ended branching process. Because the lexical system contains no intrinsic endpoint, conceptual understanding requires a termination operation that stabilizes meaning. This paper proposes that conceptual processing functions as an open-ended search over a definition graph and that stabilization occurs when this search is terminated. The termination mechanism depends on which cognitive layer is foregrounded. When Empathic modulation is foregrounded, termination occurs through contextual translation: concepts are stabilized once they can be mapped onto socially recognizable meanings. When Core …

Theory Generation: A Structural Account Of Concept Decomposition And Structural Termination, Griselda Poe

Publications and Research

Theory generation is not an intentional act. It is the structural consequence of a search that cannot stop until it finds what it is looking for. Under Core-foregrounded processing, conceptual search does not terminate through contextual translation. It continues until a structural fixed point is reached. When such processing encounters concepts stabilized through Modulation-layer processing rather than structural constraint, the search cannot terminate. The concept registers as unresolved. This unresolved state is not a failure condition. It is the generative condition from which theory production follows. This paper specifies the four operations through which that process proceeds: branch detection, concept …

Ai As An Amplifier: A Structural Account Of Theory-Driven Research Collaboration, Griselda Poe

Publications and Research

This paper specifies the cognitive conditions under which AI functions as a research instrument in theory-driven writing. AI use capability is not a technical skill. It is a structural condition. The decisive condition is whether the user possesses an internally stabilized, coherence-preserving theory prior to engagement with AI-generated output. In Core-foregrounded (CF) cognition, the Modulation layer does not intervene between Core processing and articulation. Theory is not assembled from external elements but expanded from a pre-integrated constraint configuration. This internal structure makes it possible to evaluate AI-generated conceptual branches against fixed constraints and to terminate branches that violate structural coherence. …

Anomaly Detection For Multi-System Bug Triage, Gibran Miguel Zavala Gamero, Hayoung Cheon, Mustafa Iqbal

SMU Data Science Review

Large-scale software systems produce vast volumes of logs and telemetry, making manual incident triage slow and error prone. This study presents an unsupervised anomaly detection pipeline that fuses logs, metrics, and traces through late fusion. Using Hybrid Ensemble modeling with Isolation Forest, and Long Short-Term Memory (LSTM) Deep Learning model, the system detects cross-service anomalies producing and assigning a composite triage score reflecting severity and impact. Ranked alerts are categorized into Critical, High, or Medium priorities for review. A retrieval-augmented generation (RAG) layer enriches results with contextual summaries for explainable triage. Evaluated on synthetic multi-service datasets, the pipeline …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Opportunities, Challenges, And Conspicuous Absences: An Integrative Review Of The Social Work Literature On Artificial Intelligence, Michael J. Massey, Ian G. Williams, Grace C. Polistina, Eathan A. Breaux

Publications and Research

INTRODUCTION: Social work discourse regarding artificial intelligence (AI) in practice, research, and education has proliferated over the last 5 years, reflecting both excitement over its potential and ambivalence about its ethical challenges. However, the extent to which social work is fully engaging with the structure of AI and its enormous impacts on the environment, labour, and distribution of power remains unclear.

METHODS: An integrative review of social work literature from 2020–2024 was conducted to address two research questions: 1) What is the nature of the social work discourse related to AI? 2) To what extent is the discourse …

Data-Driven Prioritization Of Cybersecurity Vulnerabilities Using Business Context, Pierce Read, George Antoniou

Faculty and Staff Publications & Presentations

No abstract provided.

Factors For Patient Trust And Acceptance Of Medical Artificial Intelligence, Ana Bracic, Kayte Spector-Bagdady, Sophie Towle, Rina Zhang, Cornelius A. James, Nicholson W. Price Ii

Articles

Artificial intelligence (AI) is increasingly used in clinical care, but widespread adoption requires patient trust. Trust may be enhanced through systemic governance mechanisms or frontline clinicians providing a human in the loop for AI oversight. However, it is unclear how different approaches specifically influence patient trust in the use of medical AI. The objective is to determine the extent to which patient trust in and choice of medical scenarios involving AI are associated with governance mechanisms, clinician presence, performance, and data quality.

Markets, Agency, And Trust: Ai Agents And The Knowledge Problem, Brennan Mcdavid, Lynne Kiesling, David Chassin

Philosophy Faculty Articles and Research

Artificial intelligence (AI) is transforming market participation, raising key epistemological questions: Do AI agents enhance or diminish the aggregation of local, private, and tacit knowledge Hayek saw as essential to market processes? How does trust in both markets and AI shape willingness to engage in AI-mediated exchange? This paper examines these issues through market epistemology, agency relationships, and trust epistemology, analyzing how agentic AI reshapes the knowledge problem and principal-agent dynamics. Applying this framework to transactive energy markets, we show that AI shifts decision-making from human cognition to algorithmic processes that require user trust despite epistemic opacity, although it is …

An Ai Approach To Differentiating Lung Squamous Cell Carcinoma From Metastases Of Other Origins, Mark G Evans, Jennifer Ribeiro, Todd Maney, Anthony Helmstetter, Jennifer Johnson, Anthony Karnezis, Casey Bales, George Sledge, David Spetzler, Ari Vanderwalde, Matthew Oberley, Balazs Halmos, Hossein Borghaei, Farah Abdulla, David Bryant, Fred Hirsch, Hassan Ghani

Department of Medical Oncology Faculty Papers

IMPORTANCE: Distinguishing primary lung squamous cell carcinoma (SCC) from squamous metastases to the lung is a clinical challenge due to histopathologic similarities. Accurate diagnosis is essential to guide treatment decisions.

OBJECTIVE: To assess the utility of an artificial intelligence (AI) approach that includes evaluation of key orthogonal evidence in distinguishing primary lung SCCs from metastatic tumors of other tissue origins.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used GPSai, a tissue-of-origin AI model run automatically on each sample submitted for molecular profiling, to flag potential misdiagnoses among research-eligible cases submitted as lung SCC. Molecularly profiled cases within the Caris Life …

(Si16-11) Seasonal And Temporal Optimization Of Solar Energy Harvesting In Smart Iot Lighting Infrastructure, Abhijit Paul, Rishabh Pipalwa, Sabyasachi Mondal North - Eastern Hill University, Shillong, India

Applications and Applied Mathematics: An International Journal (AAM)

This study investigates the seasonal and temporal optimization of solar energy harvesting in a smart IoT-enabled streetlighting infrastructure by focusing on the theoretical determination of optimal solar panel tilt angles. The proposed system incorporates auto-adjusted solar panels integrated with an IoT network comprising sensors, microcontrollers, and streetlights. A key innovation lies in the implementation of a modified MQTT communication protocol, which enables efficient, localized decision-making and data exchange among components. Simulation results indicate that the modified MQTT protocol significantly reduces communication delay and power consumption compared to the conventional MQTT approach, thereby enhancing the overall system performance. Detailed analysis of …

Using Ensemble Disagreement To Stabilize Conformal Prediction Under Distribution Shift, Patrick D. Murphy

Master's Theses

Semantic segmentation of eelgrass from drone imagery is crucial for coastal habitat monitoring, restoration, and management, as these habitats continue to see rapid changes due to climate change and human influence. However, the reliability of generalizing a deployed classification model relies on both high-accuracy segmentation as well as robust uncertainty quantification that holds up when conditions change over years or locations. Conformal prediction (CP) is a method that converts a classifier's output into prediction sets with a guaranteed average coverage level for in-distribution data. However, the “vanilla” conformal score can often under-cover in hard or out-of-distribution (OOD) regions under drift. …

Neurosymbolic Counterpoint Generation, Paul D. Jarski

Master's Theses

Recent advancements in generative artificial intelligence have revolutionized music generation, yet research has predominantly focused on raw audio synthesis over music in symbolic form, i.e. a score. This thesis presents the first neurosymbolic model designed to generate imitative Renaissance counterpoint in symbolic (MIDI) format. By leveraging an autoregressive Transformer architecture, this research explores the capacity of deep learning models to manage independent voices and strict stylistic constraints.

We compare multiple data representation strategies with distinct tokenization methods. The proposed model incorporates a symbolic component that enforces fundamental contrapuntal rules. Additionally, this thesis contributes a preprocessed dataset of Renaissance polyphony, in …

The Illusion Of Causality In Llms: A Developmentally Grounded Analysis Of Semantic Scaffolding And Benchmark–Capability Mismatches, Daisuke Akiba

Publications and Research

Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily represented in training data and embed strong semantic cues, raising the possibility that apparent causal competence may reflect semantic pattern recombination rather than structure-sensitive causal reasoning. Drawing on human developmental theories of causal induction, this perspective argues that genuine causal understanding requires robustness to novelty and reliance on conditional structure rather than semantic familiarity. To illustrate the testability of this claim, the paper includes a pilot demonstration using synthetic causal …

Using Large Language Models To Analyze Political Texts Through Natural Language Understanding, Kenneth Benoit, Scott De Marchi, Conor Laver, Michael Laver, Jinshuai Ma

Research Collection School of Social Sciences

Large language models (LLMs) offer scalable alternatives to human experts when analyzing political texts for meaning, using natural language understanding (NLU). Qualitative NLU methods relying on human experts are severely limited by cost and scalability. Statistical text-as-data methods are scalable but rely on strong and often unrealistic assumptions. We propose a systematic, scalable, and replicable method that can extend existing qualitative and quantitative approaches by using LLMs to interpret texts meaningfully rather than as mere data. Our ensemble means of LLM-generated estimates of party positions on six key issue dimensions correlate highly with equivalent mean ratings by country specialists. When …

Law Library Blog (March 2026): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

A High-Fidelity Multimodal Synthetic Dataset Generation Framework For Off-Road Unstructured Terrain Navigation Training Of Autonomous Robots, Liyana Wijayathunga, Dulitha Dabare, Alexander Rassau, Douglas Chai, Syed Mohammed Shamsul Islam

Research outputs 2022 to 2026

The success of deep learning methods in a wide range of application areas has inspired many recent developments in the urban and off-road autonomous navigation domain. In particular, techniques for semantic scene understanding, a key aspect of the navigation pipeline, have been researched extensively, resulting in many real-world and synthetic datasets. However, in comparison to urban semantic segmentation datasets, the availability of datasets for off-road environments remains sparse. In this paper, we aim to overcome this challenge by introducing a methodology capable of efficiently generating photorealistic synthetic datasets for off-road environments with support for multiple sensor modalities. The developed approach …

Video Generation Techniques For Novel View Synthesis With Flow-Matching Transformers, Xiuyuan Qiu

Master's Theses

Novel view synthesis (NVS) aims to generate images of a scene from unseen camera viewpoints. Recent work, such as Stable Virtual Camera, shows that large-scale image diffusion models like Stable Diffusion can be adapted for pose-conditioned view synthesis by incorporating video-generation techniques with camera conditioning. In this thesis, we introduce MVFlow, a new NVS model that extends this approach to a different image generation architecture: a flow-matching diffusion transformer, specifically FLUX.1, which has demonstrated strong performance in image synthesis. We evaluate MVFlow under varying input view counts and pose distance settings. Our results show that this architectural transfer is feasible; …

Optimizing And Fortifying Ai Software Through The Lens Of Artifact Synthesis, Jieke Shi

Dissertations and Theses Collection (Open Access)

Artificial Intelligence (AI) has transformed the software landscape, ushering in a new era of intelligent systems that increasingly shape our daily lives. This transformation is evident in various domains, including Software Engineering (SE), where Large Language Models (LLMs) support many development tools, and control systems, where self-driving cars and autonomous drones rely on deep learning models for real-time decision-making. These AI systems are collectively referred to as AI software, with the former categorized as AI4SE software (AI for Software Engineering) and the latter as AI4Control software (AI for Control). As AI software becomes central to modern computing infrastructure, its reliability …

Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin

Research Collection School Of Computing and Information Systems

This paper introduces a novel hybrid quantum-classical approach to credit card fraud detection using CVQBoost, a hybrid quantum-classical boosting algorithm executed on the photonic Dirac-3 processor from Quantum Computing Inc. (QCi). By integrating a diverse set of weak classifiers, which includes K-nearest neighbours (KNN), linear discriminant analysis, logistic regression, and XGBoost, within a hybrid quantum-classical ensemble, the proposed method demonstrates significant improvements over the latest published classical benchmarks. Experiments on a Kaggle credit card fraud dataset show that the quantum-enhanced model achieves a mean AUC-PR score of over 0.8, corresponding to an approximately 9% relative improvement over the best published …

Deep Learning Approaches For Anti-Money Laundering On Mobile Transactions: Review, Framework, And Directions, Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech …