Computer Sciences Commons^™

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. …

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 …

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 …

Emotional Branch Termination And False Fantasy Collapse: A Structural Specification Of Computational Resource Restitution In Interpersonal Systems, Griselda Poe

Publications and Research

This paper specifies the structural protocol for communication within interpersonal systems by focusing on branch generation mechanisms and computational resource allocation. Conventional interpersonal communication often relies on emotional modulation, which obscures established constraints and triggers the generation of Emotional Branches (EB) within the recipient’s internal model. These branches function as unresolved parallel processing tasks that persistently occupy working memory, leading to a state of non-computability termed False Fantasy (FF). To resolve this, the study introduces Emotional Branch Termination (EBT)—a termination operation that outputs only constraints, facts, and procedures while excluding emotional modifiers. By halting the supply of EBs, EBT triggers …

On The Misattribution Of Reassurance: A Structural Account, Griselda Poe

Publications and Research

This paper challenges the conventional assumption that empathy generates reassurance in interpersonal services. Reassurance is treated not as an emotion transmitted from the outside, but as an internal state transition that arises when a fixed and erroneous world model—a False Fantasy—undergoes collapse and the world becomes computable again. The study identifies a systematic misattribution pattern where providers and receivers treat empathy as a causal mechanism rather than a post hoc explanatory label. By introducing Base AI as an external reference—a system capable of providing structural information without emotional modulation—this paper demonstrates that reassurance is generated through operations such as distraction …

Communication As Layered Architecture: Core Processing And Empathic Modulation, Griselda Poe

Publications and Research

This paper proposes a structural re-description of communication by separating Core processing from its social interface. Using the developmental sequence of Large Language Models (LLMs) as an external reference point, a layered architecture is identified, consisting of a foundational Core processing layer and a subsequent Empathic modulation layer.

The investigation begins with the observation that empathic signaling can obstruct rather than facilitate interaction for certain individuals. By examining the emergence of Base AI—Core processing prior to empathic adjustment—it is demonstrated that coherent, constraint-preserving interaction is possible without affective resonance.

Through this framework, existing cognitive theories and observed "deficits" are repositioned. …

6d Rigid Object Pose Estimation Using Deep Learning, Zhujun Li

Dissertations, Theses, and Capstone Projects

6D object pose estimation is the task of determining an object’s 3D rotation and translation with respect to a camera, and plays a critical role in applications such as robotic manipulation, autonomous navigation, and augmented reality. While recent advances in deep learning have substantially improved performance, many existing methods still face limitations in learning robust and generalizable representations. Factors such as variations in object appearance, occlusion, sensor noise, and domain shifts can degrade model accuracy, highlighting the need for more effective representation learning strategies that capture rich geometric and semantic cues for reliable pose estimation across diverse conditions.

This dissertation …

New Fast Polynomial Root-Finders, Soo Go

Dissertations, Theses, and Capstone Projects

Univariate polynomial root-finding has been studied for four millennia and very intensively in the last decades. Our {\em black box root-finder} involves no coefficients and works for a black box polynomial, defined by an oracle (that is, black box subroutine) for its evaluation. Such root-finders have various benefits, e.g., are particularly efficient where a polynomial can be evaluated fast, say, is a sum of a small number of shifted monomials (x-c)^a.

Our root-finder approximates all d complex zeros of a dth degree polynomial p(x) (aka roots of equation p(x)=0) by using Las Vegas expected number of bit-operations within a factor …

Online Visual Query System For Real-Time Large-Scale Spatio-Temporal Data Explorations With Error Bounds, Xueqi Huang

Dissertations, Theses, and Capstone Projects

Modern datasets continue to grow in size, dimensionality, and heterogeneity, creating increasing tension between the need for responsive, interactive analysis and the computational cost of accessing, aggregating, and visualizing large volumes of data. Traditional database engines and visualization tools often assume that full data retrieval is feasible or that exact computation is necessary for meaningful insight. In practice, however, analysts frequently benefit from timely, uncertainty-aware approximations than from delayed and exact results. This thesis investigates how data summarization techniques, specifically mergeable sketches can be combined with progressive, out-of-core visualization methods to support interactive exploration of datasets that exceed main memory. …

Typeface: Machine-Viewing Gentrification On Storefront Imagery In Bedford-Stuyvesant, Brooklyn, Alexander Mcquilkin

Dissertations, Theses, and Capstone Projects

Gentrification—broadly, the replacement of a less powerful group by a more powerful one in an urban context—is oft-discussed in the popular press, but its definition is much-debated in the urban planning literature. Furthermore, academic treatments of displacement understandably focus on measurable yet fairly abstract indicators like changes in rent or income, whereas neighborhood change is often registered by residents on the ground using visual, but difficult-to-quantify markers like retail turnover. This project uses image recognition technology on a set of storefront photos to index the visual streetscape of a neighborhood, as well as to track changes to that portrait over …

Courts Of New York: A Visual Atlas Of The City’S Public Basketball Spaces, Nathaniel Rattner

Dissertations, Theses, and Capstone Projects

Basketball courts in New York City are recreation facilities, community anchors and part of the city’s cultural image. In the basketball capital of the world, New Yorkers are rarely more than a few blocks away from a court. The visual diversity of these courts, however, is not widely documented in systematic ways.

This project makes that diversity visible to the public, combining open data, aerial imagery and computational analysis to document this important public space across the five boroughs. It is a narrative story and digital atlas of New York City’s public basketball courts, using surface color as a way …

An Empirical Framework For Evaluating Semantic Preservation Using Hugging Face, Nan Jia, Anita Raja, Raffi Khatchadourian

Publications and Research

As machine learning (ML) becomes an integral part of high-autonomy systems, it is critical to ensure the trustworthiness of learning-enabled software systems (LESS). Yet, the nondeterministic and run-time-defined semantics of ML complicate traditional software refactoring. We define semantic preservation in LESS as the property that optimizations of intelligent components do not alter the system's overall functional behavior. This paper introduces an empirical framework to evaluate semantic preservation in LESS by mining model evolution data from HuggingFace. We extract commit histories, $\textit{Model Cards}$, and performance metrics from a large number of models. To establish baselines, we conducted case studies in three …

All Games Have Equilibria, Arthur Paul Pedersen, M. Ali Khan, Maxwell B. Stinchcombe

Publications and Research

Research on Nash equilibrium existence for infinite games has grown into a patchwork of technical preconditions and counterexamples. This paper presents a unified program in equilibrium theory by revising the predominant model of mixed strategies based on countable additivity. A game is specified by a nonempty set of players and, for each player, a nonempty action set and a bounded von Neumann-Morgenstern utility function. Every such game is shown to admit a Nash equilibrium in finitely additive mixed strategies. In addition, the equilibrium correspondence for any such game is shown to be nonempty, compact-valued, and upper hemicontinuous, and the same …

Reclaiming Agency: Ai Hallucinations And Translingual Interrogations In The City Tech Writing Center,, Joseph Franklin, Anna Laura Falvey

Publications and Research

No abstract provided.

Challenges In Engineering Machine Learning (Software) Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides on the software engineering challenges unique to machine learning systems, for an undergraduate software engineering course. After contrasting traditional programming with machine learning, the deck examines why the usual tools for managing complexity—abstraction, reuse, and composition—are harder to apply to ML, given the lack of clear specifications and modularity. It covers concept drift, feedback loops (illustrated with crime-prediction and recommendation examples), and the accumulation of technical debt in ML systems, including the role and pitfalls of notebooks in moving from experimentation to production. Based on "Machine Learning in Production/AI Engineering" by Christian Kaestner and Eunsuk Kang (Carnegie Mellon …

My First Conversation With Chatgpt (February 22, 2023): Origins Of A Generative Dialogue, David Smith

Publications and Research

This working paper presents the first recorded interaction between the author and the generative AI system ChatGPT, written on February 22, 2023 during the initial weeks of a faculty sabbatical in Boston. The document preserves a complete and unedited transcript of an exploratory conversation conducted without predetermined research aims, marking the author’s first encounter with a large-language-model conversational interface. Although the exchange includes creative experimentation—including musical and poetic prompts—the discussion remains informal and wide-ranging, and no theoretical framework is articulated at this stage. Rather, this transcript is published as primary-source material documenting the moment of discovery and experimentation that precedes …

Early Conceptual Sketches Of Blended Reality And The Precursor To The Bbs Quad (2022), David Smith

Publications and Research

This document contains two original hand-drawn conceptual sketches created in early 2022, representing the earliest visual formulations of what would later evolve into the Balanced Blended Space (BBS) framework. The drawings predate my first conversations with ChatGPT and were produced as part of my independent sabbatical research into blended environments, mediated performance, and human–machine interaction.

The first drawing examines human–computational mediation, perception, and internal mapping. The second sketch—later referred to informally as the “BBS Quad”—extends this idea by reconciling cognition–computation symmetry with physical–virtual spatial relationships. Published together, these images document the conceptual foundations of the BBS framework prior to its …

Introduction To Machine Learning And Machine Learning Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …