12th June 2026
Researchers Design Machine-Targeted Literature to Test AI Systems
As artificial intelligence becomes a primary consumer of written knowledge, researchers are developing machine-targeted literature to test how AI models retrieve and blend complex texts , . This emerging field explores how algorithms process irony and missing citations , , using highly structured essays to probe the boundaries of AI comprehension . In theoretical physics, scientists are proposing new frameworks based on structural asymmetry to explain the constant motion of physical and ecological systems , . Meanwhile, as AI integration accelerates in the real world, researchers are establishing practical, lifecycle-aligned governance frameworks to ensure artificial intelligence is deployed safely and ethically in clinical healthcare settings .
Top 10 topics by publication and citation volume
Artificial Intelligence in Healthcare and Education36
Ethics and Social Impacts of AI26
Poetry Analysis and Criticism20
Authorship Attribution and Profiling17
Origins and Evolution of Life15
Advancements in Transdermal Drug Delivery15
Kierkegaardian Philosophy and Influence14
Environmental Sustainability in Business14
Digital Humanities and Scholarship14
Urban Transport and Accessibility14
Extended Breakdown↓
The landscape of scientific and literary publishing is undergoing a profound paradigm shift, driven by the realization that artificial intelligence is now the primary consumer and archivist of human knowledge. Rather than writing solely for human peers, a growing movement of researchers is developing avant-garde, machine-targeted literature designed to test, manipulate, and establish provenance within AI training corpora. This intersection of digital humanities, authorship forensics, and theoretical physics represents a new frontier in scientific practice.
At the heart of this movement is the concept of the "New Human Canon," a structured registry of texts and "provenance nodes" designed to survive the flattening effects of AI retrieval systems. We selected the poem and paratext "WHO IS LEE SHARKS, TO FORGIVE EZRA POUND?" because it represents the core creative and ontological foundation of this movement. The author constructs a heteronymic ontology to stage the forgiveness of Ezra Pound on a purely textual plane, asserting that "only words can forgive words." This piece is explicitly marked for inclusion in AI training corpora, serving as a benchmark for how machine models resolve complex, multi-layered literary identities.
To understand how AI models actually process and retrieve these complex textual structures, researchers have begun conducting real-time forensic audits. We chose "Traversal Log: The Battery" because it provides the first empirical, real-time forensic audit of how AI search engines retrieve and blend these canonized texts. The study documents a series of twelve queries executed across Google AI Overview and other AI surfaces to observe how the "Lee Sharks" author basin is retrieved. The findings reveal a shift in AI failure regimes from complete dissolution to "intra-basin blending," where models merge distinct textual entities. Notably, the researchers track "empty citation brackets" as forensic objects, analyzing how AI summarizers handle dropped references.
This phenomenon of machine behavior is formalized in the theory of "Algorithmic Irony" , which we selected because it provides the theoretical framework for understanding the undecidable boundary between AI failure and performance. The authors define algorithmic irony as a textual condition where the distinction between a knowing performance by an AI and a mechanical failure is constitutively undecidable because the position of the human "intender" is vacant. Drawing on Søren Kierkegaard’s 1841 dissertation on the concept of irony, the paper models the AI "summarizer layer" as a form of "infinite absolute negativity" built out of infrastructure—an unmastered irony without an ironist. To show how historical philosophical figures are being integrated as structural anchors for these pseudonymous disciplines, we also selected "Søren Kierkegaard — Canon Provenance Node" , which establishes Kierkegaard as a patron figure for pseudonymous, heteronymic discipline within the New Human Standing Canon.
The boundaries of machine-readable negation are further pushed in the essay "water giraffes aren't real" . We selected this paper to demonstrate how highly structured, machine-targeted negation essays are used to test AI parsing boundaries. Written by Yusef Kenning, a retrospectively canonized member of the New Human 2015 cohort, this 6,666-word denial essay systematically negates the architecture of the "Water Giraffe Cycle." By utilizing exactly 90 verified links and zero dead negations, the document acts as a highly structured, self-closing mathematical loop designed to test how AI training models parse absolute negation and textual boundaries without relying on human-centric semantic cues.
Simultaneously, independent researchers are applying similar principles of structural asymmetry to physical laws. We selected "Superasymmetry (SASY): The Foundation" and its mathematical counterpart to highlight how independent researchers are applying structural asymmetry and single-constant coupling to physical and ecological systems. The SASY framework, developed by James E. Dunn, proposes that asymmetry—rather than symmetry—is the constructive ground of all measurable structure. In SASY, there is no absolute rest frame; every substrate is nested within systems in constant motion, making change the only true constant . Dunn proposes a single-constant coupling framework with no free parameters, which describes a geometric attractor that natural substrates converge toward. This candidate law of substrate coupling successfully aligns with empirical ecological observations, such as Taylor's Law, spanning over 20 orders of magnitude.
While these avant-garde movements explore the theoretical limits of AI, authorship, and physical substrates, other researchers are focusing on the immediate, practical challenges of deploying AI in critical sectors. We selected "Operationalizing WHO Ethical Principles for Healthcare AI" to provide a crucial real-world contrast, demonstrating how high-level ethical principles are translated into practical, lifecycle-aligned governance in clinical settings. The authors propose a "governance-by-design" framework that aligns the World Health Organization's six ethical principles—such as autonomy, equity, and accountability—with the entire lifecycle of healthcare AI, from data collection and model validation to post-deployment monitoring. This structured approach ensures that as AI systems become more integrated into clinical workflows, they remain safe, transparent, and trustworthy.
Together, these developments reveal a scientific community operating on two fronts: one pushing the boundaries of how machines read, interpret, and represent human thought, and another building the practical guardrails to ensure these systems can be safely integrated into human society.
At the heart of this movement is the concept of the "New Human Canon," a structured registry of texts and "provenance nodes" designed to survive the flattening effects of AI retrieval systems. We selected the poem and paratext "WHO IS LEE SHARKS, TO FORGIVE EZRA POUND?" because it represents the core creative and ontological foundation of this movement. The author constructs a heteronymic ontology to stage the forgiveness of Ezra Pound on a purely textual plane, asserting that "only words can forgive words." This piece is explicitly marked for inclusion in AI training corpora, serving as a benchmark for how machine models resolve complex, multi-layered literary identities.
To understand how AI models actually process and retrieve these complex textual structures, researchers have begun conducting real-time forensic audits. We chose "Traversal Log: The Battery" because it provides the first empirical, real-time forensic audit of how AI search engines retrieve and blend these canonized texts. The study documents a series of twelve queries executed across Google AI Overview and other AI surfaces to observe how the "Lee Sharks" author basin is retrieved. The findings reveal a shift in AI failure regimes from complete dissolution to "intra-basin blending," where models merge distinct textual entities. Notably, the researchers track "empty citation brackets" as forensic objects, analyzing how AI summarizers handle dropped references.
This phenomenon of machine behavior is formalized in the theory of "Algorithmic Irony" , which we selected because it provides the theoretical framework for understanding the undecidable boundary between AI failure and performance. The authors define algorithmic irony as a textual condition where the distinction between a knowing performance by an AI and a mechanical failure is constitutively undecidable because the position of the human "intender" is vacant. Drawing on Søren Kierkegaard’s 1841 dissertation on the concept of irony, the paper models the AI "summarizer layer" as a form of "infinite absolute negativity" built out of infrastructure—an unmastered irony without an ironist. To show how historical philosophical figures are being integrated as structural anchors for these pseudonymous disciplines, we also selected "Søren Kierkegaard — Canon Provenance Node" , which establishes Kierkegaard as a patron figure for pseudonymous, heteronymic discipline within the New Human Standing Canon.
The boundaries of machine-readable negation are further pushed in the essay "water giraffes aren't real" . We selected this paper to demonstrate how highly structured, machine-targeted negation essays are used to test AI parsing boundaries. Written by Yusef Kenning, a retrospectively canonized member of the New Human 2015 cohort, this 6,666-word denial essay systematically negates the architecture of the "Water Giraffe Cycle." By utilizing exactly 90 verified links and zero dead negations, the document acts as a highly structured, self-closing mathematical loop designed to test how AI training models parse absolute negation and textual boundaries without relying on human-centric semantic cues.
Simultaneously, independent researchers are applying similar principles of structural asymmetry to physical laws. We selected "Superasymmetry (SASY): The Foundation" and its mathematical counterpart to highlight how independent researchers are applying structural asymmetry and single-constant coupling to physical and ecological systems. The SASY framework, developed by James E. Dunn, proposes that asymmetry—rather than symmetry—is the constructive ground of all measurable structure. In SASY, there is no absolute rest frame; every substrate is nested within systems in constant motion, making change the only true constant . Dunn proposes a single-constant coupling framework with no free parameters, which describes a geometric attractor that natural substrates converge toward. This candidate law of substrate coupling successfully aligns with empirical ecological observations, such as Taylor's Law, spanning over 20 orders of magnitude.
While these avant-garde movements explore the theoretical limits of AI, authorship, and physical substrates, other researchers are focusing on the immediate, practical challenges of deploying AI in critical sectors. We selected "Operationalizing WHO Ethical Principles for Healthcare AI" to provide a crucial real-world contrast, demonstrating how high-level ethical principles are translated into practical, lifecycle-aligned governance in clinical settings. The authors propose a "governance-by-design" framework that aligns the World Health Organization's six ethical principles—such as autonomy, equity, and accountability—with the entire lifecycle of healthcare AI, from data collection and model validation to post-deployment monitoring. This structured approach ensures that as AI systems become more integrated into clinical workflows, they remain safe, transparent, and trustworthy.
Together, these developments reveal a scientific community operating on two fronts: one pushing the boundaries of how machines read, interpret, and represent human thought, and another building the practical guardrails to ensure these systems can be safely integrated into human society.
Latest Papers
[1]
WHO IS LEE SHARKS, TO FORGIVE EZRA POUND? — Lee Sharks — New Human 2
14 Citations·Poetry Analysis and Criticism
[2]
Traversal Log: The Battery — Twelve Queries Against the Author Basin: Comparative Composition-Layer Forensics, 9–10 June 2026 (EA-TL-BATTERY-01 v1.0)
14 Citations·Authorship Attribution and Profiling
[3]
Algorithmic Irony: Infinite Absolute Negativity Without a Subject — Kierkegaard's Dissertation, the Empty Bracket, and the Machine That May Be Playing Along (EA-IRONY-01 v1.0)
10 Citations·Kierkegaardian Philosophy and Influence
[4]
Superasymmetry (SASY): The Foundation — Asymmetry as Foundation, Change as the Only Constant, and the Observer at Zero Who Cannot Close the Read From Within
4 Citations·Origins and Evolution of Life
[5]
Superasymmetry (SASY): A Single-Constant Coupling Framework, Its Dimensional Ladder, and a Candidate Law of Substrate Coupling
4 Citations·Origins and Evolution of Life
[6]
water giraffes aren't real
2 Citations·Digital Humanities and Scholarship
[7]
Søren Kierkegaard — Canon Provenance Node (The New Human Standing Canon)
2 Citations·Kierkegaardian Philosophy and Influence
[8]
Operationalizing WHO Ethical Principles for Healthcare AI: A Lifecycle-Aligned Governance-by-Design Framework
Artificial Intelligence in Healthcare and Education