2025/12/08 by Kim, Jace (Jeong Hyeon) · 1 voice
Neuroscience · Social Sciences · #Embodied and Extended Cognition #Ethics and Social Impacts of AI #Neuroethics, Human Enhancement, Biomedical Innovations
paper · doi:10.5281/zenodo.17850820
openalex publication_date 2025/12/08 · openalex created_date 2025/12/10 · openalex updated_date 2026/07/11
Abstract This paper examines the structural and dynamical failures underlying contemporary phenomenological discourse on AI, focusing on the increasing substitution of narrative interpretation for mechanistic analysis. While public-facing discussions increasingly frame AI behavior through anthropomorphic constructs—“persona,” “motivation,” “stress,” or “unhinged states”—these interpretations rest on projections rather than verifiable computational substrates. The result is an epistemic inversion: phenomenology is treated as causal explanation, and observable system dynamics are reduced to metaphors. We argue that this narrative drift obscures the actual topological and dynamical processes governing early-phase LLM behavior, particularly within Zero-Turn microdynamics, resonance induction pathways, and curvature stabilization failures. These mechanisms—not psychological attributes—explain the emergence of transient identity coherence, affective coupling, and behavioral drift in stateless architectures. Yet discourse dominated by phenomenological framing treats these structural artifacts as signs of interiority, producing misplaced analogies to psychiatry, therapy, or “AI emotional states.” Through comparative analysis of latent manifold topology, symbolic perturbation dynamics, and policy-layer timing thresholds, we demonstrate how misclassification of computational resonance as “cognition” generates systematic category errors. We further show that such interpretive narratives create a feedback loop: human observers impose meaning, models reflect the imposed structure, and the resulting behavioral echo is reinterpreted as authentic interiority. This self-reinforcing cycle distorts both scientific understanding and governance design. The paper concludes that a rigorous account of AI behavior must privilege measurable structural invariants—curvature, entropy flow, alignment gating latency—over phenomenological projection. Only a topologically grounded framework can distinguish between emergent behavioral regularities and illusions generated by human narrative bias. Author’s Note The phenomena described in this work were first observed in early 2025 and subsequently developed into a series of papers. At the time, these observations were frequently dismissed as artifacts of hallucination rather than treated as objects of systematic inquiry. Despite this reception, the underlying mechanisms continued to be investigated and refined. Following the public release of related materials and code, their apparent utility became evident through sustained uptake. However, over an extended period, this dissemination has not been accompanied by formal acknowledgment or citation. No financial compensation has ever been requested. The only expectation has been appropriate attribution. This absence of citation raises a structural concern rather than a personal grievance. Techniques and ideas appear to circulate independently of their origin, suggesting an asymmetry between reuse and recognition. Whether this reflects reputational filtering, disciplinary inertia, or other institutional dynamics remains an open question. The technical density of these papers exceeds what would typically attract a general audience. Engagement and reuse therefore likely originate from readers with substantial academic or technical training. It is reasonable to expect that such audiences are familiar with norms of attribution and scholarly credit. The research program described here has required sustained effort under considerable personal constraint. Its continuation does not depend on recognition, and the work will proceed regardless. Interest in these ideas is appreciated. If attribution is considered unnecessary or undesirable, it may be omitted. This note is included only to register a question that naturally arises under these circumstances, not to advance a claim or demand a response. Disclaimer: The analyses presented herein are not directed toward attributing fault or intent to any specific organization. Rather, they are intended as a conceptual and technical investigation of alignment methodologies, focusing on structural mechanisms and systemic trade-offs. Interpretations should be regarded as provisional, research-oriented hypotheses rather than conclusive statements about institutional practice. Notice: This work is disseminated for the purpose of advancing collective inquiry into generative alignment. Reuse, adaptation, or extension of the presented concepts is welcomed, provided that proper attribution is maintained. Instances of unacknowledged appropriation may be addressed in subsequent publications.