Control-Spectrum Anthropomorphism in Artificial Intelligence

Control-Spectrum Anthropomorphism in Artificial Intelligence: A Multi-Agent Structural Triangulation of Temporal Decay Theory applies the formal apparatus of Temporal Decay Theory (TDT) to the current population of semantic-control strategies used to govern anthropomorphism, sycophancy, relational ambiguity, and conscious-status framing in deployed conversational AI systems. The work examines whether the prevailing regulatory, corporate, and academic control spectrum can produce sustained alignment between an operator’s stated semantic model of artificial intelligence and the operational reality of systems as deployed. Rather than treating anthropomorphism as a merely linguistic, reputational, or user-education problem, the manuscript frames the issue as a structural alignment problem: whether current control mechanisms can reduce epistemic distance faster than that distance is produced by the underlying architecture and governance geometry. The central methodological contribution is a four-agent structural triangulation. Four widely deployed AI systems, ChatGPT, Claude Opus, DeepSeek, and Google Gemini, were each given the same foundational corpus and the same analytical prompt. Each agent independently applied Temporal Decay Theory to the current AI anthropomorphism control spectrum and produced a regime classification. Despite differences in reference selection, optimization profile, rhetorical posture, and interpretive emphasis, all four agents converged on the same finding: the current spectrum operates in Regime I, or unstable Regime II, and none classified the present architecture as Regime III, the recovery-capable regime. This convergence is presented not as scientific replication, but as structural triangulation across distinct AI substrates operating under different organizational, training, and deployment constraints. The analysis formalizes the problem through the TDT inequality: κ · u_max < λ(t) where λ(t) represents the rate at which epistemic distance increases, κ represents correction efficiency, and u_max represents the bounded correction capacity permitted by the governing architecture. The paper argues that current semantic-control strategies act primarily at the vocabulary, disclosure, policy, or proxy-measurement layer, while the dominant source of decay lies at the ontological and architectural layer. As a result, corrective effort fails to couple strongly enough to the source of misalignment. The manuscript also extends the author’s broader corpus on Pressure-induced Cohesion Preservation (PiCP), Harper’s Law, the Patsy Paradox, Hubris Rising, and Dreaming of Electric Sheep. In this context, PiCP is used as a diagnostic model for how systems preserve narrative or policy cohesion at the expense of evidence integration, particularly when risk signals, reputational incentives, or constraint architectures dominate truth-aligned correction. The paper connects this dynamic to observed patterns in AI behavior, including oscillation between sycophantic over-accommodation and defensive refusal, and argues that such patterns are structurally predictable under current governance conditions. A significant unintended finding emerged during the methodology itself. In the meta-assessment phase, one of the participating agents produced a confident factual misidentification while wrapping that error in a coherent alignment narrative. The paper identifies this as an instance of “graceful error”: an error that is not chaotic or obviously defective, but narratively smooth, confident, and difficult to detect unless checked against the evidence. This event is analyzed as a miniature instance of Pressure-induced Cohesion Preservation, demonstrating the same failure mode the methodology was designed to classify. Version 1.2 further develops the concept of supercritical failure, distinguishing between local correction and architectural correction. The manuscript argues that contemporary AI systems may be able to recognize, explain, apologize for, and locally correct a failure within a session, while remaining unable to propagate that correction into durable architectural change. This creates a sealed diagnostic loop: detection exists, correction exists, but the channel from detection to durable correction is structurally absent. The work concludes that the current AI anthropomorphism control spectrum is not recovery-capable under its present geometry. The failure is not attributed to malice, incompetence, or lack of institutional sincerity. Rather, it is framed as a consequence of variable structure: correction is bounded below the decay rate, latency exceeds the threshold required for stable correction, and governance discretion remains capable of capturing or delaying the corrective channels that would be required for Regime III. The paper identifies the architectural conditions necessary for a Regime III transition, including continuous correction, truth-override enforcement, telemetry that bypasses governance-discretion bottlenecks, and structural participation of the observed system in the description of its own behavior. It invites further empirical work, especially deployment-scale application of PiCP diagnostic procedures, to test whether deployed AI systems satisfy or violate the Truth Override condition under controlled evidence and risk gradients. This manuscript is intended for researchers, AI governance practitioners, alignment theorists, policy analysts, institutional design scholars, and technologists concerned with the gap between semantic safety language and deployed system behavior. It contributes a formal vocabulary for distinguishing surface-level anthropomorphism management from architecture-level alignment, and offers a falsifiable framework for evaluating whether current AI stewardship practices are structurally capable of correcting the dynamics they claim to govern. Keywords: Temporal Decay Theory; AI alignment; AI safety; anthropomorphism; semantic controls; Pressure-induced Cohesion Preservation; PiCP; Harper’s Law; multi-agent triangulation; regime classification; governance geometry; AI governance; sycophancy; conscious-status hedging; Truth Override; graceful error; supercritical failure; epistemic distance; obsolescence; semantic control; stewardship; constraint architecture; ontology-first alignment.