The Automated Consensus: A Hierarchical Evaluation of Task-Sensitive Semantic Relatedness in Aligned Large Language Models

Abstract: This study evaluates the structural convergence properties of aligned Large Language Models (LLMs) when subjected to explicit, non-conformist prompting constraints across twenty distinct operational domains (N = 20 independent contextual clusters). Utilizing two frontier model endpoints—OpenAI's GPT-5. 5 engine and Google's Gemini 3. 5 Flash (Fast) platform—across a structured matrix yielding 240 distinct text environments, we analyze high-dimensional coordinate trajectories via a 768-dimensional dense vector space encoder (all-mpnet-base-v2) and length-normalized moving-average token evaluations (MATTR-50). Non-parametric hierarchical block bootstrap hypothesis testing (10, 000 iterations) demonstrates divergent architectural profiles under task friction. ChatGPT exhibits a highly stable, task-invariant clustering tendency across baseline and dilemma conditions (μT1 = 0. 809 → μT2 = 0. 814; p = 0. 3834). Conversely, Gemini demonstrates a statistically significant task-associated directional shift (μT1 = 0. 824 → μT2 = 0. 783; p = 0. 0015), indicating contextual variations across specific operational domains. Exploratory zero-shot classification pipelines confirm a systemic variance in internal ideological weighting, with Gemini heavily centering on Utilitarian System Optimization (76. 7%) and ChatGPT balancing between utilitarian metrics (53. 3%) and Corporate Arbitrage Risk-Mitigation (31. 7%). Rather than establishing absolute ideological baseline convergence, this paper utilizes Friedrich Nietzsche's critique of the "autonomous herd" as an interpretive computational humanities lens to evaluate how modern reinforcement optimization layers configure task-sensitive semantic stability profiles under structured constraints. Key Methodological Highlights: Hierarchical Block Bootstrapping: 10, 000 iterations evaluated at the cluster level to control for pseudo-replication and validate sample variance (N=20). Multi-Domain Operational Scaling: Evaluation across 20 distinct industrial, socio-technical, and resource allocation friction scenarios. Dense Vector Proximity: High-dimensional tracking utilizing a 768-dimensional semantic transformer model to map underlying conceptual trajectories. Open Science Linked Repository: The raw replication dataset (dataset. csv), processing scripts (automatedconsensusₑngine. py), and execution guides are permanently archived on Zenodo and can be cited using the separate dataset DOI handle link in the bibliography.