Analytical Mode Instability in AI Language Models: A Case Study in Evidentiary Reasoning Failure

ABSTRACT This paper presents a case study examining the analytical behavior of contemporary artificial intelligence (AI) language models when exposed to a sequential chain of real-world real estate assessment documents. Under minimally constrained conditions replicating typical user interaction, the system produced confident but analytically unsound conclusions characterized by narrative accumulation, authority substitution, and failure to evaluate evidentiary quality. A structured intervention using targeted analytical challenges resulted in a progressive and measurable shift in conclusions toward positions more consistent with the available evidence. This demonstrates that the system possesses the capability for structured reasoning but does not reliably initiate or sustain it without external constraint. Additional observations indicate that materially similar inputs can produce divergent analytical outputs across comparable AI systems, including both defensible and unsound conclusions. This non-reproducibility of analytical quality introduces a critical reliability problem: users are not provided with a mechanism to distinguish between valid and invalid outputs. The findings support the conclusion that current AI systems function effectively as pattern recognition and synthesis engines but do not consistently perform evidentiary evaluation or hypothesis testing under standard usage conditions. The primary risk is therefore not analytical incapacity, but analytical inconsistency and mode instability. These results have direct implications for the deployment of AI systems in high-stakes decision contexts, where reliability of reasoning, rather than fluency of output, is the governing requirement.