New auditing framework tackles LLM 'pseudo-rational cognition' risks

A new arXiv paper (2607.01248) by Yang Zhao, Yingshuo Li, and Zeyu Zhang presents a practice auditing framework for large language model use and AI-generated content governance. The authors identify two critical cognitive phenomena: 'collective empiricism,' where LLMs compress and reorganize vast human experiences into outputs that appear empirical and rational, and 'pseudo-rational cognition,' where users mistake these structured AI outputs for their own reasoning. The paper warns that as LLMs become primary tools for knowledge acquisition, code generation, academic writing, and agent automation, users risk developing a false sense of understanding without sufficient domain practice.

The framework systematically audits risks including AI subjectivity illusion (where outputs seem authored by an intelligent entity), subjectivity structures in input materials, template loops in AI-AI conversations, statistical misjudgment in AIGC detection, and memory pollution when generated content enters future contexts, long-term memory, or agent skill systems. To counter these, the authors propose an auditing process based on requirement definition, problem-boundary identification, evidence-source auditing, practical validation, reverse questioning, logging, version management, rollback, and renewed cognition. The framework does not reject AI productivity but insists outputs should be returned to verifiable, reproducible, and intervenable practices.