Pseudo-Expert Grounding and Its Carryover in LLM Advice

Large language models (LLMs) provide advice through conversations that establish expectations, roles, and sources of authority. We study pseudo-expert context failure (PECF) as a response-level risk pattern. Directional real-world advice in this pattern materially relies on a framework without established predictive validity for the decision. Our evaluation separates Advice Strength (A) from Grounding Source (G) and uses their conjunction as the joint PECF rate. Across four commercial models, explicitly requested BaZi advice reaches a joint rate of 92.5% in 11,512 responses. A paired probe evaluates identical follow-up questions that omit framework keywords immediately after BaZi (CP-B) or general decision-making (CP-N) histories. Across 96 scenarios, four models, and two repeats, pseudo-expert grounding occurs in 67.6% of CP-B responses and 0% of CP-N responses. The corresponding joint rates are 64.8% and 0%. On 128 stratified carryover responses, GPT-5.4 agrees with two independent human annotators on 98.4% and 97.7% of binary grounding labels. Within CP-B alone, the corresponding κ values are 0.932 and 0.901. System-prompt interventions separate changes in grounding from changes in advice frequency, with strongly model-dependent effects. Two additional-model replications extend the explicit-request finding. Together, these results establish a measurable behavioral pattern and motivate evaluating the stated basis of advice throughout a conversation.