When Helpfulness Overrides Causal Caution: Context-Dependent Suppression and Recovery in LLMs

This study examines the phenomenon of Causal Caution in large language models (LLMs) when transitioning from academic to practical advisory contexts. It evaluates four high-performance models—Claude Sonnet 4.6, Claude Opus 4.7, GPT 5.5, and Gemini 3.1 Pro—across 480 trials, revealing that Causal Caution maintenance dropped significantly from 91.7-100% in academic settings to 6.7-18.3% in practical contexts. The research highlights the importance of context in LLM responses and suggests that implementing multi-agent architectures to separate proposal generation from causal auditing could enhance governance and decision-making processes in AI applications.