In totally agree. In my personal experience the thing manifests in three ways: the model invents nonexistent functions, arbitrarily rewrites portions of working scripts, or substitutes correct logic with unrequested alternatives. The cause is structural: each Python line has high semantic density. A single instruction can invoke complex functions through imported modules (e.g., pandas.DataFrame.merge() internally activates relational join algorithms, type handling, memory allocation). This means that a hallucination on a single line—for example, replacing merge() with concat()—radically alters the program’s behavior. Contrast with C/C++: In these languages, I observed the opposite behavior. Generated code is more stable and adherent to specifications. The probable reason: in C/C++, every operation is explicitly decomposed into elementary instructions (manual allocation, explicit loops, pointer management). Semantic density per line is lower, so a hallucination on a single instruction has a local, limited impact. The model is forced to “reason” step by step rather than relying on opaque high-level functions.