I'd call it an approach issue: LLM vs brute-force lookahead. An LLM is predicting what comes next per it's training set. If it's trained on human games then it

I'd call it an approach issue: LLM vs brute-force lookahead. An LLM is predicting what comes next per it's training set. If it's trained on human games then it should play like a human; if it's trained on Stockfish games, then it should play more like Stockfish. Stockfish, or any chess engine using brute force lookahead, is just trying to find the optimal move - not copying any style of play - and it's moves are therefore sometimes going to look very un-human. Imagine if the human player is looking 10-15 moves ahead, but Stockfish 40-50 moves ahead... what looks good 40-50 moves out might be quite different than what looks good to the human.