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This paper investigates whether LLMs can recover latent sequential structure beyond surface action frequencies, using controlled Rock-Paper-Scissors interactions and stochastic n-gram continuation tasks. Findings show that longer context doesn't improve strategy identification, recognition doesn't guarantee faithful simulation, and higher-order dependencies degrade rule recovery — suggesting apparent behavioral fidelity can mask incorrect generative mechanisms.
An experiment giving the same 1.2B language model six different personalities and playing 100 poker tournaments reveals drastic behavioral differences: a 'Grinder' never wins but never loses, a 'Tilter' wins big or busts, and a 'Shark' dominates. The results highlight how personality prompts can profoundly shape LLM decision-making.