arXiv:2603.19167v2 Announce Type: replace Abstract: We evaluate Large Language Models (LLMs) in repeated game-theoretic settings to assess whether strategic performance reflects genuine reasoning or reliance on memorized patterns. We consider two canonical games, Prisoner's Dilemma (PD) and Rock-Paper-Scissors (RPS), upon which we introduce counterfactual variants that alter payoff structures and action labels, breaking familiar symmetries and dominance relations. Our multi-metric evaluation framework compares default and counterfactual instantiations, showcasing LLM limitations in incentive s
The rapid advancement and integration of large language models across various sectors necessitate immediate, rigorous evaluation of their true cognitive capabilities versus mere pattern recognition.
A strategic reader should care because understanding the limitations of LLMs in complex strategic reasoning is critical for their safe deployment and for guiding future AI research and development.
This research changes the understanding that LLMs do not inherently possess genuine strategic reasoning, highlighting their reliance on memorized patterns that fail in novel counterfactual scenarios.
- · AI researchers
- · AI ethics and safety organizations
- · Developers of specialized AI for strategic decision-making
- · Overly optimistic AI investors
- · General-purpose AI platforms
- · Sectors relying on LLMs for complex strategic planning without validation
The immediate first-order effect is a clearer understanding of the current boundaries of LLM capabilities in strategic game theory.
A plausible second-order consequence is a re-evaluation of LLM applications in critical strategic domains and increased focus on developing more robust strategic AI.
A speculative but reasoned third-order consequence could be a shift in AI development paradigms, prioritizing genuine reasoning architectures over solely scaling parameters, potentially impacting 'ai-agents' development timelines.
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