Poker Arena: Multi-Axis Profiling of Strategic Reasoning and Memory in LLMs
A new study introduces Poker Arena, a benchmark platform for evaluating LLMs using No-limit Texas Hold'em, featuring a three-layer memory architecture and a nine-axis cognitive profile. The findings reveal that traditional scalar leaderboards systematically misrank the true strategic capabilities of frontier AI models.
Computer Science > Artificial Intelligence
Title:Poker Arena: Multi-Axis Profiling of Strategic Reasoning and Memory in LLMs
View PDF HTML (experimental)Abstract:Strategic reasoning under uncertainty underpins consequential decisions in negotiation, finance, and policy, but prevailing game-play benchmarks collapse heterogeneous reasoning dimensions into a single scalar, leaving the capability structure of frontier LLMs unexamined. We introduce Poker Arena, a no-limit Texas Hold'em tournament platform that couples a three-layer memory architecture (within-hand, session, and cross-session) with a nine-axis cognitive profile decomposing strategic reasoning into interpretable dimensions such as bet-sizing calibration and positional awareness. We evaluate seven frontier models across 50 sessions of 1,000 hands and a controlled memory ablation; tournament chips and aggregate axis score order the field differently: Claude Opus 4.6 wins +$15,730 chips with 14 first-place finishes, yet ranks only fifth of seven on mean axis score, while persistent memory helps some models and hurts others. These findings show that multi-axis evaluation surfaces capability structure that scalar leaderboards systematically misrank, with cross-dimensional consistency outweighing peak performance on any single axis.
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Source: arXiv cs.AI Recent
