Poker Arena: Multi-Axis Profiling of Strategic Reasoning and Memory in LLMs

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.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2606.13815 [cs.AI]

(or arXiv:2606.13815v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.13815 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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