Benchmarks for language models have become essential tools for research. Yet, such benchmarks face a persistent contamination problem, with recent studies finding 25-50% of evaluation datasets appearing in training corpora. This is true even looking at the two-player zero-sum game setting, where most benchmarks are based on popular games, like chess, whose optimal strategies are all over the web. Such contamination hinders the possibility to differentiate memorization and reasoning skills. To rectify these problems, we introduce TCG-Bench, a benchmark based on a new two-player trading card game (TCG), similar in spirit to games like Magic: The Gathering. TCG-Bench offers three key innovations: (1) a contamination-resistant design by separating the publicly released game engine from hidden card implementations, (2) a continuous difficulty spectrum via Monte Carlo simulation that prevents benchmark saturation, and (3) a parallel implementation in English and Arabic, the first multilingual text-based game benchmark to do so. We also formalize a practical threat model and refresh protocol that preserves evaluation integrity even if specific cards leak.Our analysis across 17 models (50,000+ games) reveals that performance declines exponentially with difficulty, while model size correlates only weakly with strategic ability. We also observe cross-linguistic performance gaps between English and Arabic, with a gap of 47.4% at 32B, highlighting the need for multilingual game benchmarks that target reasoning capabilities in the target language. We host a leaderboard showcasing these results and welcome evaluation requests on our private cards.

Embodied agents in open-ended environments such as Minecraft increasingly adopt planner–controller architectures, with large language models acting as high-level planners. While planning has advanced rapidly, control remains underexplored. Existing systems commonly rely on a monolithic policy to execute subgoals across varying contexts, forcing incompatible behaviors into a shared parameter space and causing interference that scaling only partially mitigates. To address this, we propose MoEC, a Memory-Routed Mixture-of-Experts Controller for Adaptive Minecraft Control. MoEC routes via a subgoal-indexed, non-parametric expert memory and regulates capacity through failure-triggered expert growth and redundancy-aware consolidation. This design enables continual adaptation without full retraining, while maintaining parameter efficiency and with bounded inference cost. We evaluate MoEC on diverse and compositional Minecraft tasks, demonstrating significant gains in adaptability, robustness, and execution consistency over strong baselines, yielding a scalable and efficient alternative for open-ended control.