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This paper introduces a multi-turn interactive framework for reasoning evaluation where LLMs must query a hidden environment and integrate partial observations, instantiated as a benchmark of 474 executable games across five difficulty levels, showing discriminative power and exposing differences in reasoning.
This paper investigates the production-evaluation gap in large reasoning models (LRMs), finding that they fail to robustly evaluate reasoning despite near-perfect solution production, due to an answer confirmation bias.