Stop Trusting Text-Only Agent Leaderboards: Lessons from Cua-Bench and Factorio
Stop Trusting Text-Only Agent Leaderboards: Lessons from Cua-Bench and Factorio Overfitting to leaderboard benchmarks has created a field of agents tuned for text puzzles but fragile the moment real environments appear. GPT-4 Turbo, Gemini, Claude—pick your favorite recent leaderboard winner. None reveal their limits on static codegen or chain-of-thought sets. Put them in a kitchen, factory, or…
Text-only agent leaderboards can be misleading, as demonstrated by Cua-Bench and Factorio Learning Environment (Factorio LE). These environments expose the fragility of text-only agents when dealing with real-world tasks. Despite common belief, text leaderboards do not capture essential aspects like stateful context management and error recovery.
In practice, agents often fail to manage persistent memory, handle errors, or tolerate multimodal inputs, leading to significant drops in performance compared to their text-only scores.
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