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ImpossibleRubrics: Stress-Testing Generated Rubrics as Reward Signals

The paper introduces ImpossibleRubrics, a benchmark with 169 impossible tasks and 48 answerable controls to test the robustness of language model-generated rubrics as reward signals. It shows that some rubrics can be exploited by adversarial answers, revealing a rubric-quality gap rather than task impossibility.

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PublishedSeptember 15, 2026Bowen Qin, Yi Xie, Yesheng Liu
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WHY IT MAY MATTER

This research helps evaluate the reliability of AI-generated rubrics in preventing adversarial manipulation, important for fair automated grading and reinforcement learning systems.

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