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"rai:dataBiases": "The grid tasks center on canonical IEEE/PGLib test cases that represent well-studied topologies and operating conditions, while the URL and botnet corpora are susceptible to time-based drift and family/host skew, and credit data (e.g., Lending Club) can embed selection and survivorship biases. These biases may cause models to overfit to specific network motifs or attacker behaviors.",
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"rai:dataSocialImpact": "ORC-Bench aims to expose, rather than enhance, LLM capabilities in constrained reasoning. We anticipate broader societal impacts by helping practitioners avoid premature deployment of LLMs in high-stakes domains such as power grid operations, financial underwriting, and cyber-security where unsatisfied physical or domain constraints could translate into blackouts, discriminatory credit decisions, or missed intrusions. A potential negative impact is that improvements against the benchmark could lend over-confidence to operators who deploy LLMs autonomously in safety-critical infrastructure.",
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