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         <strong>LR\({}^{2}\)Bench</strong> is a novel benchmark designed to evaluate the <strong>L</strong>ong-chain <strong>R</strong>eflective <strong>R</strong>easoning capabilities of LLMs. LR\({}^{2}\)Bench comprises 850 samples across six Constraint Satisfaction Problems (CSPs) where reflective reasoning is crucial for deriving solutions that meet all given constraints. Each type of task focuses on distinct constraint patterns, such as knowledge-based, logical, and spatial constraints, providing a comprehensive evaluation of diverse problem-solving scenarios.
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-        <strong>Note:</strong> We plan to release the LR\({}^{2}\)Bench dataset soon. If you're interested in the performance of your model or have any questions, please feel free to contact us at <a href="mailto:[email protected]">[email protected]</a>.
+        <strong>Note:</strong> We have released the LR\({}^{2}\)Bench dataset <a href="https://github.com/Ultramarine-spec/LR2Bench">here</a>. If you're interested in the performance of your model or have any questions, please feel free to contact us at <a href="mailto:[email protected]">[email protected]</a>. We will soon provide an automated evaluation system on leaderboard website here.
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