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@@ -1133,7 +1133,7 @@ <h1 id="scicode-a-research-coding-benchmark-curated-by-scientists">SciCode: A Re
 <h2 id="introduction">Introduction</h2>
 <p>SciCode is a newly developed benchmark designed to evaluate the capabilities of language models (LMs) in generating code for solving realistic scientific research problems. It has a diverse coverage of <strong>6</strong> domains: Physics, Math, Material Science, Biology, and Chemistry. They span 16 diverse natural science sub-fields. Unlike previous benchmarks that consist of question-answer pairs, SciCode problems naturally factorize into multiple subproblems, each involving knowledge recall, reasoning, and code synthesis. In total, SciCode contains <strong>338</strong> subproblems decomposed from <strong>80</strong> challenging main problems, and it offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. Claude3.5-Sonnet, the best-performing model among those tested, can solve only <strong>4.6%</strong> of the problems in the most realistic setting. </p>
 <h2 id="overview">Overview</h2>
-<p><a class="glightbox" href="https://github.com/scicode-bench/website-draft/blob/main/docs/figures/SciCode_example_problem.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="Image Title" src="https://github.com/scicode-bench/website-draft/blob/main/docs/figures/SciCode_example_problem.png" /></a></p>
+<p><a class="glightbox" href="figures/SciCode_example_problem.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="Image Title" src="figures/SciCode_example_problem.png" /></a></p>
 <h2 id="benchmark-statistics">Benchmark Statistics</h2>
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