Landing page: Figure 2, and both figures from the current paper

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 <h2>About ShellElastic</h2>
 <figure><img src="figure1.png" alt="Figure 1 of the paper: the ShellElastic architecture"></figure>
 <p>Foundation models need to support diverse inference budgets. Elastic models train several nested sub-networks inside one weight-shared supernet. ShellElastic is an elastic transformer that organizes hidden dimensions into a dense core and successive shells, so that every hidden matrix is block lower triangular. One full-width forward pass contains the exact hidden states of every sub-network, so joint training keeps a fixed computation graph and costs about as much per step as training the widest sub-network alone.</p>
+<figure><img src="figure2.png" alt="Figure 2 of the paper: overview of µ-Elastic"></figure>
 <p>µ-Elastic extends maximal update parameterization (µP) to ShellElastic and other elastic architectures. Its scaling rules keep all sub-networks of a supernet maximally updated simultaneously, and hyperparameters tuned on a small non-elastic proxy stay near-optimal for elastic families and larger supernets. A single ShellElastic run produces sub-networks that outperform MatFormer and fully elastic baselines at matched parameter counts, and approach separately trained models that together take several times the steps.</p>
 
 <h2>Checkpoints</h2>