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Counterfactual Tucker Diffusion (CFTDiff), which integrates the treatment mask and latent Tucker structure into conditional diffusion to recover this distribution given observed control outcomes through efficient nonlinear score learning in a low-dimensional core, and establishes high-probability error bounds.
Critical visual token selection is driven by a select few attention heads, and leveraging this insight can drastically improve VLM efficiency without sacrificing performance.
