Diffusion models reverse noise corruption to recover the manifold where real images live. DINO clusters similar images together and pushes different images apart. CLIP aligns text and image representations through mutual information. Transformers compress token distributions into structured representations.

Four methods. Four research teams. Four sets of mathematical tools. All approximate solutions to the same problem: find a compressed representation that preserves structure and discards noise.

The methods were developed independently. They converge on the same underlying objective. Convergence across independent paths is the kind of evidence that suggests the underlying principle is real, not chosen.