Posts
All the articles I've posted.
Seeing with Transformers: ViT, CLIP and Vision-Language Models
Updated:Cut an image into patches and it becomes a sequence a Transformer can read; train images against their captions and a picture and its description land near each other in one space. Patches and the patch embedding worked for 224×224 images; the class token, pooling and the inductive bias ViT gives up; CLIP's symmetric contrastive loss and SigLIP's sigmoid loss computed on a 3×3 batch; vision-language models built from an image encoder, a projector and an LLM, and what their image tokens cost in KV cache; and CLIP tested on this blog's own diagrams, where it matches by reading their labels, and a rendering mistake first made it look much better.
Watermarking LLM Text: A Signal Hidden in the Sampling
Updated:Sampling leaves slack: many tokens would do at most steps. A keyed bias toward a pseudo-random green list spends that slack on a signature anyone with the key can test for, with no access to the model. The green-list scheme and its z-test derived and worked, how many tokens detection needs, the scheme run on a real 1.5B model at four bias strengths with its cost in likelihood, why low-entropy text resists it, what substitution and paraphrase do to it, distortion-free schemes (Aaronson's Gumbel trick, SynthID-Text's tournament), and why post-hoc AI-text detectors are a different and weaker thing.