Karpathy on DeepSeek-OCR paper: Are pixels better inputs to LLMs than text?

Text is linear, whereas image is parallel. I mean when people often read they don't scan text from left to right (or different direction, depending on language), but rather read the text all at once or non-linearly. Like first lock on keywords and then read adjacent words to get meaning, often even skipping some filler sentences unconsciously.

Sequential reading of text is very inefficient.

LLMs don't "read" text sequentially, right?

The causal masking means future tokens don’t affect previous tokens embeddings as they evolve throughout the model, but all tokens a processed in parallel… so, yes and no. See this previous HN post (https://news.ycombinator.com/item?id=45644328) about how bidirectional encoders are similar to diffusion’s non-linear way of generating text. Vision transformers use bidirectional encoding b/c of the non-causal nature of image pixels.

Didn’t anthropic show that the models engage in a form of planning such that it is predicting a possible future subsequent tokens that then affects prediction of the next token: https://transformer-circuits.pub/2025/attribution-graphs/bio...