AI Reasoning is Textual, not Visual - Understanding How LLMs Process Multimodal Information

Explore how AI reasoning fundamentally operates through textual embedding spaces rather than visual processing in this 20-minute video. Discover how reasoning crystallizes during autoregressive pre-training by abstracting hierarchical structures like syntactic dependencies in code that align with visual hierarchies according to the Platonic Representation Hypothesis. Learn about multimodal adaptation through projectors that map visual tokens from encoders into the LLM's latent space, enabling textual logic to extend invariantly and treat hybrid sequences as unified computations. Examine research findings from Meta Superintelligence Labs and University of Oxford that demystify how large language models develop visual priors from language pre-training, revealing the textual foundation underlying AI's reasoning capabilities across different modalities.