A paper comparison on multimodality is criticized for mostly repeating field-wide background
YouJiacheng · x · 2026-08-04
The author argues that several claims in a discussion of multimodal papers are basically common background in generative modeling.
- Ideas like “targets get averaged,” “models blur modes,” and “coverage matters” are widely shared across the field.
- Because of that, placing excerpts from different multimodality papers side by side can be misleading if the comparison does not address what XM is actually arguing.
- The author says the real point of XM is that generative expressivity matters just as much as parameter expressivity and model learnability, so increasing expressivity should be understood in that broader context.
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