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Equation 6 · Part 8 · Adapters, Native Pretraining, and Unified Tokens: The Main Multimodal Architectures, Compared

addition

Ljoint(θ)=E(xtext, ximg, xaud)∼D[ Ltext(θ)+Limg(θ)+Laud(θ) ]\mathcal{L}_{\text{joint}}(\theta) = \mathbb{E}_{(x_{\text{text}},\, x_{\text{img}},\, x_{\text{aud}}) \sim \mathcal{D}}\Big[\, \mathcal{L}_{\text{text}}(\theta) + \mathcal{L}_{\text{img}}(\theta) + \mathcal{L}_{\text{aud}}(\theta) \,\Big]
addition

What this part means

Add the term after the plus sign to the term or group before it.

Its job in the formula

Add the term after the plus sign to the term or group before it.

The passage around this formula

The documented limitation of native pretraining in this literature is not architectural instability — that problem belongs to the next section — but capability gaps that persist despite the joint training. Kosmos-1’s authors introduce a nonverbal Raven-style IQ test specifically to probe reasoning that is not mediated by language, and report that the model reaches only 26 percent accuracy against a random baseline of 17 percent, describing “a large performance gap between the current model and the average level of adults” even as they credit the model with demonstrating the basic capability [ 5 ] . Joint training from scratch buys deeper cross-modal integration; it does not, on this…

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Addition combines quantities; subtraction measures the signed difference between them. Parentheses show what is combined before the rest of the expression is evaluated.

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