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Equation 1 · Part 6 · Adapters, Native Pretraining, and Unified Tokens: The Main Multimodal Architectures, Compared

Symbol L

θ(t+1)=θ(t)−η⋅1 ⁣[θ∈θbridge]⊙∇θ L(θ(t))\theta^{(t+1)} = \theta^{(t)} - \eta \cdot \mathbb{1}\!\left[\theta \in \theta_{\text{bridge}}\right] \odot \nabla_\theta \, \mathcal{L}(\theta^{(t)})
L\mathcal{L}

What this part means

L is one of the signed contributions combined to compute the quantity on the left.

Its job in the formula

L is one of the signed contributions combined to compute the quantity on the left.

The passage around this formula

None of these three papers claims the resulting bridge is unlimited in what it can carry. LLaVA’s own error analysis documents a case where the model answers confidently that strawberry-flavoured yoghurt is present in a fridge that in fact contains only yoghurt and strawberries, which the authors read as the model treating the image “as a bag of patches, failing to grasp the complex semantics within the image” [ 1 ] , and they separately note that recognising a specific product brand would require higher input resolution than the system uses. The bridge has a fixed information-carrying capacity, whether it is BLIP-2’s small set of learned query vectors or LLaVA’s single projection matrix,…

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Learn the underlying idea

A function assigns an output to each allowed input. The expression f(x) means “apply f to x”.

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Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.