RoPE for multimodal LLMs can no longer be considered a single category

A multimodal model may make mistakes not because it lacked context, but because it misinterpreted the relationships within that context. You cannot mindlessly apply the same coordinate system to text, images, and video: they have different geometries and different densities of information.

The problem begins at the boundaries of images

In M-RoPE, positional channels are typically divided into temporal, height, and latitude channels. But if tokens from different images or visual objects appear next to each other in a sequence, their spatial offset doesn’t always make sense.

Applying H/W rotation to such a pair effectively means inventing geometry where none exists. RIG-RoPE proposes taking the visual instance identifier into account: spatial rotations are applied only to tokens from the same image or object. For other pairs, an unknown offset is not assumed to be zero but is excluded from the calculation.

“Time” Is Different for Text and Video

The second idea is even more practical. A standard position counter advances by one step per token, image block, or video segment. But a single text token, an image, and several video frames carry completely different amounts of information.

In RIG-RoPE, the temporal coordinate is accumulated based on the block’s duration: text receives a single step, an image receives a scale factor depending on its spatial size, and video receives an additional logarithmic extension based on the effective number of frames. This does not make the model “time-aware,” but it at least dispels the false assumption that all modalities proceed at the same speed.

What This Means for Developers

RIG-RoPE does not add any trainable parameters. The authors describe the implementation using additional metadata per token—modality, visual identifier, and duration coordinate—so that the mechanism can be integrated into tiled attention kernels with constant overhead.

In other words, the proposal is interesting not as a new, heavy-duty extension, but as an attempt to correct the very logic of positional features. The model does not need to train separate parameters for each type of spatial relationship: some clearly incorrect relationships can be avoided at the encoding stage.

But this is not yet a proven advantage

The paper remains a preliminary report and does not explicitly claim empirical superiority. It presents the formulation of the method, an argument regarding invariance under coordinate system changes, a result showing the impossibility of correctly using static identifiers in general H/W subspaces, and a justification for the duration-based approach—but no confirmed practical performance advantage in tests.

The open question, therefore, is not whether the idea seems reasonable, but to what extent it helps real multimodal models: whether it will degrade useful connections between different instances, how to choose an effective duration, and whether this will yield noticeable results after training.

If you’re designing a multimodal model, which is more dangerous for it: a fabricated connection between objects or the same “time step” for text, images, and video? Source: cs.CL updates on arXiv.org