ParVL scales MLLM computation by running multiple prefix-conditioned ViT and LLM branches over a shared backbone, improving average benchmark scores by 0.3 to 0.9 points and showing task-dependent vision-language allocation.
arXiv preprint arXiv:2509.17238 , year=
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ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs
ParVL scales MLLM computation by running multiple prefix-conditioned ViT and LLM branches over a shared backbone, improving average benchmark scores by 0.3 to 0.9 points and showing task-dependent vision-language allocation.