arXiv — cs.AI preprintsInternational9 October 2026
What Transfers from Text to Vision? Capability Scaling Laws and Transfer Dynamics for VLMs
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arXiv:2608.00013v3 Announce Type: replace-cross Abstract: Choosing the right large language model (LLM) backbone is the most consequential decision when building a vision-language model (VLM), yet it remains fundamentally unprincipled: compute-based scaling laws fail to generalize across model families, and no framework exists for directly predicting VLM performance before training begins. We propose the Capability-Driven Multimodal Scaling Law, the first cross-family framework that predicts VLM benchmark accuracy from directly observable textual capability. Given a low-dimensional capability
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