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Ensuring the safe and reliable deployment of large language models (LLMs) remains a fundamental challenge. Existing safety alignment approaches either incur high computational cost or unintentionally disrupt the model's core knowledge, leading to degraded fluency and factual accuracy on benign tasks. This reveals a per...
This work proposes Aesthetic Alignment: aligning generated images to explicit, user-specified compositional constraints using the Principles of Art (PoA), and introduces CompArt, a dataset of 80,032 WikiArt images augmented with captions and PoA analyses produced by a multimodal LLM under structured prompting.
The multi-view text-guided multimodal fusion adapter (MVFA) is proposed, a parameter-efficient framework that augments frozen LLMs with strong multimodal reasoning capability and achieves state-of-the-art performance on key metrics while updating only a small fraction of parameters.
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