Translating English VLM corpora to Romanian and training adapted models produces VLMs that outperform same-sized and even next-larger non-adapted models on Romanian benchmarks.
Penguin-vl: Exploring the efficiency limits of vlm with llm-based vision encoders.arXiv preprint arXiv:2603.06569, 2026a
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Tuna-2 shows that direct pixel embeddings can replace vision encoders in unified multimodal models, achieving competitive generation and stronger understanding at scale.
LLaDA2.0-Uni unifies multimodal understanding and generation inside one discrete diffusion large language model with a semantic tokenizer, MoE backbone, and diffusion decoder.
citing papers explorer
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"\^{I}n\c{t}elegi Rom\^ane\c{s}te?'' A Recipe for Romanian Vision-Language Models
Translating English VLM corpora to Romanian and training adapted models produces VLMs that outperform same-sized and even next-larger non-adapted models on Romanian benchmarks.
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Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation
Tuna-2 shows that direct pixel embeddings can replace vision encoders in unified multimodal models, achieving competitive generation and stronger understanding at scale.
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LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model
LLaDA2.0-Uni unifies multimodal understanding and generation inside one discrete diffusion large language model with a semantic tokenizer, MoE backbone, and diffusion decoder.