Few-shot in-context learning (ICL) with multi-modal large language models (MLLMs) enables task adaptation without parameter updates, but its performance is highly sensitive to the quality and coverage of the selected demonstrations. While unlabeled multi-modal data is abundant, it remains elusive how to exploit them fo...
Zirui Cheng, Xun Xu, Tiankai Chen et al.· 0 citations
It is demonstrated that strengthening the understanding capability of the Boogu-Image system, through a stronger multimodal encoder, agentic prompt rewriting, and related techniques, together with improvements in data quality, training pipelines, and agentic inference-time scaling, can substantially enhance generation...
Guoxuan Chen, Chufeng Xiao, Haoran Yang et al.· arXiv.org· 3 citations
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