AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A new multimodal ICL framework is proposed that combines contrastive demonstration modeling with the self-refinement capability of MLLMs and consistently improves MLLM performance, with particularly notable gains on visual question answering (VQA).
Performance-Driven Demonstration Selection (PDDS), which directly aligns demonstration selection with ICL performance, is proposed, which formulates selection as predicting the target LLM’s downstream task performance for a given query–in-context pair, replacing proxy heuristics with a performance-aware objec-tive.
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