AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand about the task. We formalize visual abstention: when a requested visual transformation is impossible under the task's rules, the model should recognize that no valid solution...
This work improves the two components of training that shape these fixed points: the depth prior and input injection, and learns the prior from prediction feedback, with an entropy term that keeps it broad.
Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infographics. Unlike photographs, infographics encode information through logical relations; editing one element often requires surrounding elements to be adapted. We refer to...
UReason is introduced, a benchmark for evaluating reasoning-to-generation alignment in this paradigm, consisting of 2,000 human-curated and human-verified instances spanning five reasoning-intensive tasks: Code, Arithmetic, Spatial, Attribute, and Text, and it is found that decontextualized generation consistently outp...
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