AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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LiftXR is proposed, an interleaved, geometry-guided framework that explicitly incorporates spatial layout recovery into CT reconstruction, and consistently outperforms recent X-ray-to-CT reconstruction methods, establishing a new state of the art.
This Review examines volumetric foundation models, language alignment and compression strategies, and agentic systems that extend MLLMs through planning, tools, memory, and workflow interaction, and introduces a Claim-Design-Validation framework to assess whether technical, workflow, and clinical claims are matched by appropriate design and validation.
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