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This survey defines agentic artifact creation as stateful, feedback-driven construction of deliverables by AI systems and analyzes 259 works to propose principles for accountable control.
This paper surveys 200 works on LLM quantization, formalizing the 'Great Inversion' principle that contrasts energy concentration in coding with within-group flattening in quantization, and offers a guide for transform selection based on deployment regimes and formats like MXFP4 and NVFP4.
This survey provides a comprehensive review of wireless foundation models, covering architectures, pre-training paradigms, applications, and challenges for AI-native 6G networks.
This guide distinguishes between workflows and agents in AI, breaking down the agentic AI stack from model to system using a coding agent example to illustrate the loop and layers.