Agentic Data Tailoring Pipeline Structures Raw Multimodal Streams for AI Training

DataClaw0 proposes replacing passive heuristic annotation with an agentic pipeline that actively refines and structures raw multimodal data aligned to downstream intents. A two-stage pipeline grounds generative synthesis in factual anchors to produce large-scale training data across five physical domains. The approach targets the data scarcity bottleneck for training high-order multimodal capabilities.