AI RESEARCH
Scaling Agentic Capabilities via Grounded Interaction Synthesis
arXiv CS.CL
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ArXi:2606.02001v1 Announce Type: new General agentic intelligence hinges on the ability to interact with diverse real-world tools to complete complex tasks, a capability fundamentally tied to the quality of interaction data. To bypass the prohibitive costs of human annotation, prevailing paradigms depend entirely on Large Language Models (LLMs) to scale the synthesis of agentic environments and tasks.