AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Embodied agents are increasingly built as systems around foundation models, where performance depends not only on model weights but also on the skills, context, action interfaces, and execution harness surrounding the model. While supervised fine-tuning and reinforcement learning can adapt agents to new environments, t...
Public LLM services serve diverse multi-agent applications with varying workflow dependencies and performance requirements. Requests generated by these applications often exhibit commonality and interdependence, yet current systems largely ignore such application-level structure. As a result, at the LLM engine cluster...
STRACE (Structural TRajectory Analysis and Causal Extraction) is a framework that constructs high signal-noise optimization contexts for more precise and effective optimization of long-horizon agents.
BiVidGen, a hybrid framework where an MLLM first generates semantic visual tokens and a DiT renders videos conditioned on both text and these tokens via multi-layer cross-attention is proposed.
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