AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Multimodal instruction following (MMIF) is crucial for building generalist agents. However, current training paradigms rely heavily on Supervised Fine-Tuning (SFT), which often leads to surface-level pattern matching and degrades general capabilities. While Reinforcement Learning with Verifiable Rewards (RLVR) offers a...
The neural mechanisms underlying the brain's representation of event gist remain poorly understood. In this study, we investigate the neural architecture and computational principles governing the hierarchical representation of event gist in the human brain. Using an integrative approach that combines natural language...
The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across stateful workspaces. While Agentic Reinforcement Learning (Agentic RL) provides a promising path to optimize these agents, its scaling is heavily bottlenecked by the severe sca...
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