AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes BusMA, a communication framework that allows any agent to address other agents through a shared channel, i.e., the Bus, which consists of agent registration, message routing, and shared memory management components.
This work trains 1B-parameter full-bandwidth transformers on up to 400B tokens and finds that latent feedback improves validation loss, 5-shot language-model evaluation, math and coding generation, and instruction-tuned performance.
This work proposes StateBridge, a training-free latent communication approach that aligns the sender's final-layer hidden states to the receiver's input space via a closed-form orthogonal transformation and prepended to the input of the receiver agent as a continuous prefix.
A plug-and-play graph-based online difficulty estimator that shares rollout feedback across related samples and continuously updates their difficulty estimates, mitigating cold start and staleness without dedicated probing is proposed.
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