AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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On-policy reinforcement learning has become a central paradigm for improving the reasoning abilities of large language models. However, its effectiveness is often limited by reward sparsity: when a model fails to discover correct trajectories for difficult problems, the optimization process receives little useful signa...
A matrix-valued optimizer that preserves the singular subspaces of the gradient while applying a bounded, monotone sigmoid transformation to its singular values is introduced, and the effectiveness and robustness of Soren against established optimizers are demonstrated.
SORT adds a repair update for all-wrong prompts without changing rollout generation: it derives a plan from the reference solution, compares token probabilities with and without that plan, and gives higher weight to tokens that become more predictable under plan conditioning.
HyperProve is proposed, a retrieval-augmented QA framework that addresses the challenge of multi-hop question answering by coupling question decomposition with answer-conditioned expansion over a hypergraph of atomic facts.
LLM-Detector is proposed, a framework that utilizes the in-context learning capacity of LLMs for structured, prompt-conditioned scoring synthesis, enabling LLMs to derive anomaly detection logic from structured normal-state knowledge.
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