AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes Think-at-Hard (TaH), a looped transformer optimized for selective iteration that employs a lightweight neural decider to trigger latent iteration, only at tokens likely to be incorrect after the standard forward pass.
Retrieval-Grounded Voting (RGV), which scores each rollout by the lexical overlap between its final answer and the documents it retrieved, consistently outperforms confidence-based voting and identifies the underlying failure reason as copy inflation.
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