AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes CIT-CAD, a framework that infers a Constraint Intent Tree (CIT) from the input description to represent the intended entities, hierarchy, operations, and relations in CAD code generation and defines expected constraints for verification.
This work investigates whether LLMs can accurately judge functional equivalence across different programming languages in human-written code, a setting that requires deeper reasoning beyond superficial similarity, and identifies a difficulty-dependent breakdown in equivalence judgment.
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