AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It is concluded that structural validation alone is insufficient for production-grade consistency and that semantic canonicalization must be a first-class component of the reliability architecture.
SemVerBench is introduced, the first benchmark of LLM version-constraint resolution semantics across three ecosystems (npm, PEP 440, Cargo), and six frontier models are evaluated: Opus significantly outperforms all other models, and Sonnet outperforms the OpenAI models (McNemar).
This work introduces AlphaOpsBench, which evaluates end-to-end operationalization from source-grounded economic hypotheses to auditable executable programs over 581 source-preserving strategy records and a lifecycle-scale Polymarket dataset, and separates strategy fidelity, behavioral validity, historical executability...
An LLM asked for a trading strategy returns three artifacts at once: a natural-language rationale, an executable implementation, and once run, a track record. Whether these are the same object is rarely checked. We present FIDES, a measurement protocol that treats them as three views to be reconciled rather than one de...
LLM value studies often merge questionnaire ratings, pairwise choices, and values inferred from generated text into one profile. That merge assumes that the three observations describe the same stable preference. STONIC tests this assumption on 5,144 situations from four banks and 35 fixed model configurations. It comp...
The contribution is a falsifiable evaluation method and independent public evidence, not a new DR estimator, official BFCL leaderboard score, or production-agent safety claim.
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