AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work asks whether human and model reasoning lead to different distributions over valid solutions, and compares these discrepancies across puzzle categories, and traces how they respond to reasoning-effort settings, to prompting, and to perturbations of the puzzle that leave its solutions unchanged.
This paper studies the problem of generating a uniformly random solution of a given (random) $k$-SAT or $k$-XORSAT formula and observes that continuous diffusions and learned diffusions can match the theoretical `ideal' accuracy.
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