AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Can interaction formats and textual scaffolds help large language model (LLM) agents make better decisions, and do better decisions come with better explanations? We study these questions in auctions and matching, multi-agent environments with explicit rules and known optimal strategies. These settings let us vary how...
Surprisingly, without task-specific fine-tuning or calibration to human bids, the three non-reasoning large models robustly preserve key orderings of auction formats by deviation from theory.
GRAF is introduced, a greedy algorithmic framework that produces self-selection outcomes in polynomial time by ordering workers according to a score vector, with provable guarantees—zero worker regret and platform optimality—for special cases of SSTC, and LLMScore is proposed, an LLM-driven evolutionary framework that...
The experiments demonstrate that this new deep learning framework can almost precisely replicate all known solutions from theory, expand to more complex settings, and be used to establish the optimality of new designs for data markets and make conjectures in regard to the structure of optimal designs.
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