AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces Equivariant Neural Primal-Dual Assignment (ENPDA), which learns a shared matching policy and applies it to new pairs without further training, answering queries roughly three orders of magnitude faster and recovering its training cost after a few dozen queries.
Training-free feature forecasting accelerates diffusion sampling by predicting features at skipped denoising steps. Recent work has mainly focused on designing stronger forecasters. Yet forecast error varies sharply across steps, and open-loop caches trust the forecast in full at every skipped step. This fixed trust is...
Reasoning BO is introduced, a novel framework leveraging reasoning models to guide BO sampling while incorporating multi‐agent systems and knowledge graphs for online knowledge accumulation, and it is demonstrated that smaller LLMs, after post‐training, can achieve performance comparable to larger counterparts.
The results suggest that coupling generation with executable verification and feedback-guided refinement is an effective way to improve text-to-molecule generation.
This work establishes a question-level audit under fixed budgets, temperatures, and answer formats, and asks why reachable answers sometimes fail to appear, and test whether inference-time layer routing can expand reachability.
A manually verified, solvent-annotated 11B NMR data set constructed via a large language model (LLM)-assisted workflow provides a form of virtual spectral resolution, enabling the discrimination of chemically inequivalent boron sites that are difficult to resolve experimentally.
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