AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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SpanPM is introduced, a prediction-market mechanism that sets the local-curvature multiplier from each trade's realized payoff spread and preserves 96--97% of the trader surplus achieved with exact Bregman fees while cutting excess fees by 94\% relative to the global quadratic mechanism.
Across seven frontier and open models, simulation is far ahead of planning for every model, near saturation for the strongest and well short of it for the weakest, and the length of the required solution, not grid size or obstacle count, governs planning failure.
It is shown how recent progress in generative AI becomes genuinely useful in markets when it helps model participant behavior, ground reasoning in live documents and order-flow data, and support research and execution workflows that can survive contact with production.
Financial markets are one of the few machine-learning settings where feedback is fast, mistakes are expensive, and deployment is constrained by execution and regulation. Yet most existing tutorials still treat finance either as an NLP benchmark or as a classical forecasting problem. This tutorial takes a different view...
On ClimateCheck claim-only and fine-tuned models outperform zero-shot LLM and top-performing AVeriTeC 2025 systems, highlighting that noisy evidence can degrade veracity prediction and replacing retrieved evidence with gold annotations improves veracity accuracy, confirming retrieval remains primary bottleneck.
SymStep: an LLM makes one atomic claim at a time (DEDUCE: Alice, pet, Cat), then a lightweight constraint propagator checks the claim for consistency with prior accepted deductions, rejects contradictions, and cascades implied facts automatically.
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