Unverified50% confidenceBenchmarkExact time
In zero-shot testing on an unseen grid of 6,470 buses (approximately 40% larger than any grid in training), the cost error increases by about 10%, but the model can still rank scenarios by cost with near-perfect accuracy.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/1/2026
First Seen
Valid until: 12/30/2026
Sources
Related Entities
Related Claims
UnverifiedThe power-law exponent between test loss and compute is approximately 0.05, meaning that every 10x increase in compute yields approximately a 12% improvement in model performance.71% similarUnverifiedFine-tuning GridSFM with just 10 scenarios can reduce cost error to below 2% and bring feasibility detection above 90%; using 1,000 scenarios can reach in-sample accuracy levels.68% similarUnverifiedThe 82 tasks in OS World were not run under identical conditions—half of the tasks retained the original baseline scores, and the invested resources and number of attempts also differed—so the 7-percentage-point improvement cannot be entirely attributed to memory.67% similarUnverifiedAI生成假设的边际成本趋近于零，一旦模型训练完成每增加一条假设几乎不产生额外成本67% similarUnverified在一个396个文件的大型混合职责项目上测试时，召回率几乎跌到零66% similar
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