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Existing power outage prediction models fail to account for spatial weather patterns, but this new approach leverages graph neural networks and contrastive learning to substantially improve prediction accuracy.
LLMs can slash token usage by 80% and "thinking rate" by 95% without sacrificing accuracy, simply by learning when *not* to reason.
Ditch the deterministic databases: this LLM-driven simulation framework evaluates tool-calling agents with surprisingly reliable proxy states, offering a scalable alternative to costly benchmarks.
