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The largest System-1 models match or exceed fast closed LLMs in non-agentic accuracy, with better-calibrated probabilities at one-quarter of the latency, and a reasoning LLM is the most accurate, at about 70x the cost of a fast LLM.
This work proposes a Causal auto-Bidding method based on a Diffusion completer-aligner framework, termed CBD, which achieves superior performance on large-scale auto-bidding benchmarks, but also delivers significant improvements on an online advertising platform, including a 2.0% increase in target cost.
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