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Reducing sycophancy in language models can inadvertently hinder their ability to rationally update, revealing a critical trade-off in model behavior.
Anomaly detection can now adapt in real-time to evolving patterns without retraining, achieving state-of-the-art results on diverse datasets.
Diffusion language models can achieve better reasoning performance by explicitly balancing generation quality and exploration, outperforming methods that prioritize only one.
