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Nested sequential Monte Carlo methods can dramatically improve inference-time control in text generation, outperforming traditional techniques in steering towards desired outcomes.
A simple real-time monitor can match the performance of complex safety systems, proving that less can be more in LLM safety.
Uncertainty scores derived from molecular diffusion models can effectively predict sample quality, enabling targeted filtering that boosts model performance.
Outliers got you down? This Bayesian method prunes both features *and* data points to build robust sparse models, all in one shot.
